Journal of Applied Business and Economics

North American Business Press Atlanta - Seattle – South Florida - Toronto

Editors Dr. Adam Davidson Dr. William Johnson

Editor-In-Chief Dr. David Smith

NABP EDITORIAL ADVISORY BOARD

Dr. Nusrate Aziz - MULTIMEDIA UNIVERSITY, MALAYSIA Dr. Andy Bertsch - MINOT STATE UNIVERSITY Dr. Jacob Bikker - UTRECHT UNIVERSITY, NETHERLANDS Dr. Bill Bommer - CALIFORNIA STATE UNIVERSITY, FRESNO Dr. Michael Bond - UNIVERSITY OF ARIZONA Dr. Charles Butler - COLORADO STATE UNIVERSITY Dr. Jon Carrick - STETSON UNIVERSITY Dr. Min Carter – TROY UNIVERSITY Dr. Mondher Cherif - REIMS, FRANCE Dr. Daniel Condon - DOMINICAN UNIVERSITY, CHICAGO Dr. Bahram Dadgostar - LAKEHEAD UNIVERSITY, CANADA Dr. Deborah Erdos-Knapp - KENT STATE UNIVERSITY Dr. Bruce Forster - UNIVERSITY OF NEBRASKA, KEARNEY Dr. Nancy Furlow - MARYMOUNT UNIVERSITY Dr. Mark Gershon - TEMPLE UNIVERSITY Dr. Philippe Gregoire - UNIVERSITY OF LAVAL, CANADA Dr. Donald Grunewald - IONA COLLEGE Dr. Samanthala Hettihewa - UNIVERSITY OF BALLARAT, AUSTRALIA Dr. Russell Kashian - UNIVERSITY OF WISCONSIN, WHITEWATER Dr. Jeffrey Kennedy - PALM BEACH ATLANTIC UNIVERSITY Dr. Dean Koutramanis - UNIVERSITY OF TAMPA Dr. Malek Lashgari - UNIVERSITY OF HARTFORD Dr. Priscilla Liang - CALIFORNIA STATE UNIVERSITY, CHANNEL ISLANDS Dr. Tony Matias - MATIAS AND ASSOCIATES Dr. Patti Meglich - UNIVERSITY OF NEBRASKA, OMAHA Dr. Robert Metts - UNIVERSITY OF NEVADA, RENO Dr. Adil Mouhammed - UNIVERSITY OF ILLINOIS, SPRINGFIELD Dr. Shiva Nadavulakere – SAGINAW VALLEY STATE UNIVERSITY Dr. Roy Pearson - COLLEGE OF WILLIAM AND MARY Dr. Veena Prabhu - CALIFORNIA STATE UNIVERSITY, LOS ANGELES Dr. Sergiy Rakhmayil - RYERSON UNIVERSITY, CANADA Dr. Fabrizio Rossi - UNIVERSITY OF CASSINO, ITALY Dr. Robert Scherer – UNIVERSITY OF DALLAS Dr. Ira Sohn - MONTCLAIR STATE UNIVERSITY Dr. Reginal Sheppard - UNIVERSITY OF NEW BRUNSWICK, CANADA Dr. Carlos Spaht - LOUISIANA STATE UNIVERSITY, SHREVEPORT Dr. Ken Thorpe - EMORY UNIVERSITY Dr. Robert Tian – SHANTOU UNIVERSITY, CHINA Dr. Calin Valsan - BISHOP'S UNIVERSITY, CANADA Dr. Anne Walsh - LA SALLE UNIVERSITY Dr. Thomas Verney - SHIPPENSBURG STATE UNIVERSITY Dr. Christopher Wright - UNIVERSITY OF ADELAIDE, AUSTRALIA Volume 16(5) ISSN 1499-691X

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This Issue

The Use of Voluntary Public Disclosure and Patent Strategies to Capture Value from Product Innovation ...... 11 Sharon D. James

Firms make tradeoffs in voluntarily and publicly disclosing R&D information. Disclosure can deter competition by signaling a technological advantage. However, such disclosures might signal technological opportunity and encourage competitors to develop competing innovations. This study investigates the effect of industry- and firm-specific advantages on the influence of voluntary public disclosure on competitors’ patenting in the same technology space. Theoretical predictions are tested on a sample of 322 publicly traded firms between 1991 and 2004. The results are consistent with industry and firm-specific advantages moderating the effectiveness of disclosure along with patents as a strategy for capturing value from product innovation.

The New Normal: Fundamental Shifts for 21st Century Organizations and for the CIOs Who Lead Them ...... 27 Mark W.S. Chun, Charla Griffy-Brown, Harvey Koeppel

The New Normal (the business climate following the 2008 economic crisis) has ushered in fundamental changes to the structure and governance of many organizations and, therefore, to the executives who lead them. The Chief Information Officer (CIO) remained at the epicenter of organizational evolution and enablement. Shifts within the Information Technology (IT) landscape have created significant opportunities and challenges for organizations and for their CIOs. Through our research, we learned that effective IT leaders typically possessed six key competencies: Leadership; Innovation and Growth; Business Strategy and Process; Relationship Management and Communication; Business Management; Risk Management.

Country Risk and Macroeconomic Factors: Evidence from Asian Markets ...... 51 Rahul Verma, Priti Verma

Using international version of capital asset pricing model (ICAPM), we analyze the response of country risk in Asia to a set of domestic and global macroeconomic factors. Specifically in a two-step process, we first estimate country beta models for Hong Kong, Indonesia, Malaysia, Philippines and Singapore and generate separate series of country risk variables for each market. In the second step we analyze the response of these country risks to five local factors and seven global factors. The local factors are: money supply, inflation, economic growth, interest rate and exchange rate while the international factors are: value of U.S. dollar against currencies of 15 industrialized countries, spread between 90-day Euro dollar deposit rate and 90 day U.S. Treasury Bill yield, weighted average inflation of G-7 countries, weighted average short term interest rates of G-7 countries, U.S. dollar price per barrel of crude oil, U.S. interest rate and U.S. inflation. The results indicate strong and significant effects of the global risk factors on country risk of all these Asian markets. The price of dollar has significant positive effects in all except in the case of Malaysia’s country risk. In addition, the dollar euro spread, real interest rates and inflation of G-7 countries have a significant negative impact on country beta in all the cases. On the other hand, exchange rate (in case of Malaysia and Singapore) and to some extent money supply (only in case of Hong Kong) are the only local factors, which have a significant effect on country risk of these markets. Our results are consistent with previous findings that sensitivity to global risk factors increases as the markets become more integrated.

The U.S.-China Trade Friction: Causes and Proposed Solutions ...... 63 Suk Hi Kim, Mario Martin-Hermosillo, Junhua Jia

The U.S.–China trade relationship has undergone tremendous growth since 1979, when the United States and China established their diplomatic relationship. The trade volumes have increased dramatically after China joined the World Trade Organization in 2001. However, the trade relationship between the two countries has recently experienced some setbacks—specifically in terms of the huge U.S. trade deficit with China, currency manipulation by the Chinese government, and China’s failure to enforce laws to protect the intellectual property rights of U.S. companies. This article discusses the three major issues of the US- China trade relations: burdens, causes, and solutions.

Social Media Marketing: A Myth or a Necessity ...... 74 Anne Whiting, Anant Deshpande

Social Media Marketing (SMM) is a heavily debated topic in marketing circles today. Opponents claim that it does not work well as a marketing agent, cannot bring new customers to a company/brand, and may alienate customers if blatant advertising/marketing tactics are used. Proponents maintain that the relationships built, maintained, and grown and the brand pride developed outweigh the negative factors that can occur through using social media in marketing campaigns. This study asserts that both sides have legitimate arguments, but that, when used with care, SMM can be a valuable, or even necessary, tool for an organization.

Innovation in the Automobile Industry: How the Changing Face of Global Competition Affects Motor Vehicle Patenting ...... 82 Gerald P. W. Simons, Paul N. Isely

There is much interest in how the increase in high valued added manufacturing in emerging economies is affecting established manufacturers in high income economies. One area of analysis is industry innovation. We analyze how innovation in the automobile industry has been impacted by competition in a North-South setting. North-South innovation models indicate that greater production by the South will encourage North companies to engage in more innovation to stay ahead of the new competition. In contrast, our analysis suggests that greater competition from auto manufacturers in the South results in less innovative output by manufacturers in the North.

Factors Associated with Student Performance in Intermediate Accounting: A Comparative Study at Commuter and Residential Schools ...... 86 Mostafa M. Maksy

Of the three motivation factors, the grade the student intends to earn had a strong association with student performance at the commuter school but a weak one at the residential school. Intention to take the CPA exam or attend graduate school had no associations with student performance at either school. The same with respect to self-perceived writing, reading and listening abilities and the distraction factors of job hours, job type, and course load. Math ability and GPA had strong associations with student performance at the commuter school only. Intermediate Accounting I grade is a strong predictor of student performance at both schools.

Hypermarket Corporate Brand Extension Personality ...... 109 Hasliza Hassan, Muhammad Sabbir Rahman, Abu Bakar Sade

Products and services are complementary to each other. This research explores the relationship of hypermarket corporate brand extension of products and services as parallel independent constructs towards brand personality. Through convenience sampling of hypermarket distribution outlets throughout Malaysia, 785 data were collected from hypermarket consumers based on proportionate quota. The collected data were analysed using exploratory factor analysis, confirmatory factor analysis and structural equation modeling. It is proven that both the products and services that are offered by the hypermarkets are equally important in influencing the hypermarket corporate brand personality.

Is Economic Liberalization Causing Environmental Degradation in India? An Analysis of Interventions ...... 121 Avik Sinha, Joysankar Bhattacharya

India’s fossil fuel based energy-led economic growth and carbon emissions are largely influenced by economic liberalization. In this paper, we have considered twenty years before and after liberalization (1971-2010) and by formulation of an error correction model, we have demonstrated how causal associations among economic growth, drivers of growth, and negative consequences of growth undergo changes based on three constructs, namely industrialization, energy efficiency, and rural-urban migration. Analysis of missing feedback link in Environmental Kuznets Curve hypothesis using contextual interventions is the primary contribution of this paper in ecological economics literature.

Global Leadership and Emotional Quotient ...... 137 Geoffrey VanderPal

This paper aims to identify and investigate the relationship between emotional intelligence (EI) and the effectiveness of global leaders. Practical experiences emphasize that effective global leadership is essential for rapidly changing multinationals and emotional intelligence has been identified by a large number of researchers as a major driver of effective leadership. Although academic evidences are increasing, the gap on the connection between emotional intelligence and leadership still maintains. The exploration of extensive researches has revealed a powerful link between emotional intelligence and leadership performance. Directions of future research of EI of global leaders may refer to the investigation of job satisfaction, organizational engagement or followers’ motivation.

GUIDELINES FOR SUBMISSION

Journal of Applied Business and Economics (JABE)

Domain Statement

The Journal of Applied Business and Economics is dedicated to the advancement and dissemination of business and economic knowledge by publishing, through a blind, refereed process, ongoing results of research in accordance with international scientific or scholarly standards. Articles are written by business leaders, policy analysts and active researchers for an audience of specialists, practitioners and students. Articles of regional interest are welcome, especially those dealing with lessons that may be applied in other regions around the world. This would include, but not limited to areas of marketing, management, finance, accounting, management information systems, human resource management, organizational theory and behavior, operations management, economics and econometrics, or any of these disciplines in an international context. Focus of the articles should be on applications and implications of business, management and economics. Theoretical articles are welcome as long as their focus is in keeping with JABE’s applied nature.

Objectives

. Generate an exchange of ideas between scholars, practitioners and industry specialists

. Enhance the development of the Business and Economic disciplines

. Acknowledge and disseminate achievement in regional business and economic development thinking

. Provide an additional outlet for scholars and experts to contribute their ongoing work in the area of applied cross-functional business and economic topics.

Submission Format

Articles should be submitted following the American Psychological Association format. Articles should not be more than 30 double-spaced, typed pages in length including all figures, graphs, references, and appendices. Submit two hard copies of manuscript along with a disk typed in MS-Word.

Make main sections and subsections easily identifiable by inserting appropriate headings and sub-headings. Type all first-level headings flush with the left margin, bold and capitalized. Second-level headings are also typed flush with the left margin but should only be bold. Third- level headings, if any, should also be flush with the left margin and italicized.

Include a title page with manuscript which includes the full names, affiliations, address, phone, fax, and e-mail addresses of all authors and identifies one person as the Primary Contact. Put the submission date on the bottom of the title page. On a separate sheet, include the title and an abstract of 200 words or less. Do not include authors’ names on this sheet. A final page, “About the authors,” should include a brief biographical sketch of 100 words or less on each author. Include current place of employment and degrees held.

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Authors will receive an acknowledgement by e-mail including a reference number shortly after receipt of the manuscript. All manuscripts within the general domain of the journal will be sent for at least two reviews, using a double blind format, from members of our Editorial Board or their designated reviewers. In the majority of cases, authors will be notified within 60 days of the result of the review. If reviewers recommend changes, authors will receive a copy of the reviews and a timetable for submitting revisions. Papers and disks will not be returned to authors.

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The Use of Voluntary Public Disclosure and Patent Strategies to Capture Value from Product Innovation

Sharon D. James Arkansas State University

INTRODUCTION

Publicly traded firms face a dilemma in publicly disclosing qualitative information about their R&D accomplishments. On the one hand, firms might disclose such information because they need financing and seek to reduce their cost of capital (Botosan, 1997; Botosan & Plumlee, 2002; Dedman, Lin, Prakash, & Chang, 2008; Jones, 2007). By reducing uncertainty of innovation outcomes, disclosure helps investors to make better estimates of future profits from innovation. As a result, greater disclosure reduces a firm’s cost of capital. On the other hand, disclosing such information may expose a firm to greater risk of competitive entry that reduces its competitive advantage (Davis, 2001; James, Leiblein, & Lu, 2013). As such, some firms may have strategic motivations to withhold information about their innovative efforts (Hemphill, 2004; Kale & Little, 2007). Given this paradox of disclosure (Arrow, 1962), the influence of disclosure on a firm’s competitive advantage and ability to capture value from product innovation (James et al., 2013) is an important consideration for top managers who establish and implement disclosure strategies on behalf of the firm. Yet little work has been done regarding the intentional use of public disclosure (versus secrecy) and patents as a strategy for capturing value from innovation. This study addresses this gap in the literature in an investigation of the conditions under which public disclosure along with patents can be an effective value capture strategy in the context of competitive patenting in the same technology space. For some firms, disclosing proprietary information about product innovations may make their strategies more transparent and reduce profits from innovation (Cohen, Nelson, & Walsh, 2000; Levin, Klevorick, Nelson, & Winter, 1987; Winter, 2000). In such cases, disclosure might help competitors benefit from lower innovation costs or the time to develop and patent a product innovation (Bhattacharya & Ritter, 1983; Choi, 1991; De Fraja, 1993). The net result is that disclosure can reduce a firm’s technological advantage in a race to patent an innovation ahead of rivals. Therefore, some firms may have

Journal of Applied Business and Economics vol. 16(5) 2014 11 strategic motivations to withhold information about product innovation activities (Arundel, 2001; Katila, Rosenberger, & Eisenhardt, 2008). However, for firms that have technological capabilities that reduce the risk of preemptive patenting by competitors, the tradeoffs managers face in publicly disclosing proprietary information will be lower for three reasons. One, for such firms disclosing their technological strengths will likely not enable competitors to replicate their R&D strategies or leapfrog their efforts to patent innovations (Dierickx & Cool, 1989; Ethiraj & Zhu, 2008). Two, for firms that have significant learning or lead-time advantages, disclosing their advantages may deter competitors from entering the same technology space (Polidoro Jr. & Theeke, 2012). Three, for firms that face a more competitive technology environment, broadcasting their R&D accomplishments might expand profit opportunities via cross-licensing or other cooperative arrangements (Harhoff, Henkel, & von Hippel, 2003). This study examines these potential deterrence and attraction effects of voluntary public disclosure on a sample of 322 firms in the pharmaceutical and communications equipment industries. The focus is on the impact of voluntary public R&D disclosures during the early stages of R&D before a firm would have filed for a patent on a given innovation and in the period during which managers would likely be more concerned with competitive patenting in the same technology space. Patent applications are used as a competitive outcome of interest because prior research has shown that a firm’s patent stock is considered to be an important source of competitive market advantage (Gans & Stern, 2003; Jaffe, 1986; Lerner, 1994; Somaya, 2012). The results are consistent with industry and firm-specific advantages moderating the effective use of disclosure, along with patents, to deter competitive imitation. This work contributes to literature on how firms capture value from competitive advantages, in general, and firms’ technological advantages, in particular. Disclosure is empirically examined as a value capture mechanism in the context of high-tech firms where firms’ efforts to protect their innovative ideas can be costly (Liebeskind, 1997), yet such efforts are necessary in industry contexts where owning patents are necessary but not sufficient conditions for capturing value from innovation (Gehl Sampath, 2007; James et al., 2013; Thumm, 2004). For firms that have technological advantages, strategically disclosing information signaling these strengths may deter competitors from patenting in the same technology space (Polidoro Jr. & Theeke, 2012). However, for firms lacking these advantages secrecy may be a more effective mechanism for preventing competitive imitation (Cohen et al., 2000; Hemphill, 2004). The remainder of the paper proceeds as follows. The next section reviews literature on the use of disclosure as a value capture mechanism and develops hypotheses on industry and firm-specific advantages as moderators of the deterrence effects of this value capture mechanism. This is followed by empirical analysis of theoretical predictions. The paper concludes with a discussion of the implications for firms’ ability to effectively use intentional information disclosure along with patents as a value capture strategy.

VOLUNTARY PUBLIC DISCLOSURE AND PATENTING

Prior research suggests that credible R&D disclosures may help firms achieve a sustainable competitive advantage in developing and profiting from cutting edge technologies (Harhoff, 1996; Harhoff et al., 2003). An important implication of this research is if disclosure influences competitors’ R&D activities in the same technology space, a firm that credibly signals a technological advantage may reduce rivals’ development and patenting of competing product innovations. However, few studies have explored this possibility. A recent study investigated plausible motivations for voluntary public R&D disclosures outside of the patent system (James & Shaver, 2014). The results are consistent with firms that have a technological advantage having strategic motivations to disclose and highlight the need for more research on the competitive implications of such disclosure. Some firms might disclose their advantages to deter competition in strategic factor and product markets. Other firms may disclose to encourage development of complementary product innovations or to attract cross-licensing partners that control capabilities they need to profit from a product innovation. If R&D disclosures are indeed strategic and have the intended effect on competitors’ product innovation

12 Journal of Applied Business and Economics vol. 16(5) 2014 activities, then we would expect to observe competitive outcomes that are favorable to the disclosing firm. The following discussion outlines the effects of industry and firm-specific advantages on competitors’ patenting in the same technology space.

Industry Intellectual Property Regime Strength The characteristics of the industry in which a firm operates may dictate the effect of voluntary public disclosures on competitors’ patenting efforts. One important factor is whether the disclosing firm’s industry has strong inherent patent protection. An industry has a relatively strong IP regime when owning patents inherently provides a stronger defense against imitation or legal challenges from competitors. For example, in discrete product industries such as the pharmaceutical industry one patent tends to map into a given product innovation, and owning that patent provides a relatively strong intellectual property right to the owner, all other things equal. However, in complex product industries, such as communications equipment, a product innovation tends to consist of many patents, some of which may not be owned by the firm (Cohen, Goto, Nagata, Nelson, & Walsh, 2002; Cohen et al., 2000). Consequently, a given patent provides relatively weak IP protection for two reasons. First, it may be more difficult for a firm to make a broader claim of novelty for a patented technology and as a result that technology may be easier to invent around. Second, absent a cross-licensing agreement, other firms that own patents on similar technologies may make a claim of infringement against the firm. For instance, in 2000 NTP, Inc. filed a lawsuit against Research in Motion (RIM) for infringement against NTP’s patents on technologies similar to RIM’s patented technologies in the Blackberry® wireless email system. After an appeal of NTP’s claims, in 2006 Research in Motion paid NTP $612.5 million to settle the dispute1. In sum, the inherent strength of legal protection within an industry influences the strength of a firm’s patents and thus its technological advantage over rivals. It follows then that the strength of patent protection within an industry likely influences whether public disclosures will deter competitor’s subsequent patenting in the same technology space. For firms in industries with relatively strong patent protection, disclosures are more likely to result in competitors patenting fewer similar product innovations. Because firms in such industries have a stronger defense against potential legal challenges of their patents and are more likely to prevail in patent infringement disputes, disclosing technological advantages will likely deter competitive entry. In contrast, firms that operate in industries characterized by relatively weak patent protection would likely face greater competition after publicly disclosing proprietary information. Because firms in these industries have a relatively weak defense against potential legal challenges and are less likely to prevail against patent infringement claims, disclosures that signal technological opportunities to competitors that have a similar technology profile are less likely to deter entry. The foregoing logic leads to the following:

Hypothesis 1a: For firms that operate in industries characterized by a relatively strong intellectual property regime, R&D disclosures decrease patent applications by competitors in the same technology areas.

Hypothesis 1b: For firms that operate in industries characterized by a relatively weak intellectual property regime, R&D disclosures increase patent applications by competitors in the same technology areas.

Firm-Specific Advantages and Competitive Patenting Firms may be motivated to disclose proprietary R&D information because they have internally developed a strong defense against competitive entry into the same technology space. This firm-specific appropriability may stem from lead time and learning curve advantages that help firms to patent around core technology areas to wall off those areas (Denicolò & Alberto Franzoni, 2004). Such defensive patenting makes it more difficulty or costly for competitors to replicate the firm’s R&D strategies. To the extent that a firm has internally developed a strong defense against competitive entry, voluntary public

Journal of Applied Business and Economics vol. 16(5) 2014 13 R&D disclosures will likely deter competitors from developing and patenting innovations in the same technology space. The deterrence effect of disclosure will be more prominent in cases where a lead-time advantage is an important mechanism for profiting from innovation, and accelerating R&D spending over a shorter time period does not achieve the same R&D output as maintaining a given rate of R&D spending over a longer time period. These time compression diseconomies make it unprofitable for rivals to redirect R&D spending into more promising areas highlighted by disclosures from firms that have lead-time advantages (Dierickx & Cool, 1989). Under these conditions, rivals are less likely to engage in direct competition with the disclosing firm. However, for firms that do not have internal capabilities that make competitive entry into the same technology space more difficult disclosure will likely have an unintended negative competitive effect. Another possibility is firms might disclose to broadcast their capabilities and attract trading partners with the ultimate goal of increasing profit opportunities. This motivation is likely in cases where firms face a more competitive landscape where other firms control complementary technologies or other capabilities (i.e., specialized manufacturing, sales, or service capabilities) that are necessary to commercialize an innovation. In such cases, disclosing technological accomplishments may help the firm to accelerate the commercialization of an innovation. Firms may also disclose early technological breakthroughs to lead the development of a new technological standard or to develop a new customer base for a breakthrough product innovation (Harhoff et al., 2003; Spencer, 2003). Thus, I hypothesize:

Hypothesis 2: Technological capabilities indicating firm-specific appropriability strength will have a negative association with patent applications by competitors in the same technology areas.

The foregoing discussion leads to the following conjectures about the effect of voluntary public disclosure on competitors’ patenting in the same technology space. One, for firms that operate in industries characterized by relatively strong patent protection, R&D disclosures decrease patent applications by competitors in the same technology areas. However, for firms that operate in an industry with relatively weak patent protection, we would expect the opposite effect. By disclosing their R&D strategies, firms in such industries would make it easier for competitors to develop and patent innovations in the same technology space. Therefore, absent other motivations to disclose such as to attract licensing or alliance partners, for firms in an industry with relatively weak patent protection secrecy would be a more effective mechanism for protecting profits from innovation. Two, holding industry level appropriability constant, firms that have a strong internal defense against competitive imitation might also deter competition by disclosing. By developing and patenting innovations around core technology areas, such firms can make it more costly for others to replicate their R&D strategies without adequate compensation. For firms that do not have this strong technology position, disclosing proprietary information about technological opportunities would likely attract competitive entry. The next section empirically examines these effect of industry and firm-specific appropriability on competitive patenting in the same technology space.

EMPIRICAL ANALYSIS

Data and Sample The research setting is all firms that operate in the global communications equipment (SIC codes 3661, 3663, and 3669) and pharmaceutical preparation (SIC 2834) industries and trade stocks on a US exchange over the period 1990 to 2004. These industries are investigated because they differ significantly in the inherent strength of patent protection. This distinction stems from the tendency of products in the pharmaceutical industry to consist of relatively few patentable elements (i.e., discrete product technologies) compared to firms in the communications equipment industry where products tend to

14 Journal of Applied Business and Economics vol. 16(5) 2014 consist of a much larger number of patentable elements (i.e., complex product technologies), some of which the firm may not control. Invoking variance in the strength of industry level patent protection allows us to distinguish between industry and firm-specific factors that drive the competitive effects of disclosure. Specifically, if the analysis shows that disclosure has opposite effects from those implied by prior research, then firm- specific factors might better explain the competitive implications of disclosing proprietary information. Alternatively, if the empirical analysis demonstrates the expected effects then industry factors likely have a strong influence on competitive patenting in the same technology space. The sample includes all firms that have R&D expenditures at any point in the sample period and financial statement data available. Because most of the independent variables have one-period lags, this sampling process yields an initial sample of 2,790 firm year observations for 322 firms from 1991-2004. Disclosure data are drawn from all press releases issued by a firm or by others on behalf of the firm to major news wires (i.e., PR Newswire, Business Wire). Patent data are drawn from the National Bureau of Economic Research Patent Data file and the USPTO. The Compustat Industrial Annual database provides financial data.

Dependent Variable Patent counts have been used to measure innovativeness in prior technology and innovation research. I utilize patent applications as an outcome of a firm’s technological advantage for two reasons. First, recent theoretical and empirical work suggests that internal attributes of a firm that increase their ability to capture value from innovation are more likely to generate a competitive advantage and superior performance compared to external factors such as industry patent protection. Second, owning more patents can strengthen a firm’s bargaining position when negotiating technology licensing arrangements. Competitors’ patent applications are the number of patent applications from firms in the same technology classes as the focal firm in a given firm year. Technology classes for each patent owned by firms in the sample were drawn from USPTO raw patent data. The data indicate heterogeneity across firms and across industries in the incidence of R&D disclosures. Table 1 presents a frequency distribution of R&D disclosure over the sample period 1991- 2004. Panel A includes the distribution for pharmaceutical firms. Panel B shows the frequency of disclosures for communications equipment firms. Interestingly, pharmaceutical firms have more years with disclosures and larger numbers of disclosures than communications equipment firms. In contrast, communications equipment firms have more years with no disclosure (93%) compared to pharmaceutical firms (56%). Thus, although many firms never publicly disclose R&D information, such disclosure is not rare as some firms disclose regularly.

Journal of Applied Business and Economics vol. 16(5) 2014 15 TABLE 1 FREQUENCY DISTRIBUTION OF DISCLOSURES BY FIRM-YEAR

Panel A: Pharmaceuticals

Value of Disclosures Frequency Percent of all observations 0 813 55.99 1 240 16.53 2 148 10.19 3 86 5.92 4 59 4.06 5 32 2.2 6 23 1.58 7 14 0.96 8 10 0.69 9 8 0.55 10 2 0.14 11 4 0.28 12 2 0.14 13 2 0.14 14 5 0.34 15 1 0.07 16 1 0.07 19 1 0.07 22 1 0.07 n=1452, 166 firms

Panel B: Communications Equipment

Value of Disclosures Frequency Percent of all observations 0 1,244 92.97 1 65 4.86 2 12 0.9 3 8 0.6 4 3 0.22 5 2 0.15 6 2 0.15 7 1 0.07 10 1 0.07 n=1338, 156 firms

Independent Variables R&D Disclosure R&D disclosures in a given firm year are equal to the count of all press releases containing R&D information, as discussed previously, which are coded “1”. R&D disclosures that include information about projects in the initial research phase of the innovation process and before the development or testing of a product innovation measure a firm’s ‘early-stage’ disclosures (Early-stage R&D Disclosures). R&D disclosures about projects in the initiation research phase that exclude all references to patent applications measure a firm’s research disclosures (Research R&D Disclosures).

16 Journal of Applied Business and Economics vol. 16(5) 2014 Early-stage R&D Disclosures and Research R&D Disclosures are included as more robust measures of R&D disclosure to control for the possibility that total R&D disclosures might include information previously disclosed to the FDA or the USPTO.

Industry IP Regime Strength To measure the effect of industry level patent protection on competitors’ patenting activities, the empirical analysis is conducted on split industry subsamples. As discussed previously, prior research has shown that industries differ in the extent to which they seek to patent innovations and the effectiveness of this mechanism for capturing value from R&D.

Firm-Specific Appropriability Strength Self-citation Ratio and Patent Applications are used as measures of firm-specific appropriability or technological strength. Self-citation Ratio, measured as total self-citations divided by total citations to a firm’s patents in a given firm year, is included as a measure of a firm’s internal intellectual property protection. Self-citation ratio is an indicator of the extent to which firms build on the stock of owned technologies in developing product innovations. Despite the limitations and criticisms of patent citations, self-citation ratio represents the extent to which a firm retains the value of R&D within internal boundaries. Firms that develop and patent innovations around core technology areas (i.e., patent thickets) have a stronger internal defense against patent infringements by competitors. Self-citation Ratio is also used in sensitivity analyses to distinguish the effects of firm-specific technological advantage from an industry advantage. Patent Applications are measured as the count of all patent filings by the focal firm. This variable is a measure of a firm’s stock of technological capabilities.

Control Variables Publication R&D disclosures are the count of press releases that highlight publication of R&D outcomes in scientific journals. I include this measure to distinguish disclosures directly to the public from scientific publications which are not necessarily widely disseminated. Also included are controls for important firm attributes found in previous research to have a positive relationship with disclosure – secondary offerings, R&D spending, self-citation ratio, and patent applications. Secondary Offerings measure a firm’s need for financing in public capital markets. Measured in millions of dollars, this variable is the value of secondary offerings of debt and equity securities. These data were collected from the Securities Data Corporation database of new issues. R&D Spending indicates the extent to which firms have different levels of R&D inputs which likely influences disclosure. Self-citation Ratio and Patent Applications are included in models estimating the effect of Industry IP strength on competitors’ patenting behavior. Both variables are defined in the previous section. All models include the dummy variable post1995 to control for unobserved temporal effects, such as changes in disclosure requirements on firms’ patenting efforts. In 1995, the Securities & Exchange Commission increased disclosure requirements that likely influence a firm’s propensity to disclose R&D information. Table 2 presents descriptive statistics and correlations among the variables. Panel A includes data for the 1,452 firm-years for 166 pharmaceutical firms in the sample. Panel B provides statistics for the 1,338 firm-years for 156 communications equipment firms in the sample.

Journal of Applied Business and Economics vol. 16(5) 2014 17 TABLE 2 DESCRIPTIVE STATISTICS AND CORRELATIONS

Panel A: Pharmaceutical Firms

Mean Std. Dev. Min Max 1 Competitors' Patent Applications 45.01 116.08 0 942

2 R&D Disclosurest-1 1.32 2.26 0 22

3 Early-stage R&D Disclosurest-1 0.47 1.11 0 11

4 Research R&D Disclosurest-1 0.31 0.84 0 11

5 Publication R&D Disclosurest-1 0.06 0.38 0 6

6 Secondary Offeringst-1 8.42 56.34 0 1340

7 Self-citation Ratiot-1 0.23 0.32 0 1

8 R&D Spendingt-1 252.88 726.90 0 12183

9 Patent Applicationst-1 20.97 54.89 0 415

1 2 3 4 5 6 7 8 9 1 1 2 0.004 1 3 0.054* 0.768* 1 4 0.054* 0.698* 0.878* 1 5 0.012 0.542* 0.639* 0.587* 1 6 0.049 -0.017 0.017 -0.004 -0.015 1 7 0.306* 0.075* 0.090* 0.049 0.062* 0.005 1 8 0.441* 0.023 0.009 0.045 -0.042 0.033 0.213* 1 9 0.732* 0.005 0.054* 0.074* 0.002 0.010 0.308* 0.576* 1 n=1452, 166 firms * p<0.05

Panel B: Communications Equipment Firms

Mean Std. Dev. Min Max 1 Competitors' Patent Applications 18.97 97.00 0 938

2 R&D Disclosurest-1 0.12 0.61 0 10

3 Early-stage R&D Disclosurest-1 0.08 0.50 0 8

4 Research R&D Disclosurest-1 0.04 0.28 0 4

5 Publication R&D Disclosurest-1 0.001 0.04 0 1

6 Secondary Offeringt-1 19.59 147.35 0 3275

7 Self-citation Ratiot-1 0.07 0.14 0 1

8 R&D Spendingt-1 131.84 546.32 0 5152

9 Patent Applicationst-1 20.89 111.29 0 1400

18 Journal of Applied Business and Economics vol. 16(5) 2014 1 2 3 4 5 6 7 8 9 1 1

2 0.510* 1

3 0.484* 0.928* 1

4 0.410* 0.794* 0.877* 1

5 -0.006 0.279* 0.267* 0.271* 1

6 0.159* 0.322* 0.320* 0.159* -0.005 1

7 0.236* 0.146* 0.149* 0.135* 0.004 0.143* 1

8 0.576* 0.674* 0.654* 0.547* 0.259* 0.370* 0.234* 1

9 0.754* 0.512* 0.487* 0.409* 0.054* 0.342* 0.193* 0.654* 1 n=1338, 156 firms * p<0.05

Looking at the raw correlations for pharmaceutical firms in Panel A, with the exception of total R&D Disclosures, Publication R&D Disclosures, and Secondary Offerings, have significant positive correlations with Competitors’ Patent Applications. In addition, all of the significant variables have a positive sign, which is contrary to what theories of strategic disclosure suggest. Similarly, the descriptive statistics for communications equipment firms in Panel B show that all variables have positive and significant correlations with the dependent variable except for Publication R&D Disclosures. The empirical analysis that follows further explores what might be driving these surprising descriptive results. Other noteworthy descriptive results include positive correlations between important firm attributes and R&D Disclosure for firms in both industries. In the pharmaceutical subsample, Self-citation Ratio has a positive and significant correlation with total R&D disclosures and Early-stage disclosures. Patent Applications have a significant positive correlation with Early-stage R&D Disclosures and Research R&D Disclosures. In the communications equipment firm subsample, Self-citation Ratio has a significant positive correlation with total R&D Disclosures and Early-stage R&D Disclosures. Patent Applications have a significant positive correlation with all types of R&D disclosures.

Analysis The econometric approach used to test the hypotheses regresses Competitors’ Patent Applications on the independent variables and controls. A Poisson or negative binomial regression model with fixed effects is recommended to deal with dependent count variables of this sort (Cameron & Trivedi, 1998; Greene, 1994; Hausman, Hall, & Griliches, 1984). The variance in the dependent variable is significantly larger than the mean which indicates over-dispersion. The negative binomial specification allows us to control for this over-dispersion. However, critics argue that the fixed effects negative binomial is not a true fixed effects estimator. This specification estimates the conditional mean using a fixed parameter to account for over-dispersion in the variance, rather than include fixed effects in the model estimating the dependent variable (Allison & Waterman, 2002; Greene, 1994). Thus, to test the hypotheses I use a fixed-effects Poisson estimator for the following reasons. As demonstrated by Hausman, Hall, and Griliches (1984), this specification includes true firm fixed effects which controls for unobservable factors. In addition, ‘the fixed-effect Poison estimator has very strong robustness properties for estimating the parameters in the conditional mean’ (see Wooldridge, 2002, pages 674-675) . Specifically, this method allows for over-dispersion or under-dispersion in the variance of the dependent variable and thus addresses the limitations of the negative binomial estimator. Because the fixed effects Poisson regression excludes all firms that have zero observations for the dependent variable across all firm years in the sample, the analysis is based on a usable sample of 1,283

Journal of Applied Business and Economics vol. 16(5) 2014 19 firm-years for 139 pharmaceutical firms and 847 firm years for 96 communications equipment firms. Two different models are estimated for the pharmaceutical subsample and the communications equipment subsample, respectively. Models Pharma1 and Comm1 include Early-stage R&D Disclosures and the control variables. Pharma2 and Comm2 replace Early-stage R&D disclosures with Research-stage R&D Disclosures.

Results Table 3 presents the results of fixed effects Poisson regressions on competitors’ patent applications in the same technology classes in a given year as the dependent variable.

TABLE 3 FIXED EFFECTS POISSON REGRESSION RESULTS BY INDUSTRY (ROBUST STANDARD ERRORS IN PARENTHESES)

Dependent Variable: Competitors’ Patent Applications

Pharma1 Comm1 Pharma2 Comm2 Pharma3 Comm3

R&D Disclosurest-1 -0.07*** 0.16*** (0.003) (0.004) Early-stage R&D Disclosurest-1 -0.04*** 0.15*** (0.005) (0.005) Research R&D Disclosurest-1 -0.11*** 0.16*** (0.006) (0.008) Publication R&D Disclosurest-1 0.11*** -2.43*** 0.08*** -2.45*** 0.08*** -2.36*** (0.02) (0.32) (0.02) (0.32) (0.02) (0.32)

Secondary Offeringt-1 0.0004*** -0.0003*** 0.0003*** -0.0003*** 0.0003*** -0.0002*** (0.00004) (0.00003) (0.00004) (0.00003) (0.00004) (0.00002)

Self -citation Ratiot-1 0.65*** 0.94*** 0.65*** 0.92*** 0.65*** 0.83*** (0.02) (0.06) (0.02) (0.06) (0.02) (0.06)

R&D Spendingt-1 -0.0002*** -0.0005*** -0.0002*** -0.0004*** -0.0002*** -0.0004*** (0.00001) (0.00001) (0.00001) (0.00001) (0.00001) (0.00001)

Patent Applicationst-1 0.005*** 0.002*** 0.006*** 0.002*** 0.005*** 0.002*** (0.0001) (0.00004) (0.0001) (0.00004) (0.0001) (0.00004) Post1995 0.04*** 1.40*** 0.001 1.37*** 0.02+ 1.41*** (0.01) (0.03) (0.01) (0.03) (0.01) (0.03) Log Likelihood -25367.99 -7118.74 -25589.95 -7331.77 -25469.96 -7624.82 Χ2(7) for covariates 9362.45*** 12367.03*** 9030.24*** 12070.55*** 9294.19*** 11458.32*** 1,283 pharmaceutical firm years, 139 firms, 847 communications equipment firm years, 96 firms + p<.10, ** p<.05, *** p<.01

Hypothesis 1a argued that firms operating in a strong industry IP regime would show a negative association between R&D disclosures and competitors’ patent applications in the same technology areas as the focal firm. Hypothesis 1b argued the opposite effect for firms operating in a weak industry IP regime: a positive association between R&D disclosures and competitors’ patent applications. The results

20 Journal of Applied Business and Economics vol. 16(5) 2014 for pharmaceutical firms show that the coefficients for all types of R&D disclosures are negative and significant. In contrast, communications equipment firms show positive and significant results for all types of R&D disclosures. Table 4 includes t-tests comparing the results for pharmaceutical firms with those for communications equipment firms, demonstrating that the findings are significantly different from zero. Taken together, the analysis in Table 3 and Table 4 provide strong support for hypothesis 1a and hypothesis 1b.

TABLE 4 COMPARISON OF FIXED-EFFECTS POISSON REGRESSION COEFFICIENTS BY INDUSTRY

(1) (2) Pharmaceutical Communications Equip. t-test Coefficient Std. Dev. Coefficient Std. Dev. Mean p-value

R&D Disclosuret-1 -0.07*** 0.11 0.16*** 0.12 (1)-(2)≠0 0.0000

Early-stage R&D Disclosuret-1 -0.04*** 0.18 0.15*** 0.15 (1)-(2)≠0 0.0000 Research R&D Disclosuret-1 -0.11*** 0.21 0.16*** 0.23 (1)-(2)≠0 0.0000 (1) 1,283 firm years, 139 firms (2) 847 firm years, 96 firms

Overall, these results are consistent with firms in an industry with strong patent protection having greater strategic incentives to disclose technological advantages as these firms face lower risk of competitive imitation. Accordingly, competitors are less likely to develop and patent innovations in the same technology areas. However, in an industry with relatively weak patent protection competitors are more likely to engage in head to head competition as imitation costs (i.e., stemming from legal penalties associated with replicating a firm’s technologies) would be lower in this context. To test hypothesis 2, the moderating effect of firm-specific technological strength on competitors’ patent applications in the same technology space is estimated in two ways: self-citation ratio and patent applications, respectively, as measures of firm-specific appropriability to assess whether firms with stronger technological capabilities exhibit differential effects of R&D disclosure on competitors’ patenting efforts from firms with relative weaker capabilities. First, the sample was split into two groups based on firms’ own patenting efforts: (1) firms that actively file new patent applications and (2) firms that do not file patent applications. Second, additional analyses were conducted based on firms’ citing of their owned patents: (1) firms that cite their owned patents in patent applications and (2) firms that do not cite their owned patents. Table 5 presents these results for pharmaceutical firms. Panel A presents results comparing subsamples based on pharmaceutical firms’ own patenting efforts versus firms that do not have patent applications. Panel B replicates this analysis for subsamples of pharmaceutical firms that cite their owned patents versus firms that do not. Table 6 replicates the analysis in Table 5 on communications equipment firms.

Journal of Applied Business and Economics vol. 16(5) 2014 21 TABLE 5 PHARMACEUTICAL INDUSTRY COMPARISON OF FIXED-EFFECTS POISSON REGRESSION COEFFICIENTS

Panel A – By Number of Patent Applications

(1) (2) Patent Applications>0 Patent Applications=0 t-test Coefficient Std. Dev. Coefficient Std. Dev. Mean p-value

R&D Disclosuret-1 -0.09*** 0.08 -0.03 0.61 (1)-(2)≠0 0.0937

Early-stage R&D Disclosuret-1 -0.07*** 0.14 0.89*** 1.64 (1)-(2)≠0 0.0000

Research R&D Disclosuret-1 -0.17*** 0.18 1.25*** 1.80 (1)-(2)≠0 0.0000 (1) 866 firm years, 121 firms (2) 294 firm years, 59 firms

Panel B – By Self-citation Ratio

(1) (2) Self-citation Ratio>0 Self-citation Ratio=0 t-test Coefficient Std. Dev. Coefficient Std. Dev. Mean p-value

R&D Disclosuret-1 -0.05*** 0.07 -0.16*** 0.36 (1)-(2)≠0 0.0000

Early-stage R&D Disclosuret-1 -0.06*** 0.12 0.25*** 0.61 (1)-(2)≠0 0.0000

Research R&D Disclosuret-1 -0.11*** 0.14 0.11*** 0.78 (1)-(2)≠0 0.0000 (1) 567 firm years, 87 firms (2) 629 firm years, 105 firm

TABLE 6 COMMUNICATIONS EQUIPMENT INDUSTRY COMPARISON OF FIXED-EFFECTS POISSON REGRESSION COEFFICIENTS

Panel A – By Number of Patent Applications

(1) (2) Patent Applications>0 Patent Applications=0 t-test Coefficient Std. Dev. Coefficient Std. Dev. Mean p-value

R&D Disclosuret-1 0.10*** 0.08 -1.59*** 9.51 (1)-(2)≠0 0.0061

Early-stage R&D Disclosuret-1 0.09*** 0.09 -11.88 5774.44 (1)-(2)≠0 0.9743

Research R&D Disclosuret-1 0.09*** 0.16 -12.58 13076.83 (1)-(2)≠0 0.9880 (1) 477 firm years, 72 firms (2) 242 firm years, 44 firms

22 Journal of Applied Business and Economics vol. 16(5) 2014 Panel B – By Self-citation Ratio

(1) (2) Self-citation Ratio>0 Self-citation Ratio=0 t-test Coefficient Std. Dev. Coefficient Std. Dev. Mean p-value

R&D Disclosuret-1 0.10*** 0.06 -0.20 4.10 (1)-(2)≠0 0.1247

Early-stage R&D Disclosuret-1 0.10*** 0.07 0.46 10.81 (1)-(2)≠0 0.4842

Research R&D Disclosuret-1 0.12*** 0.13 -0.89 16.11 (1)-(2)≠0 0.1882 (1) 315 firm years, 59 firms (2) 442 firm years, 66 firm

Turning to the results for pharmaceutical firms in Table 5, panel A shows firms that actively file patent applications indicate a negative association between different types of R&D disclosure and competitors’ patenting in the same technology areas. However, for firms that have no patent applications, we observe a positive association between disclosure and competitors’ patenting. Taken together, these results are consistent with firm-specific technological strength driving this relationship. Similarly, the analysis in panel B contrasting pharmaceutical firms that cite their owned patents with those that do not yields consistent results. The results of t-test indicate that the coefficients on different types of R&D disclosures are significantly different from zero, providing strong support for hypothesis 2. The results for communications equipment firms are notably different from pharmaceutical firms. Panel A in Table 6 compares the results for firms actively file patent applications with those that do not. The results for firms with patent applications greater than zero indicate a positive association between R&D disclosure and competitors’ patent applications. Panel B replicates this analysis of firms that cite their owned patents versus firms that do not and yields similar results. These results are consistent with arguments supporting a weak industry IP regime making disclosing firms more vulnerable to competitors’ patenting even in the presence of firm-specific technological strength. Interestingly, for firms that do not have patent applications or do not cite their owned patents, the results are not significant. The next section discusses the implications of these findings.

DISCUSSION AND CONCLUSION

This study investigates the influence of voluntary public R&D disclosures on competitors’ patenting in the same technology areas. The results show that R&D disclosures by pharmaceutical firms have a negative association with competitive patenting while disclosures from communications equipment firms have a positive association. These findings are consistent with industry IP strength driving the negative association between R&D disclosure and competitors’ patenting strategies. Further analysis of how firm- specific technological capabilities moderate the effect of disclosure on competitors’ patenting demonstrates evidence of firm-specific advantages negatively influencing competitors’ patenting efforts. Overall, the results for pharmaceutical firms demonstrate that firm technological capabilities are an important influence on competitors’ efforts to innovate in the same technology space. Further, the results demonstrate that these firm-specific advantages are distinct from industry advantages. Otherwise, if industry advantage was driving the association between disclosure and competitive patenting, we would expect to find a consistent negative effect of disclosure across all subsamples of pharmaceutical firms. In the communications equipment subsample, the results for firms that cite their owned patents show a positive and significant association between disclosure and competitors’ patenting, suggesting that industry advantages moderate this relationship. The non-significant results in the subsample of firms that do not cite their owned patents provide inconclusive evidence of industry level effects. On the one hand,

Journal of Applied Business and Economics vol. 16(5) 2014 23 if weak industry patent protection was driving the association between disclosure and competitive patenting, then we would expect to observe a consistent significant positive effect of disclosure on competitors’ patenting activity. On the other hand, the non-significant results in the subsample of firms that do not have patent applications or cite their owned patents might lead us to conclude that public disclosures have no effect at all on competitors’ patenting strategies. One alternative explanation might be that in industries with relatively weak IP regimes firms tend to defensively patent innovations for future use as bargaining chips in cross-licensing or other cooperative negotiations (Cohen et al., 2000; Grindley & Teece, 1997; Rothaermel, 2001). Another alternative is firms disclose their R&D accomplishments to encourage other firms to patent complementary technologies which are necessary to commercialize innovations (Harhoff et al., 2003; Spencer, 2003). Taken together, the results of this study highlight the importance of both industry and firm-specific appropriability advantages. These findings have important implications for technology strategy literature, which examines mechanisms firm use to capture value from their innovations (Cohen et al., 2002; Cohen et al., 2000; James et al., 2013; Levin et al., 1987) as well as work on new product development (insert cites). This work contributes to strategy research on how firms achieve and sustain competitive advantages (Barney, 1991; Peteraf, 1993; Porter, 1985), in general, and how firms capture value from their technological competitive advantages (James et al., 2013; Somaya, 2012), in particular. The extent to which firms have costly-to-imitate resources depends on rivals’ ability to develop these resources internally or to acquire them in strategic factor markets, and information acquisition costs influence this ability (Makadok & Barney, 2001). Voluntary public disclosures reduce information uncertainty and as a result lower competitors’ information acquisition costs. The current study complements this research by demonstrating that firms can not only increase their competitive advantage by developing costly-to- imitate product innovations, but also by deterring competitors from patenting similar product innovations in the same technology space (Polidoro Jr. & Theeke, 2012; Polidoro Jr. & Toh, 2011). Future studies that explore other competitive effects of disclosure will provide further insights on how firms can effectively manage the tradeoff between secrecy and disclosure in different contexts to capture value from innovation.

Managerial Implications This study offers important insights for strategic managers. For managers in firms that have a strong technological advantage, publicly disclosing R&D successes may increase their ability to profit from product innovations while also mitigating imitation risk. This insight is contrary to conventional wisdom within some firms that never publicly disclose ahead of commercializing an innovation despite their competitive advantages. The results of this study suggest that in some cases strategic public disclosure of a firm’s technological strengths might deter competitive entry into the same technology space. For managers in firms that do not possess technological advantages, this study confirms that keeping intermediate R&D successes secret may be a more effective mechanism for protecting profits from innovation. In such cases, the profit-maximizing strategy is to disclose product innovations only around the timing of launching new products in order to capture value innovations without enabling competitors to appropriate this value (Bayus, Jain, & Rao, 2001; Dranove & Gandal, 2003; Haan, 2003).

ENDNOTE

1. Research in Motion corporate press release dated March 3, 2006. http://press.rim.com/release.jsp?id=981

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26 Journal of Applied Business and Economics vol. 16(5) 2014

The New Normal: Fundamental Shifts for 21st Century Organizations and for the CIOs Who Lead Them

Mark W.S. Chun Pepperdine University

Charla Griffy-Brown Pepperdine University

Harvey Koeppel President, Pictographics Inc.

INTRODUCTION

The demands upon the Chief Information Officer (CIO) have changed significantly since its inception over 30 years ago. Increasingly, these executives have been responsible for many of the innovations that firms have taken advantage of throughout the recent world economic turbulence. Whether through cost reduction or innovation, the CIO has played a critical role in ensuring that organizations have received sustained or innovative services amidst drastically shrinking budgets. Yet in many organizations, the IT function has been seen as a hindrance rather than an enabler of the firm’s ability to innovate. The costs associated with IT continue to escalate, while the time to market for new products and services seems to have gotten longer in an era where “real-time,” “nimble,” “adaptable,” and “scalable” has continued to be more and more important than ever before [Chun and Mooney, 2009]. In a recent Chief Executive Officer (CEO) study [IBM Whitepaper, 2012], over 1,700 CEOs representing 64 countries and 18 industries were asked to rank a list of the external factors that will impact their organizations over the next three to five years. Technology was perceived as the leading external factor. As seen in Figure 1, Technology has consistently increased in importance from sixth in 2004 when the study was originated to first in 2012.

Journal of Applied Business and Economics vol. 16(5) 2014 27 FIGURE 1 “WHAT ARE THE MOST IMPORTANT EXTERNAL FORCES THAT WILL IMPACT YOUR ORGANIZATION OVER THE NEXT 3 TO 5 YEARS?”

These findings should be viewed as a wakeup call for CIOs to re-think how technology is used and leveraged within their organizations. They must adapt their skills, roles, and responsibilities to match their organizations’ needs with both existing and ever-changing technology capabilities if they expect to meet the demands set by their CEOs and executive teams. Our research has highlighted several emerging IT trends that have had significant impact upon how CIOs were required to further develop and evolve their roles and responsibilities. These include: 1. Social Networking Technologies (SNT) has emerged as a transformative way to access or to disseminate information across multiple constituencies, stakeholders and platforms. Yet these new technologies have presented significant security risks to the corporation’s operating infrastructures and knowledge assets. 2. Cloud computing has provided CIOs a new alternative means to deliver cost-effective and scalable applications, processing power, information storage, and other critical IT resources on demand. Leveraging these alternatives has had a significant impact upon the organization’s financial management, as implementing (external) Cloud computing resources has allowed firms to move owned or leased assets from the organization’s balance sheet and to reclassify the related costs as operating expenses. CIOs and management teams must decide how to adapt new business processes horizontally across the organization to maximize the benefits offered by Cloud computing. 3. “Affordability” (better known as “Better-Faster-Cheaper-NOW”) has been a common topic of discussion among corporate executives as they have been further tasked to drive the organization to do even more with less in a turbulent economy. The 2008 global financial crisis has forced firms to cut IT budgets, yet CIOs have been expected to engage the organization to use existing and new technology resources to do even more with much less. 4. Real-time analytics capabilities have become a must-have for many organizations with the explosion of data everywhere, especially customer information derived from social media sources as well as internal customer records. Firms have increasingly demanded immediate access to internal corporate data and external data from an ever-growing list of sources to develop insights and to generate actionable responses that produced measurable results in real-time. The ability to provide the firm with real-time analytical capabilities to test market strategies or understand the

28 Journal of Applied Business and Economics vol. 16(5) 2014 immediate effectiveness of marketing campaigns is only one example of how CIOs have been held to a higher standard of responsiveness and service delivery. 5. Mobility is a critical dynamic that CIOs must leverage. It is estimated that by 2017 there will be more networked mobile devices than people on the planet. This explosion in digital ecosystems, platforms, and ubiquitous connectivity means that CIOs must consider the business implications both strategically and operationally for leveraging mobile devices to create business value for their organizations. 6. “Bring your own device” (BYOD also known as “consumerization of IT”) is an increasing trend in which employees bring personally owned mobile devices to work and use these devices to access privileged company resources such as email, company data, customer data., competitive data, etc. In the private sector, 74% of individual workers paid for their laptops themselves. However, only 12% of companies have formerly encouraged this practice or have policies to oversee it. Increasingly CIOs have needed to implement policies, create standards, and deploy tools and technologies to manage this heterogeneous environment. 7. Finally, there has been an increased influence and leveraging of external alliances and partnerships. CIOs have begun to extend their roles and responsibilities outside of the firm to collaborate more with customers, vendors, supply chain partners, and even -- in some situations -- competitors. CIOs needed to find ways to adopt and leverage new technologies that connected to their ecosystem in ways that were productive, added value and are, of course, was secure.

HOW CIOS HAVE PREPARED FOR THE NEW NORMAL

Over thirteen years ago, Ross & Feeney [Ross and Feeney, 1999] presented research on the evolving role of the CIO. At that time they described three key eras and the driving forces that were influential on the CIO’s role within each of them. The three stages represent the growth process of the CIO role as a function of organizational learning, depicting the evolution of the CIO’s credibility and status. The first era was the Mainframe Era. During this time, the role and responsibilities of the CIO were focused upon operational management of highly specialized functions. CIOs were responsible for the on-time delivery of automated or semi-automated products and services, and they ran operations that were heavily focused upon availability and reliability. The second era was the Distributed Era. During this time, the role of the CIO evolved to become more of a strategic partner with other executives. Tactical operations management skills became more important as the role evolved into more of a strategic leader who was focused on more closely aligning IT with business strategies and outcomes. Typically CIOs at this stage engaged other members of the executive team, assisting in designing the organization, advising on IT architecture, and procuring technology for the organization. The third era was the Wed-Based Era. Here, one of the key tasks of the CIO was to help drive the company’s internal and external strategy by supporting the development of new business models for use on the Internet and by introducing internal management processes that leveraged web-based technologies. In our study, we were interested in understanding the skills, competencies, roles, and responsibilities of CIOs as it continued to evolve amidst ever-changing dynamics, most recently characterized by the New Normal (i.e. the post-2008 turbulent socio-economic and political environment). We pursued the answers to three key questions: 1. What are the current roles & responsibilities of CIOs? 2. How have these roles & responsibilities changed over the last 10 years? 3. What are the attributes and characteristics of a successful CIO?

By focusing upon skills and competencies exhibited by successful CIOs, we hoped to gain a better understanding of what enables these executives to adjust their roles and help adapt their organizations to meet the challenges and opportunities presented by the New Normal.

Journal of Applied Business and Economics vol. 16(5) 2014 29 FIGURE 2 THE EVOLVING ROLE OF THE CIO

As a starting point, we identified both the business and technology drivers that characterize the environment within which today’s enterprises and their CIOs must operate. Tables 1 and 2 present these drivers as articulated by CIOs in this study.

TABLE 1 KEY BUSINESS DRIVERS IN THE NEW NORMAL

Key Business Drivers

Global economic & competitive challenges Increased role of government Political and social mandates for transparency CEOs in search of innovative business models to adapt and grow Traditional vendor and supply chain relationships replaced with collaborative partnerships Focus on delighting the customer Need for increased risk management & compliance

30 Journal of Applied Business and Economics vol. 16(5) 2014 TABLE 2 KEY TECHNOLOGY DRIVERS IN THE NEW NORMAL

Key Technology Drivers

Ubiquitous broadband access to the internet Mobile computing Consumerization of information technology Social everything Cloud computing / Everything as a Service (EaaS) Explosive growth in data generation, especially unstructured

Data-driven versus process-driven computing Real-time analytics and business intelligence Relentless focus upon information security

In the context of the New Normal, we learned that successful CIOs have expanded their roles and responsibilities from IT cost center managers to business-savvy enterprise leaders. These CIOs were full C-suite peers and have adapted their roles, responsibilities, and skills to match the changing needs of the enterprise and the marketplace within which they operate. They were responsible for educating colleagues and stakeholders and leading change both horizontally and vertically throughout their organizations. This evolution in CIO skills and competencies is shown in Table 3 below.

TABLE 3 THE EVOLUTION FROM IT COST CENTER MANAGER TO BUSINESS SAVVY CIO

From IT Cost Center Manager… To Business-Savvy CIO...

Management  Leadership

Operational Efficiency & Expense  Innovation & Growth Reduction

Incremental Process Improvement  Business Strategy & Process

Relationship Management & Reporting  Communication

Technology Management  Business Management

Risk Avoidance  Risk Management

Journal of Applied Business and Economics vol. 16(5) 2014 31 Historically, IT has created business value through the reduction or elimination of costs embedded within the organization by focusing upon streamlining processes and driving programs yielding operational efficiencies. Our research suggests that business-savvy CIOs were viewed as technology executives who took an active leadership role in their organizations by aligning and leveraging the IT function with the vision and strategic objectives of their enterprise and who ultimately enabled the creation of sustainable business value and growth. Our research has also shown that, in addition to leading change and enabling business value creation internally, business-savvy CIOs actively influenced and leveraged the external environment to enhance strategic advantage. These CIOs demonstrated influential leadership by developing new forms of alliances with partners and suppliers to create value from the outside of the organization. An example of this skill to influence the firm’s external environment was seen from the success story of the Bharti Airtel CIO, Jai Menon, who created strategic partnerships with external suppliers to plan and deliver infrastructure and operations based upon an innovative outcomes-based revenue sharing model. As part of his initiative, Menon was responsible for creating an outsourcing strategy to efficiently create scalable operations, to exploit best practices, and to obtain telecom industry knowledge on a global scale. The program included the integration of 64 different systems into a common platform, and created value for the firm by getting strategic partners and suppliers to embrace and support the firm’s integration efforts. Hence, we argue that to continue to establish and expand their credibility and sphere of influence, CIOs need to acquire and develop skills and characteristics [Leidner and Mackay, 2007] that enable them to support their company’s business vision, goals, and objectives by influencing strategic partners, alliances, and customers both internally and externally [Preston, et al., 2008].

FIGURE 3 AN EVOLVED ROLE OF THE CIO: THE EVOLUTION OF THE ROLE OF THE CIO

Our study indicated that successful CIOs were those who successfully adapted their roles in response to the demands of the internal enterprise and, at the same time, be responsive to and influenced key external relationships and processes. We interviewed and surveyed over 413 CIOs in both the public and private sectors and solicited their input on what, in their opinion, was needed become an effective CIO in the New Normal business environment (methodology section, Appendix #1). Through this study, we also learned that not all CIOs

32 Journal of Applied Business and Economics vol. 16(5) 2014 were able to change and adapt their roles, primarily due to the constraints of enterprise culture, legacy technology portfolios and their inability to influence internal and/or external change.

A NEW TYPE OF CIO LEADER: WHAT’S DIFFERENT ABOUT CIOS IN THE NEW NORMAL

Key CIO Competency #1: Leadership CIOs universally indicated that one of their key competencies was the ability to be an effective leader. Effective leadership extended across many facets of the organization – from clearly articulating the strategic vision of both the IT and business operations, to helping to facilitate organizational changes related to shifts in business operations as a result of technology implementation. These executives indicated that effective CIOs were able to successfully develop business-oriented skill sets throughout the IT organization and were able to facilitate a better understanding for how technology impacted operations throughout the business units. As part of their efforts, CIOs indicated that they needed to actively seek and respond to internal and external customers’ needs and to be able to establish insights that enabled the organization to drive business and technology strategies. Much of this leadership was demonstrated through coaching and developing talent within the organization. Leadership was also viewed as persuading others within the division and across other areas of the business to support new IT initiatives.

TABLE 4 LEADERSHIP BEST PRACTICES

Leadership Best Practices:

 Compel others to listen to you as a trusted advisor  Develop a strategic vision around the business planning cycles  Influence the appropriation process for budget allocation processes  Communicate the vision of how IT will drive business  Align business colleagues to a common IT vision  Maintain strong relationships with executive colleagues  Build strong teams and trusted relationships

Leadership Through Consensus Building A CIO from a Japanese automobile manufacturer indicated that successful leadership involved motivating those around him. He mentioned that the days of conducting business through the process of “let’s disagree and commit” to a project implementation are gone. Instead, the CIO indicated that his colleagues adopted the new viewpoint of consensus building – “Everyone agrees to support.” The key objective was to get everyone in the organization to support your initiative whether or not they fully agreed with it. Included in this was the ability to maintain strong relationships with other C-suite executives. We learned that CIOs who could establish the role as a trusted advisor for both technical and business-related issues were most apt to be able to build consensus rapidly and effectively during program implementation.

Talent Management: Building Capabilities and Expertise in Others CIOs also indicated that a major critical success factor was managing the talent within the IT department. There continued to be a consistent need to educate and to train IT staff on skills related to both the business and technology aspects of the enterprise. Oftentimes, IT staff members were primarily skilled in the technical aspects of the job, and they did not understand how to communicate with their

Journal of Applied Business and Economics vol. 16(5) 2014 33 business colleagues and to explain how their efforts influenced or affected business activities or outcomes. CIOs expressed the need to consistently train the IT staff not just on current or new technologies, but also how these could be leveraged throughout the business. Building effective capabilities involved expertise across three key domains: business, technical, and managerial. One Federal public sector CIO reflected, “If you build capability in others (i.e., supporting management), it allows you to sit back and to reflect on the strategic vision that you need to help formulate and support, and to be able to prioritize the numerous initiatives that the organization needs to accomplish.” CIOs expressed the need to train the IT workforce to be able to clearly define and measure the business value of technology investment in the context of the enterprise.

Preparing the Next Generation of CIOs CIOs shared that preparing the next generation of IT executives was an important part of their jobs. They indicated that there was a need to establish a consistent and reliable pipeline of IT professionals with skills and expertise in leading business and technology initiatives. Building capability across the IT organization to understand and to communicate with the business was a critical challenge expressed by both public and private sector CIOs. Public-sector CIOs indicated that one of the continuous pressing problems that existed within their agencies was the inability to retain talent. In public agencies, middle level IT managers were typically unwilling to remain with the agency and take on more senior positions, as public agencies typically did not invest the time or money to train upcoming IT executives. In fact, the organizational structures of public agencies were typically described as flat, resulting in the creation of fewer mid-level management positions than is true of private firms. This fact, coupled with the fact that public sector employees were oftentimes not as highly compensated as their private sector counterparts, meant that there exists little-to- no incentive for public sector IT executives to remain with the organization. Hence, the problem of not being able to develop or nurture the next generation of IT executives continued to be a challenge. Talent retention by CIOs within both the public and private sectors was accomplished by crafting very detailed development plans for high-potential and high-performing employees. Public-sector CIOs oftentimes employed a higher proportion of contract staff as compared to their private-sector counterparts. This staffing approach was reportedly used as a means to temporarily accommodate budgetary challenges. We learned that oftentimes 25% or more of the IT teams in public agencies were contractors who were temporarily hired to serve a specific agency need. This approach commonly manifested itself in an organizational environment with a short-term narrow focus. In the private sector this “narrowness” of focus was also reported, but it was more often the result of silos separating the IT function or business unit from other business units. In both cases, the key skills required by CIOs were the ability to develop incentives and unite talent to help business units move forward collaboratively rather than competitively, and to motivate their teams to move beyond “business as usual” or “keeping the railroad running” objectives.

Enforcing an Enterprise View of the Organization Through Transparency We learned that the one of the top objectives of CIOs was to motivate their organizations to think in terms of enterprise value rather than focusing solely upon point solutions. CIOs shared that one of the first steps to adopting an enterprise perspective was to establish trust with other division leaders across the organization. Establishing trust commonly began when the CIOs became stewards of information transparency and accuracy throughout the firm. In this context, unified data systems, data quality standards, and reduced data redundancy were described as imperative for eliminating “competing sources of truth.” Transparency was universally viewed as an essential and transformative principle of building trust and establishing leadership positions. CIOs unanimously struggled with the reality that “transparency cannot be achieved with stovepipes.” Hence, the competency of developing well-defined corporate-wide data models undergirding information stewardship was essential.

34 Journal of Applied Business and Economics vol. 16(5) 2014 Key CIO Competency #2: Innovation & Growth The second key competency for CIOs was the ability to engage in the creation and development of innovative technology-enabled growth strategies that aid an enterprise to maintain its competitive advantage in the marketplace. As part of this competency, CIOs noted that they proactively engaged in promoting the creative use of existing and emerging technologies to create new opportunities to grow the business. These technology executives underscored the need to identify new uses for existing technologies as well as the need to collaborate with internal and external constituents to drive innovation throughout the enterprise. Sources of innovation reported by CIOs interviewed included using new technology to replace or supplement existing solutions and drawing from external suggestions for how to more effectively leverage data to enhance or create new products and services. More than 75% of the CIOs involved in this study indicated a need to leverage technology to build a culture that is able to promote, engage in, and enable business innovation. Successful CIOs were able to link innovation to business performance outcomes. Constraints from the legacy systems or lack of collaboration were the most commonly cited reasons for CIOs not engaging in or embracing business innovation.

TABLE 5 INNOVATION & GROWTH BEST PRACTICES

Innovation & Growth Best Practices:

 Work regularly to develop a culture of innovation  Identify technology for competitive advantage  Establish capabilities for real-time data analytics  Secure resources for innovation by identifying opportunities  Identify both internal and external sources of innovation  Look for bottlenecks as well as how to leverage IT resources for value and growth  Lead initiatives to support flexibility and agility for rapid problem resolution  Ensure a proactive and collaborative process for evaluating innovation opportunities

CIOs told us that their ability to articulate and communicate their role in driving innovation and their ability to define and deliver enterprise value were key elements of earning the permission to innovate within their organizations. However, we learned that only a few CIOs were successful in selling their beliefs and obtaining investment resources needed to move innovative ideas forward. We additionally learned that CIOs were commonly challenged to offer new and innovative ways to use technologies to deliver disruptive innovations for competitive advantage, well beyond the more traditional responsibility of rapidly resolving operational problems. CIOs were tasked to be able to continuously examine how to use existing (and sometimes borrowed) IT resources to create new value and growth for their companies. Below, we offer several areas of focus that CIOs reported that enabled the innovation and growth of their organizations.

Build and Nurture Real-Time Data Analytics Capabilities Real-time data analytics has become a new expectation for CIOs. CEOs have expected the CIO to enable the organization to access and analyze data as it became readily available via live real-time information feeds, RSS feeds, social media, sensors and many other structured and unstructured data sources. In the past, it was common for IT departments to run end-of-day programs to process data collected throughout the day to generate reports for future review. Today’s business leaders expect CIOs to provide sophisticated analytics and immediate access to insights gained on a real-time basis. Providing real-time analytics capabilities has become the New Normal for CIOs in organizations that proactively

Journal of Applied Business and Economics vol. 16(5) 2014 35 identify trends and demand actionable real-time insights in support of their business strategies. The CIOs that we surveyed reported that they were expected to lead the enterprise in continuously improving business capabilities to collect, analyze, and distribute knowledge immediately after it became available in the marketplace. One CIO from a leading search portal in Beijing, China shared how the real-time mining of data streamed from users’ portal websites and micro-blogs helped them determine the feasibility and effectiveness of new product and service offerings. He indicated the ability to conduct real- time analytics helped the company to adjust marketing and product differentiating strategies as needed.

Innovating Data Delivery Through Social Networking The recent rise in popularity of social networking in both personal and business contexts has provided CIOs with a plethora of opportunities and challenges as they have introduced these new sources of process and data innovation across their organizations. CIOs were clear that many opportunities for innovation came from customers, partners, or, in the case of public sector CIOs, from their constituent community. Social networking has played a major role in facilitating these innovative collaborative activities. Enterprise acceptance of social networking technologies has required CIOs to acquire additional expertise to address expanded requirements for IT governance, data privacy, information security, and risk management associated with the deployment and use of these capabilities. CIOs have used social networking to enable their organizations to get closer to their customers through instantly accessing and responding to customer data mined through these channels in real-time. CIOs reported that social networking technologies also introduced new challenges for their organization in the area of marketing, communications, and public relations as potentially negative feedback left by customers now needs to be responded to in real-time to avoid damage to the firm’s brand. CIOs consistently indicated that social networking has forced their organizations to rethink their capabilities around generating and accessing new types of data and their ability to directly respond to data becoming available in real-time.

Adding Value from the “Outside In” Traditionally, firms have provided value to their customers from the inside out, meaning that they were able to provide products or services that maximize value to the customer at a price that customers were willing to pay. In this instance, the CIO’s primary role was to manage applications and data within the firm’s operations and to provide technology solutions that would aid in the development and delivery of new and improved products and services. The New Normal for CIOs includes adding value from the outside in, where customers, prospects and alliance partners provide inspiration for new products and services. A Texas city’s CIO shared an innovative approach where he relied on the citizens’ suggestions and ideas to identify and use publically available data to improve public services. The agency learned that one of the top frustrations for its citizens was the reporting, coordination, and fixing of potholes. Typically, citizens had to contact the appropriate agency to report the pothole, a work order was assigned through the appropriate channels, and the work was completed over a three-to-four week period. Constituents found the timeframe unacceptable. The city CIO was able to leverage social networking to enable citizens to report potholes to their neighborhood agencies electronically. Data was disseminated more efficiently and effectively to the multiple touch-points needed to address and fix the problem. Of importance was the ability of the CIO to facilitate rapid problem resolution and expectation management on behalf of his constituents. The key thing learned was that citizens wanted to be updated and informed on how long it would take to fix a problem. Customer response time was a critical driver of value, and the CIO had to have the skills to understand that priority (time), while becoming an active participant in community interaction and solution creation. This CIO was able to understand how to create value from the outside in.

Key CIO Competency #3: Business Strategy & Process CIOs indicated that another key competency in their job was the ability to understand and to act upon the internal and external forces influencing the company’s growth and success. These technology

36 Journal of Applied Business and Economics vol. 16(5) 2014 executives were challenged to not only manage the technology portfolio but also to think strategically to create growth, to improve financial performance, to gain a renewed competitive advantage, and to be able to envision an end-to-end view of value creation on behalf of customers, shareholders, and employees. Business-savvy CIOs actively engaged in and contributed to the development and enhancement of the business strategy of the firm by bringing together resources from across the organization and by championing the collaboration and teamwork required to execute on the strategy. Typically this was accomplished through the CIO’s understanding of the key business performance measures and their ability to enable and drive the organization to meet its strategic objectives.

TABLE 6 BUSINESS STRATEGY & PROCESS BEST PRACTICES

Business Strategy & Process Best Practices:

 Engage in systemic change management initiatives  Build processes that leverage organizational assets beyond information technology  Participate actively in developing business strategy  Actively collaborate with colleagues to create a unified vision  Champion service transformation  Lead clear governance processes that include engaging external partners  Develop indicators to directly link IT performance to business goals

Conducting a Feasibility Analysis for New Technological Integration Several of the CIOs surveyed reported that many of their legacy IT systems that were implemented over the years had “stove-piped” architectures, mainly because each business unit had its own unique objectives and funding sources. Many of these systems were designed to serve a specific function of the organization and generally did not integrate or share information across other business units’ applications vertically or horizontally. Stove-piped applications continued to exist in many firms, and the inability of these applications to share and integrate data remains one of the biggest challenges to CIOs. This issue was made considerably more challenging when multiple business units within an enterprise share often disparate information and applications with external strategic partners and stakeholders. Implementing these technologies provided a “quick fix” solution for accessing and aggregating data from siloed sources, but they also distracted an organization from dealing with fundamental business and technology issues that need to be addressed to efficiently address horizontal integration. Many CIOs surveyed discussed the opportunity to leverage cloud computing to supplement or to enhance the company’s IT applications and infrastructure. Cloud computing has required CIOs to address potentially significant changes in their model for delivering technical solutions to their internal and external customers. Cloud computing has enabled a much shorter time-to-market for new products and services while potentially affording more highly scalable and easier-to-manage operations. This type of computing enabled CIOs to more rapidly adjust the firm’s IT resources (i.e. applications, data, servers, storage, and networking) to meet fluctuating and unpredictable business demands. CIOs additionally shared the need for increased understanding of how new technologies affected their IT portfolio management, including applications, infrastructure, architecture, and security risks. Although many available technologies in the industry provided desirable benefits for the organization, CIOs told us that the ability to determine how new technologies contributed to short and long-term realization of goals and objectives and how they support the strategic vision of the company was a critical skill that all CIOs need to possess.

Journal of Applied Business and Economics vol. 16(5) 2014 37 Change Management Over 98 percent of CIOs who participated in this study identified change management as an essential competency that was significantly underdeveloped in many technology executives. CIOs attributed this gap to their lack of hierarchical authority to enact or mandate technological and business process change within or across other business units or throughout the enterprise. We learned that leading process change and change management was a significant challenge due to deep-rooted traditions, regulations, and sometimes contracted or unionized labor. CIOs indicated that the skill of collaboration (internally and externally) was critical to success, as was the ability to create incentives within a frequently scrutinized and often constrained environment. Several CIOs indicated that their ability to create clear incentive structures, provide oversight, and demonstrate a willingness to manage risk acceptable levels of risk were critical to their success. In the area of change management specifically, CIOs emphasized the need for strong communication skills, a record of bringing diverse groups together from within the organization and external to it, business analysis skills, and strong negotiating skills.

Business Analysis Trumps Technical Skills Ninety percent of the CIOs involved in our study mentioned that the CIO role used to be highly technical. However, in the New Normal, the role required strong business analysis skills. They indicated that providing clarity of vision as a leader and delivering high quality products and services tied to business strategy and metrics were additional critical success factors. One way that these CIOs demonstrated these skills was through thoroughly understanding and articulating how technology was used to improve or to bring innovation to the firm’s business operations. These technology executives shared that a fundamental expectation of the CIO role was to understand key business performance measures that drove the organization’s performance and to use technology to support and to improve business operations. CIOs needed both knowledge of the business-line and the IT that supported the achievement of mission. This often entailed having a line-of-site to the transactional level of the organization and a concept of the customer’s definition of “value” or measure of success. The ability to identify and solve business problems was clearly essential to the CIOs interviewed for this study.

Key CIO Competency #4: Relationship Management and Communication CIOs indicated that one of their key roles was to inspire and build trust throughout their organizations or agencies. They discussed the need to be able to demonstrate superior relationship management and influence skills within and across all levels of the organization as well as with external stakeholders. The ability to exercise a powerful and transparent two-way communication channel on the business issues within the C-suite and throughout the organization was considered key. CIOs also reported that the skills to clearly communicate the business value of technology to across all levels of business management and operations were additional critical success factors.

TABLE 7 RELATIONSHIP MANAGEMENT AND COMMUNICATION BEST PRACTICES

Relationship Management and Communication Best Practices:

 Ensure effective exchange of information regarding current state and future directions with business peers  Develop business capabilities within the IT team  Know the critical competencies for the team’s success  Link mission, not adjustable metrics, to team’s performance

38 Journal of Applied Business and Economics vol. 16(5) 2014 Managing Horizontally as Well as Vertically CIOs expressed the need to be able to “manage horizontally and vertically.” We learned that this skill set was particularly important in leading and managing through the adaptive changes in enterprise structure in the New Normal, including downsizing, outsourcing, and the increased adoption of cloud technologies. Here the CIO took the leadership role by filtering out noise related to technology hype that comes from both external and internal sources while simultaneously championing new value creation through innovation that positively impacted the accomplishment of strategic goals and objectives. Often the negotiation process with senior executives was as complex as negotiating with external partners and stakeholders. Multiple iterations of obtaining buy-in from fellow executives were often required. Throughout the technology adoption and implementation process, information on the project needed to be communicated with transparency, frequency, and in language that other partners and stakeholders could understand.

Seeing Things from the Customer’s (or Citizen’s) Perspective CIOs indicated that they needed to understand, to influence, and be influenced by the needs and directions of their external environments. They shared that a key responsibility of CIOs was to identify and create value from the perspective of the firm’s external environment. We learned that several CIOs adopted a customer-centric approach, which meant that they needed to identify problems and provide solutions for rapid problem resolution and adding value from the customer’s perspective. This involved identifying, often through observation, what was valuable to the customer and changing processes in order to maximize value. For the private sector this meant that they identified potential problems early and, at times, contacted the customer after seeing complaints in social media before the complaints were raised to the company. It also meant completely considering the value propositions for customers in terms of time, convenience, and choice. For the US city county CIOs we spoke with, it meant that many of the community problems that were identified by the agency were situated close to the citizen’s home (e.g. potholes, street lighting, public safety, transportation, housing, etc.). In this context, customer-centricity was referred to as the “Front Porch View.” For Taiwan’s national government, it meant setting up a public webpage that enabled citizens to share ideas with the CIO’s office about how to improve the nation’s technology infrastructure on behalf of the needs of the citizens. For the private sector CIOs, it meant having their IT staff accompany business managers in their jobs to better understand opportunities and challenges and to determine how the IT department could leverage technology to create value-added solutions. From this perspective, internal and external stakeholders were better informed about projects that immediately made a positive impact on the organization. CIOs that followed this management approach aligned the internal resources and processes of the enterprise with the resources of their external environment in order to most effectively meet or exceed business and technology goals and objectives.

Key CIO Competency #5: Business Management A key dimension of the Business Management competency was the effective establishment and management of human capital. At the top of the list of organizational and talent management skills was the CIO’s ability to prepare the next generation of IT professionals. Public-sector CIOs have a clear vision of where they need to take their agencies, but one of the biggest challenges was to acquire, train, and transform the human capital necessary to successfully execute projects in the New Normal. We found that over 93% of the CIOs could identify the technical competencies and skills and human capital needed to establish a world-class IT organization. But within public agencies there was oftentimes no formal process for developing the skills of IT professionals. This resulted in public agency employees who have legacy skills and were unable to keep up with the technology demands of today’s fast-paced and rapidly changing environment. Legal constraints imposed by unions were also deemed to have adversely impacted talent management programs. Additional challenges reported by public sector CIOs included building coalitions within their departments and externally across other divisions, steering committees, and supply chain partners.

Journal of Applied Business and Economics vol. 16(5) 2014 39 TABLE 8 BUSINESS MANAGEMENT BEST PRACTICES

Business Management Best Practices:

 Develop business and IT capabilities of the team  Know the critical competencies required for the team’s success  Link mission, not adjustable metrics, to team’s performance  Establish a plan to acquire and grow talent  Develop and incorporate appropriate incentives and disincentives  Make recognition visible for high performing team members  Familiarize yourself with the basic finance principals and processes  Position your firm’s governance model as it relates to cost-effectiveness, asset utilization, business growth, & business flexibility  Establish IT governance mechanisms around decision making structures, alignment processes and formal communication  Understand how your governance policies align with those of your strategic partners  Strive to establish your IT governance policies on one page  Revisit and redesign your IT governance policies and procedures on a regular basis

We learned from the CIOs involved with our study that business management entailed the development and implementation of an effective strategic governance model. They needed to clearly define and measure the return on technology investments in the context of how the enterprise defined value. We also learned that effective CIOs’ took their existing business management skills and used them to influence external strategic partnerships and alliances so that their efforts were also aligned with the vision and strategic goals of their firm.

Demonstrating Sound IT Governance and Business Management CIOs identified the need to be leaders who identified and implemented sound strategic IT governance models that influenced and affected many parts of the business. They indicated that their organizations typically realized value throughout the business operations immediately following the implementation process. CIOs indicated that in order to demonstrate credibility, they needed to demonstrate and display a sound command of corporate finance principles and a basic understanding of financial analysis so that they could evaluate and justify the business rationale for major IT initiatives. These CIOs shared the need to collaborate with their management teams in ways that transcended the traditional IT cost center management span of influence. In this context, CIOs needed to be able to demonstrate business value creation in terms of both revenue generation and cost-reduction.

Balancing the Big “P” and the Little “p” (Power and Persuasion) CIOs strove to be trusted advisors and information stewards within their organizations. They were required to play an active contributing role in the overall strategy formulation and execution. These executives needed to provide a clear vision of how IT helped to drive the business forward. Over 97% of the CIOs surveyed believed that a key competency required was the ability to lead and influence others without any formal authority. These CIOs revealed that in order to be successful in their jobs, they needed to maintain strong relationships with executive colleagues from other agencies and lead by persuasion rather than relying on the power associated with their positions. The competency of exercising power and persuasion was especially important when CIOs needed to negotiate for budgets during the appropriation process. CIOs needed to convince leaders and colleagues of the value that their organization may bring

40 Journal of Applied Business and Economics vol. 16(5) 2014 with the use of technology. Much of the persuasive power came through an acute understanding of the appropriations process and by proposing how the technology added value to business operations.

Exercising and Demonstrating Affordability Largely precipitated by the economic crisis, CEOs have tasked CIOs to take on additional leadership roles by leveraging the IT portfolio to deliver better, faster, cheaper (and give it to me now) technology solutions that make doing business more affordable. From a financial perspective, sound business management encapsulated the ability to demonstrate return on equity, return on capital, and return on assets. CIOs expressed the need to display a sound command of corporate finance principles and to conduct basic financial analysis as related to IT investments including a demonstrated understanding of how technology enhanced business operations. From a customer focus and revenue generating perspective, sound business management included the ability to innovate and to derive revenue from new products and services and the ability to increase market share by expanding the existing customer base. From a back office perspective, sound business management means the ability to improve operations through better asset utilization (efficiency), more accurate planning and forecasting (effectiveness), and on-time delivery of products and services (quality).

Determining the Longevity of an IT Implementation: “Red Light, Green Light, 1-2-3” The average tenure of a public sector CIO is about 6.3 years. Given the high rate of turnover of these technology executives, CIOs needed to accurately select the appropriate projects that could have been completed during their tenure. Red Light, Green Light, 1-2-3 was a concept used by a finance department CIO in New York. It referred to the scorecards used to measure employee performance and project success. It also pertained to the constant stopping (Red Light) and starting (Green Light) of IT project implementations due to electoral cycles or budget constraints. CIOs in both public and private sectors needed to demonstrate that they were able to manage implementations amidst ongoing executive changes in their organizations. They were required to demonstrate leadership by effectively prioritizing the projects to be budgeted and resourced. One executive noted that without this competency, decision-making and long-term planning suffered because executives did not want to put the red light on a project and be held accountable for the losses attributed to sunk investments. Performance was typically evaluated according to the accuracy of the scorecard and not on project success. Similarly, CIOs avoided starting projects that could not be completed during their tenure. The ability to see long-term success beyond the political headwinds and turbulence was therefore deemed essential.

Key CIO Competency #6: Risk Management CIOs expressed a strong need to be able to communicate effectively about risk and risk tolerance as it related to internal and external threats and to adopting and deploying new technologies. A comprehensive Risk Management agenda required CIOs to understand the broader elements of risk – technology, operations, and enterprise risk. It also entailed guiding the enterprise to understand and define its risk appetite and the levels of risk that were acceptable to the firm, including balancing the cost of risk avoidance with the numerous opportunities for growth.

Journal of Applied Business and Economics vol. 16(5) 2014 41 TABLE 9 RISK MANAGEMENT BEST PRACTICES

Risk Management Best Practices:

 Create a culture of risk awareness  Understand the levels of risk that the firm is willing to accept  Establish an operational risk taxonomy  Proactively engage in regular and consistent communication about risk with your colleagues  Establish a contingency plan for risk management  Consistently monitor internal and external points of compromising exposure  Regularly revisit the established rules established by the firm  Educate your colleagues on the systemic impacts of risk exposure

CIOs have not typically been granted the authority (or resources) to manage enterprise risk. Traditionally, the technology executive managed the risk associated with adopting new technology or securing corporate data. We learned through our study that, in the New Normal, Boards of Directors and CEOs more commonly relied on the CIO to define and to manage the elements of internal and external risk at an enterprise level. CIOs increasingly led the enterprise risk management function in a manner that created business opportunity, well beyond the more traditional function of asset protection. CIOs established an integrated systemic risk management protocol that were supported by end-to-end processes and tools that enabled their enterprises to manage and balance opportunity with safety and soundness. A CIO of a leading ERP and cloud computing service provider in Hong Kong understood the need to allow employees to use their personal mobile phones to engage in constant and immediate communication with their external customers and the competitive advantage that leveraging these technologies enabled. He also saw the risks to the security of enterprise information posed by employee use of personal technologies and implemented the appropriate controls to balance the risks with the opportunities.

Risk Management The consumerization of IT, where employees used their personal technologies, e.g. smartphones, laptops, tablets, etc., for both business and personal purposes, was increasingly prevalent. Employees who used personal devices that were provisioned with personal applications and service providers posed potentially significant risks to enterprise infrastructures, operating environments and data. These technologies were, at the same time, seen in some organizations as an effective opportunity to provide employees with access to real-time data and extended communities of professional peers that enhance understanding, insight and decision-making. CIOs who operated in the New Normal identified the need to establish a more consistent approach to managing risk, and to create a culture that was risk aware versus risk averse. CIOs needed to have the skills to identify, understand and address factors that contributed to risk associated with technology, operations, and information at an enterprise level in a seamlessly integrated manner. They needed to be able to effectively communicate risk and to transparently discuss risk tolerance with their executive colleagues. Several US Federal government CIOs surveyed for this study indicated a tendency to be more risk averse than their private sector counterparts. Electoral cycles and budget constraints played an integral role in driving a more conservative approach to risk management for these technology executives.

42 Journal of Applied Business and Economics vol. 16(5) 2014 Reducing Corporate Risk and Security Exposure Associated with IT governance, the ability to reduce corporate risk and minimize security exposure was identified as another key skill needed by business-savvy CIOs. New and innovative uses of technologies such as social networking, wikis, and blogs have provided new capabilities for organizations to capture and to disseminate information, but they have also created new security risks that organizations must address and manage. Interestingly, we heard from several CIOs in our study that internal security breaches, even when unintended and/or not intentionally malicious, represented a meaningful percentage of all identified incidents. Increasingly, CEOs required CIOs to incorporate social networking into the firm’s daily activities. More than half the CIOs that we spoke to had been actively involved in designing and deploying strategies and policies for enterprise use of social media and social networking. CIOs indicated that there were many points of failure throughout the process through which data was shared across the organization, and that they need to understand every step in the process to identify, evaluate and remediate as appropriate, potential points of failure and risk.

Leading Enterprise Development of a Risk Management Culture One of the major challenges that CIOs face today was to design, build, and to embed a culture of active risk management across the organization. Because enterprise risk management, by its nature, spanned multiple lines of business and functions at every level of organization, CIOs needed to lead this effort in areas where they have both explicit authority and control, as well as in areas where they need to leverage their persuasion skills to achieve acceptance and participation in critical enterprise risk management initiatives.

CONCLUSION

Given the introduction of new business models and rapidly advancing technologies, CIOs were forced to adapt. Consequently, their role and the competencies needed to be successful have changed with the New Normal. Our research identified and described six key competencies that effective IT leaders possessed. These competencies spanned industries, countries and across public and private sector enterprises. Numerous other key competencies were identified by CIOs, but these others were not yet consistently practiced across all boundaries. For this reason, we believe businesses have only begun to understand the value that CIOs have brought to the organization. As the understanding and momentum builds, it is likely that new and innovative CIO competencies will surface. It is our hope that, by identifying and describing these competencies, we can assist organizations in developing CIOs who will copilot successful enterprises in both the public and private sectors operating within the New Normal. We asked CIOs to provide us with strategies that IT executives should consider as they develop competencies and evolve their role as business-savvy executives. We end this article by providing the top six strategies identified by technology executives who participated in our research for building the critical competencies that enable CIOs to lead their enterprises in leveraging IT to drive innovation and generate sustainable business value.

Six Strategies for Becoming a Business-Savvy CIO

1. Focus on the data, not the applications that delivers it. Being a successful CIO is about focusing on the data, not just the development and delivery of applications. The ability to define data, develop data models, and rationalize data structures across horizontal processes and boundaries was paramount for p CIOs. Building competencies in this area required both technical skills and specific understanding of the data from the perspective of customers and ultimate beneficiaries. If CIOs do this well, there is the potential to add significant value to the organization by connecting the value of the data directly to the value of products and services to customers. Questions that CIOs asked that they deemed useful in understanding how to provide value were: What frameworks and metrics apply to measuring customer value? How can we better anticipate

Journal of Applied Business and Economics vol. 16(5) 2014 43 customer needs? Have we established a process for setting priorities? What customer-facing systems can help customers understand trade-offs and increases the control that they have over data?

2. Distinguish among compliance versus regulations versus legacy constrictions. Among CIOs, these three areas of focus tended to get muddled into a single category, impacting the CIOs’ ability to focus on the data. Successful CIOs built this competency in a number of ways, primarily through formal and informal meetings with their peers in other agencies. In this context, helpful questions CIOs asked themselves were: How do we distinguish between data usage and data collection? How do we communicate compliance and related costs? How are trade-offs calculated and communicated? How will the data be used? What are the constraints on interpretation of the data? Who defines and manages data definition and stewardship?

3. Spend the first 90 days understanding the landscape. One of the first things CIOs new to their positions needed to do was understand the business model down to the transactional levels of detail. CIOs needed to understand the fundamentals of the business and determine how to focus the organization’s resources available and efforts underway. Also included in this 90-day process was building rapport with colleagues in other divisions and agencies. Questions that CIOs asked in order to build this competency were: What is the project portfolio and how do I evaluate the current status? Do I have monthly “health checks” of my portfolio? What are the upcoming milestones? Are there any “hot buttons” that I need to be aware?

4. Establish communities of interest to learn from and to measure progress. CIOs reported that the best approach to learn about the most effective management techniques and technology adoption methodologies was through discussions with colleagues at other organizations and agencies. They established informal conferences or technical forums where colleagues demonstrated the latest tools and discussed the implications, consequences, and benefits of strategies and tactics. Successful CIOs also engaged in benchmarks of their own organization’s efforts compared with other firms to identify areas of strength, weakness and opportunity. Questions CIOs asked to help them establish communities were: Who would it be helpful to network with, both in the public and private sectors, given the strategy and direction of the organization? What organizations should be included in benchmarking? What groups are currently available to help me succeed?

5. Understand how social networking technologies can aid or hinder your organizations and agencies. The use of social networking technologies has been challenging for some organizations because it requires the integration of multiple automated and manual processes across multiple lines of business and functions. Questions that CIOs could ask themselves include: What forward-facing service areas exist that are congruent with the social networking model? What companies or divisions are using this technology, and how is it working? How can I engage constituents in providing services, and even prioritization?

6. Build Bridges. It was important for public sector CIOs to build rapport and establish relationships with the executives from other agencies. Oftentimes these agencies provided ideas or resources for colleagues to adopt and leverage within their own environments. Without establishing relationships with other agencies, CIOs would spend most of their time attempting to integrate silo and horizontal business processes within and across other agencies without the critical knowledge required to do so effectively. Questions CIOs asked their agency personnel as well as other public sector CIOs were: How do we integrate the horizontal and vertical processes within our agency? What are the benefits and challenges associated with our current constraints? How can we leverage our current constraints? How can we create partnerships to facilitate span of influence and control? How can we help each other achieve success?

44 Journal of Applied Business and Economics vol. 16(5) 2014 METHODOLOGY

This research utilized data collected from two sources of research collected by the authors. In the first study, the Center for CIO Leadership sent a confidential online survey to 2,421 CIOs worldwide in July 2011. The study was promoted via Twitter, the Center’s LinkedIn Group, and Center partners and yielded 338 responses. The objective of the study was to understand the status of the global CIO profession within six specific best-practice competencies that the Center for CIO Leadership and its academic partners believed were imperative competencies for technology executives [Center for CIO Leadership, 2012]. The original survey instrument [Figure 4] was developed by the Center for CIO Leadership in collaboration with Dr. Lynda Applegate of Harvard Business School and Dr. Soumitra Dutta of INSEAD. Some of the questions in the survey were sourced from The New CIO Leader Self-Assessment in The New CIO Leader: Setting the Agenda and Delivering Results, authored by Dr. Mariane Broadbent and Elen S. Kitzis. The data used in this manuscript was developed based on an updated survey (2011) in collaboration with Dr. Marianne Broadbent (EWK International), Dr. Joe Peppard (Cranfield’s Information Systems Research Center (ISRC)), and George Westerman (MIT Sloan’s Center for Digital Business), and additional review from Dr. Haim Mendelson (Stanford University).

Journal of Applied Business and Economics vol. 16(5) 2014 45 FIGURE 4 ORIGINAL CIO SURVEY INSTRUMENT

46 Journal of Applied Business and Economics vol. 16(5) 2014

Journal of Applied Business and Economics vol. 16(5) 2014 47

48 Journal of Applied Business and Economics vol. 16(5) 2014

Journal of Applied Business and Economics vol. 16(5) 2014 49 The authors of this paper also conducted 75 personal semi-structured interviews and aggregated the data into a second dataset. This dataset was integrated with the findings of the CIO Leadership Survey whitepaper to write this paper. Personal interviews were conducted with CIOs from both the private and public (national, state, and county government) sectors across the United States and internationally (e.g., China, Japan, Australia, India, New Zealand). The key research questions for the study were: What are the roles & responsibilities of CIOs? How have these roles & responsibilities changed over the last 10 years? What are the attributes and characteristics of a successful CIO? Each interview lasted approximately 60 minutes.

REFERENCES

Chun, M. and J. Mooney (2009). “CIO Roles and Responsibilities: Twenty-Five Years of Evolution and Change.” Journal of Information and Management, 46, no. 6: 323-334. doi:10.1016/j.im.2009.05.005. Feeny, D. F. and L. P. Willcocks (1998). “Core IS Capabilities for Exploiting Information Technology.” Sloan Management Review, 39, no. 3 9-21. IBM Whitepaper (2012). Leading Through Connections, Insights From the Global Chief Executive Officer Study. Kindness, A. (2012). Understanding The Network Skills Gap, Forrester Research Inc., February 14. Leidner, D. and J. Mackay (2007). “How Incoming CIOs Transition Into Their New Jobs.” MIS Quarterly Executive, 6 (1), pp. 7–28. Preston, D., D. Leidner, and D. Chen (2008). “CIO Leadership Profiles: Implications of Matching CIO Authority and Leadership Capability On IT Impact.” MIS Quarterly Executive, 7(2), pp. 57–69. The Center for CIO Leadership (2010). Beyond the Crossroads: How Business-Savvy CIOs Enable Top- Performing Enterprises & How Top-Performing Enterprises Leverage Business-Savvy CIOs, whitepaper, February. The Center for CIO Leadership (2012). “A Roadmap For CIO Business Leadership: Analysis and Recommendations from the 2011 Center for CIO Leadership Survey,” whitepaper, May.

ACKNOWLEDGEMENTS

This research was funded and made possible by Pepperdine University’s Denney Academic Chair Scholarship. The authors would like to also thank and acknowledge The Center for CIO Leadership for this research collaboration.

50 Journal of Applied Business and Economics vol. 16(5) 2014

Country Risk and Macroeconomic Factors: Evidence from Asian Markets

Rahul Verma University of Houston-Downtown

Priti Verma Texas A&M University-Kingsville

INTRODUCTION

International investors invest in emerging markets quite differently than they do in developed markets. The international investors perspective on country risk which is often used in conjunction with cross border investments is therefore quite different in developed versus emerging markets. International investors react to both global and local economic conditions while infusing capital in emerging markets when it is hot and offload local currency based holdings when the global and/or local economy slows down. The central banks of these countries get hard-pressed to prop up a large amount of devalued local currency based securities which such outflows leaves behind. This phenomenon is in contrast to the developed markets (like the U.S., United Kingdom, Japan), where investors react mainly to the local economic conditions and are more willing to hold on to the local currency denominated securities even in case of global slowdown.

Journal of Applied Business and Economics vol. 16(5) 2014 51 The country risk for a given economy is the unique risk faced by foreign investors when investing in that country. For developed countries it is convenient to measure country risk by credit ratings. However, more refined measures are needed for emerging markets which are structurally different and where international investors respond dramatically to both local and global factors (Gangemi et al., 2000; Verma and Soydemir, 2006). For example, in case of emerging markets, during slowdown, an increase in the local interest rate which is supposed to entice investors and therefore reduce country risk (commonly seen in developed markets) becomes a double edged sword as it can also take a toll on the economic growth. Such simultaneous positive and negative effects can lead to increase or decrease in country risk. Harvey (1991) suggested the country beta approach to model country risk. Under this approach the country risk is measured as the conditional sensitivity (or covariance) of country returns to the world stock returns. Accordingly, Harvey and Zhou (1993) estimate country betas for seventeen developed countries conditional on the effect of a weighted world market portfolio. Similarly, Erb at al. (1996b) estimate country betas for twenty-one developed and twenty-six emerging equity markets as a function of country credit risk. Gangemi et al. (2000) estimate Australia’s country beta conditional on Australian macroeconomic variables by using a country beta approach. On similar lines Verma and Soydemir (2006) examined country risk of four Latin American countries. In the light of these theoretical, empirical findings and recent global developments it can be postulated that the modeling of country risk in Asian emerging markets is quite different from those used in developed countries. However, it is important to realize that it would be unfair to lump all emerging markets into one basket. Findings for Latin American may or may not corroborate for Asian markets. Even there exist differences among countries of the same region. Many of the emerging markets have sound macroeconomic, financial, and policy fundamentals; some of the medium-term fundamentals for most emerging markets, including the fragile ones, remain strong: urbanization, industrialization, catch- up growth from low per capita income, a demographic dividend, the emergence of a more stable middle class, the rise of a consumer society, and the opportunities for faster output gains once structural reforms are implemented. Therefore, a differentiation is needed while investigating the determinants of country risk in Asian markets. This study extends prior research by investigating whether global and local risk factors have any varying degrees of influence on country betas in five Asian markets: Hong Kong, Indonesia, Malaysia, Philippines and Singapore. This study contributes to the extant literature in the following distinct ways: first, unlike previous studies, which examine the effect of global and local factors on Asian stock returns, we investigate their effects on their time varying country risk. Second, we examine the determinants of time variations in country risk in Asia. Thirdly, we apply the country beta approach to a set of Asian countries, which have not been done in earlier studies. We employ a two-step process to examine the postulated relationships: we first estimate country beta models for Hong Kong, Indonesia, Malaysia, Philippines and Singapore and generate separate series of country risk variables for each market; in the second step we analyze the response of these country risks to five local factors and seven global factors. The local factors are: money supply, inflation, economic growth, interest rate and exchange rate while the international factors are: value of U.S. dollar against currencies of 15 industrialized countries, spread between 90-day Euro dollar deposit rate and 90 day U.S. Treasury Bill yield, weighted average inflation of G-7 countries, weighted average short term interest rates of G-7 countries, U.S. dollar price per barrel of crude oil, U.S. interest rate and U.S. inflation. The estimations results indicate strong and significant effects of the global risk factors on country risk of all these Asian markets. The price of dollar has significant positive effects in all except in the case of Malaysia’s country risk. In addition, the dollar euro spread, real interest rates and inflation of G-7 countries have a significant negative impact on country beta in all the cases. On the other hand, exchange rate (in case of Malaysia and Singapore) and to some extent money supply (only in case of Hong Kong) are the only local factors, which have a significant effect on country risk of these markets. Our results are consistent with previous findings that sensitivity to global risk factors increases as the markets become more integrated.

52 Journal of Applied Business and Economics vol. 16(5) 2014 The remainder of this paper is structured as follows: Section 2 reviews the previous literature while section 3 presents the model specification. Section 4 describes the data and the econometric methodology and section 5 reports empirical results. Section 6 concludes.

LITERATURE REVIEW

There are a variety of factors that potentially influence time varying country risk. Oetzel et al. (2000) suggest that several economic factors impact country risk and therefore, relate country risk to national macroeconomic policies. A sound monetary policy with low inflation and unemployment rates contribute to lowering country risk. When a country’s economic conditions become unstable, country risk may increase. They find the currency risk as an important element associated with country risk. Erb et al. (1996a) address the economic content of five different measures of country risk for 117 countries (political, financial, economic, composite risk indexes and country credit ratings) over the period 1984 – 1995. Their results suggest that the country risk measures are correlated with future equity returns. However, a major limitation of their study is the inability to capture the nature of country risk and its potential impact on global investment strategies in emerging markets where risk information may be limited (Gangemi et al. 2000). Erb et al. (1996b) extend this previous analysis and model country risk as a function of country’s credit rating over the period 1979 - 1995 for 21 developed and 26 emerging markets. They find those factors that simultaneously influence a country’s credit rating are mainly political risk, inflation, exchange rate variability and control, industrial portfolio, economic viability, and sensitivity to global economic shocks. Abell and Krueger (1989) examine the influence of the U.S. macroeconomic variables on the country beta by allowing it to vary with a set of macroeconomic variables. Specifically, their variable set includes budget deficit, six months commercial paper rate, consumer price index, AAA corporate bond yield, crude oil price index, exchange rate, federal funds rate, M1 money supply, merchandise trade balance, and unemployment rate. Their results show that beta estimates are sensitive to a number of macroeconomic variables. In a similar analysis Gangemi et al. (2000) investigate a set of economic variables to examine the effect of foreign debt on Australia’s country risk. The variable set includes Australia’s government net overseas borrowing, 90-day bank accepted bill rate, ten year Treasury bill rate, wool price, trade weighted index, manufacturer’s price index, retail trade, balance on current account and Australian money supply. They find that the exchange rates are the only macroeconomic factor that has a significant impact on Australia’s country beta. Following same approach Verma and Soydemir (2006) find that both local and global factors have relatively different impacts on the country risk of Mexico, Brazil, Argentina and Chile. The real interest rates and inflation rates of G-7 countries have a statistically significant negative impact on country beta (the highest effect is on Mexico followed by Brazil and Chile). Among the local factors, money supply and exchange rates have a statistically significant effect on country risk. The effect of money supply is most significant in the case of Mexico followed by Chile and Brazil. Using a semi–parametric approach, Jeon (2001) explores the influence of macroeconomic influences on country risks for 14 developed countries. The variable set includes the U.S. term premium, the U.S. default premium, trade weighted exchange rate, consumer price index, money market rate and industrial production index. The results suggest that for the majority of countries consumer price index, industrial production index, and exchange rates have a significant impact on country betas. The previous literature has well documented the impact of macroeconomic variables on betas for developed countries. However, such causal relationships have been little questioned in Asian markets. The set of macroeconomic variables relevant in the context of developed countries might be different than those of emerging markets. This study is an extension of the current literature in that it investigates the postulated relationship between economic forces and country risk in the Asian markets by using the

Journal of Applied Business and Economics vol. 16(5) 2014 53 country beta approach. Our choice of macroeconomic variables is based on the studies that deal exclusively with emerging markets equity returns.

MODEL SPECIFICATION

This study employs the approach suggested by Gangemi et al. (2000) which modeled Australia’s country risk and Verma and Soydemir (2006) which performed similar analysis for Latin American countries. The country risk under this approach is modeled in four steps. In the first step the international version of the Capital Asset Pricing Model (Sharpe, 1964; Lintner, 1965) is estimated. This model predicts that the expected return on any traded asset in excess of a risk free return is proportional to the systematic risk of the asset as measured by its covariance with the a market wide portfolio return. Accordingly, the following time varying standard country beta model for the purpose of measuring country risk is estimated:

RRjt−= ftβε jt() RR wt − ft + t (1) th where Rjt is the return on j country’s stock market index, Rft is return on a risk free asset, βjt is the parameter; Rwt is the return on global stock market index; εt is the random disturbance term. Specifically th βjt is a measure of relative risk, which is determined by a combination of j country’s economic, financial, political factors, world market conditions and sensitivities of the country’s market to the world market conditions at a particular time ‘t’1. In the second step, based on the arguments of Fama and French (1989), Mcqueen and Roley (1993), and Ferson and Harvey (1991) a time varying country beta model is estimated. Specifically, these studies suggest that equity returns are highly correlated with the business cycle through a variety of macroeconomic influences and that beta risk is time varying in nature as a result of business cycle. Accordingly, this study specified the following country risk model: N (2) βt =++ b0 ∑ bEi it u t i=1 where b0 and bi are the parameters; Eit is a set of local and global factors affecting beta at time ‘t’; ut being the independent and identically distributed random disturbance. In the third step, we specific the local and global factors that can affect country risk of the Asian markets. We identify a set of local and global factors based on the findings that domestic money supply, goods prices, real activity, production rates, productivity, gross national product growth rate, unemployment, yield spread, interest rates, inflation, dividend yields and other local factors and exchange rates are significant in their association with emerging equity returns (Fama, 1970; Chen et al. 1986; Jorian 1991; Groenewold and Fraser, 1997; Ely and Robinson 1997; Kwon and Shin 1999; Serra 2000Bilson et al., 2001; Ferson and Harvey, 1994). Accordingly based on the previous studies, we select five local factors and seven global factors which can have significant effect on country beta of Asian markets. The local factors are: (i) money supply (M1), (ii) goods prices (CPI), (iii) real activity (IIP), (iv) interest rate (IR) and (v) exchange rates (XR). The global risk factors are: (i) foreign exchange value of the U.S. dollar against the price of the currencies of 15 industrialized countries (Dollar), (ii) Spread between 90-day Euro dollar deposit rate and 90 day U.S. Treasury Bill yield (Euro$) (iii) weighted average inflation of G-7 countries (G7_INF) (iv) weighted average short term interest rates of G-7 countries (G7_INT) (v) U.S. dollar price per barrel of crude oil (OIL) (vi) U.S. interest rate (US_INT) (vii) U.S. inflation (US_INF). When beta varies over time with a set of these factors, equation (2) can be specified as follows:

β =++b01 b M1$t b 2 CPI t + b 3 IIP tt ++ b 4 IR b 5 XR t + b 6 dollar t + b 7 Euro t t (3) +b8 G7_ INFt + b9 G 7_ INTt ++ b10 OIL t b11 US_ INTt + b12 US_ INFtt +υ However, since beta is not directly observable, one cannot estimate the time varying equation of beta in its present form. Therefore, equation (3) is substituted in time varying international CAPM model

54 Journal of Applied Business and Economics vol. 16(5) 2014 presented in equation (1) and in order to estimate the parameters of the model. Accordingly, in step 4, we generate the following specific time varying country beta model for each Asian country in the sample:

RRabRRMbRRCPIbRRIIPjt−=+ ft 12()1()()wt − ft t + wt − ft t + 3 wt − ft t

+−b45()()() Rwt R ftt IR +− b R wt R ft XR t + b 6 R wt − R ft dollar t + b 7 ()$ R wt − R ft Euro t (4) +−b8 ()7_()7_() Rwt R ft G INFt + b9 R wt − R ft G INTt + b10 Rwt − R ft OIL t

+−b11( Rwt R ft )_ US INTt + b12 (Rwt−+ R ft )_ US INFtυ t The step 4, allows us to indirectly estimate the values of parameters for equation (3) in terms of observable variables. A significant (insignificant) parameter b1 through b12 of this estimated equation would suggest a significant (insignificant) relationship between the local and global risk factors with country risk.

DATA AND ECONOMETRIC METHODOLOGY

This study employs country beta model to analyze the country risk of the following five Asian countries: Hong Kong, Indonesia, Malaysia, Philippines and Singapore. The sample period spans from January 1989 to January 2012 and data is obtained in monthly intervals from Datastream and Federal Reserve Bank of St. Louis. The stock market data is the local currency denominated stock indexes for each of the Asian countries in the sample. The exchange rates are the nominal values expressed as local currency per U.S. dollar. The proxy for interest rate (IR) is the individual middle rates for 30 days certificate of deposits for all these countries while money supply (M1) is measured as the narrow stock of money (M1). The goods prices (CPI) series are the domestic consumer price indexes for these countries and the real activity (IIP) series are the index for industrial production. The world market index is the MSCI World Index which is composed of stocks that broadly represents stock composition in the different countries. The premium on euro dollar deposit rates relative to the U.S. treasury rate (EURO$) is the spread between the 90-day Eurodollar deposit rate and the 90 day U.S. Treasury bill yield. The U.S. inflation (US_INF) is the percentage change in the CPI of the U.S. and the proxy for the U.S. interest rate (US_INT) is the 90 day U.S. Treasury Bill yield. The G-7 interest rate (G7_INT) is the weighted average of the short term real interest rate in G-7 countries using the shares of G-7 GDP as the weights less the G-7 inflation rates. Similarly, the G-7 inflation rate (G_7INF) is the weighted average of the percentage change in the CPI in the G-7 countries using the relative shares of the gross domestic product (GDP) as the weights. The proxy chosen for fluctuation in the U.S. dollar (DOLLAR) is the trade weighted foreign exchange value of the U.S. dollar against the price of the currencies of 15 industrialized countries. Lastly, the oil price variable (OIL) is the U.S. dollar price per barrel of crude oil and the data. Table 1 reports the descriptive statistics for most of the variables employed in this study2. The mean returns for all five Asian markets are higher than those of the world market return. Moreover, the standard deviations of stock returns for these Asian markets are higher than those of the world market suggesting that these emerging markets are highly volatile and investors are compensated for bearing risk. In comparison, all these markets have similar volatility but Hong Kong seems to have the highest mean return followed by Philippines while Indonesia and Singapore have similar returns and Malaysia being the lowest. The exchange rate for Hong Kong has zero mean and median value with an extremely low standard deviation. Hong Kong is one of the few economies that have adopted a fixed exchange rate and it is also one of a few to have maintained exchange rate stability effectively over a long period. On the other hand, the exchange rate of Indonesia displays the highest mean appreciation of approximately 1% with an extremely high standard deviation. This is followed by the mean appreciations in exchange rates for Philippines and Malaysia. As expected these appreciations are higher than those of the price of dollars. A possible reason for this could be a massive increase in supply of dollars due to quantitative easing

Journal of Applied Business and Economics vol. 16(5) 2014 55 program followed by the Federal Reserve. Similarly, the price of euro displays a negative mean growth during the sample period possibly due to recent crisis in the euro zone. The inflation and interest rates for both the U.S. and G7 countries display a small mean mainly due to the recent monetary policy measures taken by the developed countries.

TABLE 1 DESCRIPTIVE STATISTICS: MONTHLY CONTINUOUSLY COMPOUNDED RETURNS AND GROWTH RATES (IN DECIMALS)

The variables are stock market returns of Hong Kong, Indonesia, Malaysia, Philippines, Singapore, world (S_HK, S_IND, S_MAL, S_PHI, S_SING, W_EX), changes in exchange rates for Hong Kong, Indonesia, Malaysia, Philippines, Singapore (R_XRHK, R_XRIND, R_XRMAL, R_XRPHI, R_XR_SING), growth in price of dollar (R_DOLLAR), spread between euro dollar interest rate and U.S. interest rate (R_Euro$), weighted average inflation for G-7 countries (G7_INF), weighted average interest rate for G-7 countries (G7_INT), change in oil price (R_OIL), U.S. inflation (US_INF), and U.S. interest rate (US_INT).

Std. Mean Median Maximum Minimum Dev. Skewness Kurtosis S_HK 0.0088 0.0100 0.2645 -0.3482 0.0803 -0.1472 5.3482 S_IND 0.0044 0.0052 0.2502 -0.3786 0.0904 -0.6959 5.4215 S_MAL 0.0027 0.0031 0.2895 -0.2784 0.0892 0.0183 4.8655 S_PHI 0.0062 -0.0006 0.3317 -0.2989 0.0903 0.2764 4.9222 S_SING 0.0044 0.0057 0.2484 -0.2107 0.0712 -0.0497 4.7179 W_EX -0.0339 -0.0299 0.0716 -0.1935 0.0432 -0.4540 3.5450 R_XRHK 0.0000 0.0000 0.0040 -0.0077 0.0012 -2.2013 19.4320 R_XRIND 0.0095 0.0022 0.8802 -0.3337 0.0942 4.6878 47.5589 R_XRMAL 0.0021 0.0000 0.1076 -0.1392 0.0236 0.1335 17.6480 R_XRPHI 0.0044 0.0012 0.1382 -0.0860 0.0299 1.1865 8.3927 R_XRSING -0.0003 -0.0016 0.0561 -0.0598 0.0153 0.1561 5.8807 R_DOLLAR 0.0003 0.0005 0.0562 -0.0471 0.0150 -0.1658 4.1369 R_EURO$ -0.0007 -0.0025 0.1942 -0.1663 0.0481 0.6899 6.1423 R_G7INF -0.0002 -0.0002 0.0035 -0.0040 0.0015 -0.0702 2.9018 R_G7INT 0.0004 0.0011 0.0546 -0.0459 0.0149 -0.1727 4.0060 R_OIL -0.0007 -0.0007 0.1968 -0.2014 0.0718 -0.0488 2.8556 R_USINF -0.0002 -0.0002 0.0047 -0.0071 0.0020 -0.0458 3.5998 R_USINT 0.0000 0.0000 0.0066 -0.0051 0.0015 -0.0608 5.3765

ESTIMATION RESULTS

Before proceeding with the main results, we check the presence of multicollinearity by examining the correlations among the local and global risk factors. The correlations among the exchange rates of the Asian countries seem to be high suggesting these economies to be highly integrated. As expected the correlation between the U.S. and G-7 countries’ interest rates are high and so is the relationship between the oil prices with the U.S. inflation and price of dollars. The correlations between other variables are low suggesting that multicollinearity is not an issue. This suggests that each local and global macroeconomic factor chosen as a determinant of country risk represents the unique risk. Table 2 reports the correlation coefficients for Mexico, Brazil, Argentina and Chile under panels A through D respectively.

56 Journal of Applied Business and Economics vol. 16(5) 2014 TABLE 2 CROSS CORRELATIONS

The variables are changes in exchange rates for Hong Kong, Indonesia, Malaysia, Philippines, Singapore (R_XRHK, R_XRIND, R_XRMAL, R_XRPHI, R_XR_SING), growth in price of dollar (R_DOLLAR), spread between euro dollar interest rate and U.S. interest rate (R_Euro$), weighted average inflation for G-7 countries (G7_INF), weighted average interest rate for G-7 countries (G7_INT), change in oil price (R_OIL), U.S. inflation (US_INF), and U.S. interest rate (US_INT).

R_XRHK R_XRIND R_XRMAL R_XRPHI R_XRSING R_DOLLAR R_EURO$ R_G7INF R_G7INT R_OIL R_USINF R_USINT R_XRHK 1.00 R_XRIND -0.03 1.00 R_XRMAL 0.00 0.48 1.00 R_XRPHI 0.06 0.39 0.47 1.00

Journal R_XRSING 0.12 0.45 0.55 0.35 1.00 R_DOLLAR 0.07 0.14 0.25 0.19 0.52 1.00 of R_EURO$ -0.07 -0.09 -0.01 0.01 -0.09 -0.05 1.00 Applied R_G7INF 0.06 -0.01 -0.06 0.01 -0.02 0.10 -0.12 1.00 R_G7INT 0.07 0.14 0.26 0.19 0.53 1.00 -0.04 0.00 1.00

Business R_OIL 0.04 -0.04 -0.16 0.02 -0.13 -0.13 0.04 0.11 -0.15 1.00 R_USINF -0.14 0.04 -0.04 -0.05 -0.05 0.02 0.08 -0.04 0.01 0.34 1.00 R_USINT 0.05 -0.01 -0.01 -0.06 0.11 0.23 -0.09 0.04 0.24 0.04 -0.13 1.00 and Economics vol. 16(5) 2014 57 In accordance with equation (1) we first estimate the international version of the CAPM in which the excess return of Hong Kong, Indonesia, Malaysia, Philippines and Singapore are regressed against the excess return of the world market. We estimate this international CAPM five times for each of these markets. The coefficients (betas) for all these markets are positive and statistically significant suggesting that Asian markets are highly integrated with the world equity prices. In comparison, the betas for Hong Kong and Singapore are highest followed by Indonesia while Malaysia and Philippines have lower coefficients. The Durbin-Watson statistics is close to 2 in all the regressions suggesting that the equations are correctly specified. The R squares for all the equations suggest a strong role of the world market in Asian market movements.

TABLE 3 INTERNATIONAL CAPM ESTIMATES

Excess returns for Asian stock markets Hong Kong Indonesia Malaysia Philippines Singapore Excess returns of the MSCI world 1.0690*** 1.0371*** 1.0102*** 1.0152*** 1.0651*** market S.E 0.0911 0.1168 0.1154 0.1143 0.0800 t- statistics 11.7267 8.788 8.7487 8.8820 13.3081 R2 0.3773 0.2350 0.2090 0.2384 0.4097 LL 223.70 181.79 183.75 185.49 245.71 Durbin-Watson 1.9265 1.7823 1.8405 1.8212 1.8335

In the next step, following Fama and French (1989), Mcqueen and Roley (1993), and Ferson and Harvey (1991) we specify a time varying country beta model for each Asian markets based on equation (2). However, since beta is not directly observable, one cannot estimate the time varying equation of beta in its present form. Therefore, in accordance with equation (3) and (4) we estimate a set of time varying country beta model for each Asian country in the sample. This step allows us to indirectly estimate the values of parameters for equation (3) in terms of observable variables. A significant (insignificant) parameter b1 through b12 of this estimated equation would suggest a significant (insignificant) relationship between the local and global risk factors with country risk. Table 4 (column 2) reports the estimation results for Hong Kong’s time varying country beta. Consistent with Ferson and Harvey (1994), we find positive effects of the price of dollar while negative effects of the spread between the euro and the dollar, the U.S. interest rates, inflation for G-7 countries, interest rate for the G-7 countries, the U.S. interest rate and the U.S. inflation. Among the local factors, we find significant negative effect of money supply on country risk. This result is consistent with Bilson et al. (2001) who find that money supply is an important variable in emerging markets. These findings are also consistent with Verma and Soydemir (2006) which find similar results for Latin American countries. Overall, the result for Hong Kong suggests that the domestic money supply is an important source of local macroeconomic risk. Also, the world market, world inflation and interest rates are significant global factors affecting Hong Kong’s time varying country beta. Table 4 (column 3) reports the estimation results for Indonesia. Similar to Hong Kong, among the global factors, there is a significant positive effect of the price of dollar while negative effects of the spread between the euro and the dollar, G-7 inflation, G-7 real interest rate and the U.S. inflation on country risk. However, we do not find any significant results for the U.S. interest rate and the spread between the euro dollar and the U.S. interest rates. Moreover, unlike the case of Hong Kong, there is no effect of any domestic money supply on the time varying country risk.

58 Journal of Applied Business and Economics vol. 16(5) 2014 TABLE 4 REGRESSION RESULTS OF EXCESS RETURNS WITH INTERACTIVE FACTORS

The variables are excess return on the world market (W_EX), unanticipated inflation (R_CPI), unanticipated domestic interest rate (R_IR), unanticipated inflation for G-7 countries (R_G7INF), unanticipated real interest rate for G-7 countries (R_G7INT), unanticipated U.S. interest rate (R_USINT), U.S. inflation (R_USINF), unanticipated component of spread between Euro dollar interest rate and U.S. interest rate (R_Euro$), unanticipated growth rates in industrial production (R_IIP), unanticipated movement in exchange rate (R_XR), unanticipated changes in money supply (R_M1), unanticipated changes in oil price (R_OIL) and unanticipated movements in price of dollar (R_DOLLAR).

Excess returns for Asian stock markets Hong Kong Indonesia Malaysia Philippines Singapore C -0.0264*** -0.0319*** -0.0310*** -0.0278*** -0.0284*** W_EX*R_CPI 27.5770 0.0875 18.9923 10.9821 3.3145 W_EX*R_DOLLAR 681.6290** 677.3998** 320.3003 778.6439** 497.4569** W_EX*R_EURO$ -5.4704** -6.1407** -5.0237** -8.3762*** -3.8394** W_EX*R_G7INF -952.3195*** -574.0967* -540.5000* -931.1365*** -663.6727*** W_EX*R_G7INT -674.5046** -671.3668** -315.7427 -769.1219** -503.2011** W_EX*R_IIP 7.5762 -3.0359 1.6885 3.1656 4.4068 W_EX*R_IR -6.8333 -19.2463 14.1221 0.4191 -49.0300 W_EX*R_OIL -0.4631 -2.3707 -3.7837* -4.2279** -1.3381 W_EX*R_USINT -42.2457 13.8295 37.3101 88.6741 -38.2162 W_EX*R_USINF -315.8445*** -71.7329 -106.4554 -186.7503** -86.3472 W_EX*R_XR 273.0469 1.1178 22.1815*** 5.2991 27.3186*** W_EX*R_M1 -6.4211* 4.3029 1.1875 -2.8367 -3.6108 R-squared 0.1858 0.0966 0.2118 0.1609 0.2119 S.E. of regression 0.0771 0.0935 0.0854 0.0887 0.0685 Sum squared resid 0.9098 1.3375 1.1161 1.2031 0.7188 Log likelihood 196.5965 164.6162 179.6362 173.4019 216.1578 F-statistic 2.9102 1.3641 3.4269 2.4455 3.4285 Prob(F-statistic) 0.0012 0.1889 0.0002 0.0061 0.0002

Table 4 (column 4) reports the estimation results for Malaysia. Similar to the results from Hong Kong and Indonesia negative effects of the inflation rates from G-7 countries and the spread between the euro and the dollar. In addition, there is a negative relationship between oil prices and Malaysian country risk. However, we do not find the effects of other global factors including G-7 and the U.S. interest rates. Unlike Hong Kong and Indonesia we find a significant effect of the exchange rate on Malaysia’s country risk. Table 4 (column 5) reports the estimation results for Philippines’ time varying country beta. Similar to Hong Kong, we find positive effects of the price of dollar while negative effects of the spread between the euro and the dollar, the U.S. interest rates, inflation for G-7 countries, interest rate for the G-7 countries, the U.S. interest rate and the U.S. inflation. In addition similar to Malaysia, there is a negative

Journal of Applied Business and Economics vol. 16(5) 2014 59 effect of oil prices in this case. Similar to Indonesia, there is an insignificant effect of any local macroeconomic variable on the country risk. Table 4 (column 6) reports the estimation results for Singapore. The findings are similar to those of Indonesia and Philippines in that there is a significant positive effect of the price of dollar while negative effects of the spread between the euro and the dollar, G-7 inflation and G-7 real interest rate. However, there is no effect of the U.S. inflation on Singaporean country risk. Moreover, similar to Malaysia there is a significant effect of exchange rate on time varying country risk while insignificant effects of other local macroeconomic variables. Overall estimation results suggest that both local and global factors have relatively differing impacts on country risks of Asian markets. Among the global factors, the price of dollar has significant positive effects except in the case of Malaysia’s country risk. In addition, the dollar euro spread, real interest rates and inflation of G-7 countries have a significant negative impact on country beta in all the cases. On the other hand, exchange rate (in case of Malaysia and Singapore) and to some extent money supply (only in case of Hong Kong) are the only local factors, which have a significant effect on country risk of these markets. The significant effects of exchange rate movements on country betas in case of Malaysia and Singapore supports the findings of Oetzel et al. (2000), Gangemi et al. (2000) and Verma and Soydemir (2006) that currency risk is an important determinant of country risk in some emerging markets. Furthermore, the results indicate strong and significant effects of the global factors on country risk of all these Asian markets. This finding is in line with the arguments of Harvey (1995a, 1995b) and Bekaert and Harvey (1995) on market integration. The Asian markets are highly integrated with the world market as suggested by the international CAPM and our results are consistent with previous findings that sensitivity to global risk factors increases as the markets become more integrated. Interestingly, the world interest rate has a significant effect while the domestic interest rates have an insignificant effect in all cases. Perhaps because studies incorporating interest rates have found that it is not the interest rate itself that is relevant but the yield and the default spread that are more likely to influence equity returns (Chen et al. 1986). Further, Bilson et al. (2001) argue that in many emerging markets including there is not an active secondary market for bond issues and government paper which makes interest rates as an insignificant factor in financial markets. Our results are consistent with Gangemi et al. (2000) and Verma and Soydemir (2006) which find insignificant effect of interest rate on Australian and Latin American country betas respectively.

CONCLUSIONS

In this study, we employ country beta approach to investigate the response of country risk to local and global risk factors in case of five Asian markets: Hong Kong, Indonesia, Malaysia, Philippines and Singapore. In a two-step process, we first estimate country beta models for these markets and generate separate series of country risk variables for each market. In the second step we analyze the response of these country risks to five local factors (money supply, inflation, economic growth, interest rate and exchange rate) and seven global factors (value of U.S. dollar against currencies of 15 industrialized countries, spread between 90-day Euro dollar deposit rate and 90 day U.S. Treasury Bill yield, weighted average inflation of G-7 countries, weighted average short term interest rates of G-7 countries, U.S. dollar price per barrel of crude oil, U.S. interest rate and U.S. inflation). The estimations results indicate strong and significant effects of the global risk factors on country risk of all these Asian markets. The price of dollar has significant positive effects in all except in the case of Malaysia’s country risk. In addition, the dollar euro spread, real interest rates and inflation of G-7 countries have a significant negative impact on country beta in all the cases. On the other hand, exchange rate (in case of Malaysia and Singapore) and to some extent money supply (only in case of Hong Kong) are the only local factors, which have a significant effect on country risk of these markets. Our results are consistent with previous findings that sensitivity to global risk factors increases as the markets become more integrated.

60 Journal of Applied Business and Economics vol. 16(5) 2014 Our study has useful implications for both academicians and professionals. By identifying those variables affecting country betas and at varying degrees, international investors may be able to better hedge against the inherent risks stemming from a specific variable. From a policy perspective, a better understanding of such varying causal relationships can have important implications for correct monetary and fiscal policy designed to achieve stability in financial markets.

ENDNOTES

1. Harvey (1991) suggests this model be in the form of excess or unadjusted return over a risk free rate. Gangemi et al. (2000) confirms that the results are insensitive to the choice of the form of the model. However, to avoid the potential problem of misspecification, the model is expressed using excess returns. 2. Due to space constraint, some of the local variables’ descriptive statistics are not reported. However, they are available from authors on request.

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62 Journal of Applied Business and Economics vol. 16(5) 2014

The U.S.-China Trade Friction: Causes and Proposed Solutions

Suk Hi Kim University of Detroit Mercy

Mario Martin-Hermosillo University of Detroit Mercy

Junhua Jia Governors State University

INTRODUCTION

The government-directed capitalism of China for the past three decades has lifted over 400 million people out of poverty, made China the second-largest economy in the world, and caused China to become the third-largest trading nation in the world.1 China is the second-largest U.S. trading partner, its third- largest export market, and its biggest source of imports.2 However, some of the policies of China’s tightly managed capitalism, such as its currency manipulation, have created considerable friction with its trading partners, raising serious international concerns about growing current account imbalances, most notably with the United States (U.S.). In 2006, the Bank for International Settlements said that such huge imbalances could pose a serious problem in the long run for the world economy.3 In recent years, international organizations such as the International Monetary Fund and some countries, such as the U.S., have proposed numerous measures to correct them. With the world’s largest population and the world’s second-largest economy, China is a huge market for U.S. exports and investors. However, bilateral economic relations have become strained over a number of issues, such as large U.S. trade deficits with China, its currency manipulation, its poor record on enforcing intellectual property rights (IPR), its mixed record on implementing the World Trade Organization (WTO) commitments, and its extensive use of industrial policies.4 Another complication of the U.S.–China bilateral relationship has to do with the growing level of economic integration and mutual interdependence between the two economies. This article provides an overview of U.S.–China economic

Journal of Applied Business and Economics vol. 16(5) 2014 63 relations (trade and investment), surveys the trade disputes, and discusses measures to reduce the trade imbalances.

U.S.–China Economic Relations This section deals with the two types of closely related international transactions, foreign trade and investments. Tables 1 and 2 underscore a new reality in bilateral trade relations between the U.S. and China. As shown in Table 1, the U.S. trade deficit with China increased from $83 billion in 2001 to $273 billion in 2010. Table 2 shows that the U.S. current account deficit declined somewhat in dollar amounts in 2009 over 2008, but China’s account-balance surplus as a share of the U.S. account-balance deficit has consistently surged over time, even throughout the U.S.-originated global credit crisis of 2007–2009.

TABLE 1 U.S. TRADE IN GOODS WITH CHINA (U.S.$ BILLIONS)

Year Exports Imports Balance 2011 103.94 399.36 -295.42 2010 91.88 364.94 –273.06 2009 69.50 296.37 –226.87 2008 69.73 337.77 –268.04 2007 62.94 321.44 –258.51 2006 53.67 287.77 –234.10 2005 41.19 243.47 –202.28 2004 34.43 196.68 –162.25 2003 28.37 152.44 –124.07 2002 22.13 125.19 –103.06 2001 19.18 102.28 –83.10 2000 16.18 100.01 –83.83 1999 13.11 81.789 –68.68 1998 14.24 71.17 –56.93 1997 12.86 62.56 –49.69 1996 11.99 51.51 –39.52 1995 11.75 45.54 –33.79 1994 9.28 38.79 –29.51 1993 8.76 31.54 –22.78 1992 7.42 25.73 –18.31 1991 6.28 18.97 –12.69 1990 4.81 15.24 –10.43 1989 5.75 11.99 –6.23 1988 5.02 8.51 –3.45 1987 3.50 6.29 –2.80 1986 3.11 4.77 –1.66 1985 3.86 3.86 0.00 Source: U.S. Census Bureau (http://www.census.gov/foreign- trade/balance/c5700.html), February 2012.

64 Journal of Applied Business and Economics vol. 16(5) 2014 Such Chinese trade surpluses have enabled the country to accumulate a huge amount of foreign exchange and gold quickly, so that as the world’s largest holder of reserves, China now accounts for almost 30 percent of the world total, which equates to about 50 percent of the nation’s gross domestic product (see Table 3). This Chinese economic success has been highlighted in news reports and academic papers about the country’s trade surplus and its vast foreign holdings: “No wonder why the global financial crisis has brought the bilateral trade relationship between the U.S. and China the spotlight of international attention. Indeed, China and the U.S. together epitomize the sources and dangers of global macroeconomic imbalances.”5

TABLE 2 CURRENT ACCOUNT BALANCES FOR THE U.S. AND CHINA (U.S.$ BILLIONS)

Year United States (A) China (B) (B)/(–A) (%) 2000 –416.3 20.5 4.9 2001 –396.6 17.4 4.4 2002 –457.3 35.4 7.7 2003 –519.1 43.1 8.3 2004 –628.5 68.9 11.0 2005 –745.8 132.4 17.8 2006 –800.6 231.8 29.0 2007 –710.3 353.2 49.7 2008 –677.1 420.6 62.1 2009 –376.6 243.3 64.6 2010 -470.9 237.8 50.5 2011 -473.4 201.7 42.6 Sources: The World Bank (http://data.worldbank.org/indicator/BN.CAB.XOKA.CD?page=2)

TABLE 3 CHINESE FOREIGN EXCHANGE RESERVES: 2001-2011

As a percentage of Chinese Year U.S.$ (billions) GDP (U.S.$ billions) GDP 2001 212.2 1,325 16.02 2002 286.4 1,454 19.70 2003 403.3 1,641 24.58 2004 609.9 1,932 31.57 2005 818.9 2,257 36.28 2006 1,066.3 2,713 39.30 2007 1,528.2 3,494 43.74 2008 1,946.0 4,522 43.03 2009 2,399.2 4,991 48.07 2010 2,847.3 5,931 48.00 2011 3,181.1 7,319 43.46 Sources: for reserves, China’s State Administration of Foreign Exchange (http://www.safe.gov.cn/); for China’s GDP, the World Bank (http://data.worldbank.org/indicator/NY.GDP.MKTP.CD)

Bilateral trade between the U.S. and China has steadily increased since these two countries established their diplomatic relationship back in 1979. Their bilateral trade volume increased from $5

Journal of Applied Business and Economics vol. 16(5) 2014 65 billion in 1980 to $457 billion in 2010: “In 1978 (before China’s reform began) total U.S.–China trade (exports plus imports) was $1 billion; China ranked as the 32nd largest U.S. export market and its 57th largest source of U.S. imports. In 2010, China was the second-largest trading partner (after Canada), the third-largest U.S. export market (after Canada and Mexico), and the largest source of U.S. imports.”6 Table 1 shows that, under the prosperous growth of trade between the two countries, the U.S. trade deficit with China has also surged sharply in the past few decades, as U.S. imports from China have grown much faster than U.S. exports to China. That deficit increased from $0 in 1985 to $273 billion in 2010; in recent years, China has accounted for about 29 percent of the total U.S. trade deficit.

U.S.–China Investment Ties International investment flows consist of foreign portfolio investments (financial flows) and foreign direct investments. Foreign portfolio investments are purchases of foreign bonds, stocks, financial derivatives, or other financial assets without a significant degree of management control. Foreign direct investments (FDIs) are equity investments such as purchases of stocks, the acquisition of entire firms, or the establishment of new subsidiaries. The U.S. Department of Commerce defines FDIs as investments in either real capital assets or financial assets with a minimum of 10 percent ownership in a foreign firm.7 Financial flows between the two economies have increased, but have become more lopsided over time in favor of Chinese holdings of U.S. securities. Table 4 shows that portfolio investments from China to the U.S. have surged in recent years. This largely reflects Chinese Central Bank purchases of U.S. securities, which include short-term and long-term U.S. Treasury and corporate securities. As shown in Table 4, from 2003 to 2010, China’s holdings of U.S. securities grew by almost $1.35 trillion, or 527 percent; China’s holdings of U.S. securities as a share of China’s total foreign holdings also rose from 3.9 percent in 2002 to 15.2 percent in 2009, increasing its ranking of major foreign holders of U.S. securities from fifth to first.8 However, not only have U.S. holdings of Chinese securities been small fractions of Chinese holdings of U.S. securities, but also China has been a small source of U.S. holdings of foreign securities, with only 1 or 2 percent of total U.S. holdings of foreign securities. The largest types of U.S. securities held by China are short-term and long-term U.S. Treasury securities, which are used to finance U.S. federal deficits. Furthermore, Table 5 shows that China’s holdings of U.S. Treasury securities as a share of total foreign holdings increased from 6 percent in 2000 to 26 percent in 2010.

TABLE 4 CHINESE AND U.S. HOLDINGS OF EACH OTHER’S SECURITIES (U.S.$ BILLIONS)

China’s holdings of U.S. U.S. holdings of Chinese Year Chinese balance securities securities

2003 255.497 13.738 241.759 2004 340.972 12.723 328.249 2005 527.275 28.443 498.832 2006 698.929 75.314 623.615 2007 922.046 97.284 824.762 2008 1,205.080 54.903 1,150.177 2009 1,464.027 102.303 1,361.724 2010 1,610.737 102.226 1,508.511 2011 1,726.621 76.798 1,649.823 Sources: for Chinese holdings, the U.S. Department of the Treasury (http://www.treasury.gov/resource- center/data-chart-center/tic/Documents/shlhistdat.csv); for U.S. holdings, the U.S. Department of the Treasury (http://www.treasury.gov/resource-center/data-chart-center/tic/Pages/fpis.aspx) Note that the table excludes Hong Kong, Macau, and Taiwan, which are reported separately.

66 Journal of Applied Business and Economics vol. 16(5) 2014 Table 6 shows that FDI flows between the two economies have increased. In sharp contrast to financial flows, however, the U.S. has directly invested in China many times (somewhere between 40 to 40 times from 2000 to 2010) more than China has directly invested in the U.S. Nevertheless, the U.S. FDI in China is relatively small relative to China’s holdings of U.S. securities. In fact, the U.S. is one of the largest foreign direct investors in China.

TABLE 5 THE CHINESE MAINLAND HOLDINGS OF U.S. TREASURY SECURITIES: JUNE 2002- JUNE 2011

Grand total (U.S.$ China’s holdings in terms of total Year China (U.S.$ billions) billions) foreign holdings (%)

2000 60.3 1,015.2 5.94 2001 78.6 1,040.1 7.56 2002 118.4 1,235.6 9.58 2003 159.0 1,523.1 10.44 2004 222.9 1,849.3 12.05 2005 310.0 2,033.9 15.24 2006 396.9 2,103.1 18.87 2007 477.6 2,353.2 20.30 2008 727.4 3,077.2 23.64 2009 894.8 3,685.1 24.28 2010 1,160.1 4,435.6 26.15 2011 1,151.9 5,004.2 23.02 Sources: U.S. Department of the Treasury (http://www.treasury.gov/resource-center/data-chart- center/tic/Documents/mfhhis01.txt), February 20, 2012.

TABLE 6 U.S. AND CHINESE BILATERAL FDI FLOWS, 2000-2011 (U.S.$ MILLIONS)

Year U.S. FDI in China Chinese FDI in the U.S. U.S. balance 2000 11,140 277 10,863 2001 12,081 535 11,546 2002 10,570 385 10,185 2003 11,261 284 10,977 2004 17,616 435 17,181 2005 19,016 574 18,442 2006 26,459 785 25,674 2007 29,710 584 29,126 2008 53,927 1,105 52,822 2009 50,048 1,624 48,424 2010 58,509 3,245 55,264 2011 54,509 3,815 50,694 Source: the U.S. Bureau of Economic Analysis (http://www.bea.gov/international/), February 20, 2012.

Journal of Applied Business and Economics vol. 16(5) 2014 67 Causes of U.S.–China Trade Imbalances Although U.S.–China economic relations have substantially increased in recent years, tensions have arisen over a variety of issues, as the U.S. trade deficit with China has surged sharply since China joined the WTO. Four major U.S. concerns have been China’s currency manipulation, its intellectual-property theft, its WTO implementation issues, and its extensive use of industrial policies.

China’s Currency Policy Many observers believe that China’s heavily managed exchange rate has contributed to its huge trade surplus with its trading partners, most notably the U.S. Figure 1 shows that China pegged its renminbi (RMB), or yuan, to the U.S. dollar at about 8.28 yuan to the dollar between 1994 and 2005. On July 21, 2005, China appreciated the RMB to the dollar by 2.1 percent and moved to a managed float based on a basket of major foreign currencies. The dollar–yuan exchange rates moved from 8.27 in July 2005 to 6.83 in July 2009, or by 21.1 percent. However, once the effects of the global financial crisis began to become apparent around July 2009, China stopped its gradual appreciation of the RMB, and had kept the dollar– yuan rates relatively constant at 6.83 from July 2009 to mid-2010. With little fanfare, however, China’s currency has appreciated by 12 percent since June 2010 and 40 percent since 2005, leading many economists to question whether the exchange rate was still the most important economic issue for the U.S. to press with China’s leaders. Nevertheless U.S. policy-makers, politicians, labor unions, and business executives have charged that China continues to manipulate the RMB in order to keep its value artificially low. Estimates of undervaluation for the yuan against the dollar had ranged from 5 percent to 20 percent.9 However, on April 16, 20012, China moved to widen the daily trading range from ± 0.5 percent to ±1 percent from the par value. China does not eliminate its tight grip on the yuan because China’s central bank still sets a daily reference rate (par value) for its currency. Nevertheless, the change is seen as an important step toward foreign complains about China’s currency polity.

FIGURE 1 THE CHINESE YUAN VERSUS THE U.S. DOLLAR

Direct source: The Federal Reserve Bank of St. Louis (http://research.stlouisfed.org/fred2/series/DEXCHUS), December 3, 2012.

68 Journal of Applied Business and Economics vol. 16(5) 2014 Some researchers used to argue that a large appreciation of the RMB against the dollar, such as a 40 percent appreciation, could eliminate the bilateral U.S.–China deficit.10 However, they also say that such a policy would only hurt China and not reduce the overall U.S. trade deficit, because the U.S. would start to import the same goods from other countries instead. Some other researchers argue that depreciation of the dollar against the yuan may cause the U.S. trade deficit with China to deteriorate, at least in the short run, because the higher costs of U.S. imports will more than offset the reduced volume of U.S. imports.11 Many business executives and economists say that other issues, such as intellectual property theft and barriers to entering Chinese markets are now a bigger drag on the U.S. economy.

Intellectual Property Rights (IPR)12 China has failed to combat widespread IPR piracy in the country, despite its repeated promises to the U.S. and other countries. The U.S. has pressed China to improve its IPR protection efforts since the late 1980s, especially after the country became a member of the WTO in 2001. Although China has introduced many new laws and regulations designed to combat IPR violations, it has not enforced those new laws and regulations to a sufficient degree to halt the widespread of piracy of U.S. companies’ intellectual property in China. Such failures have led to new threats from the U.S. concerning trade sanctions against China. The Chinese enforcement agencies and judicial system often lack the resources and/or the will needed to vigorously enforce the IPR laws, and convicted IPR offenders generally face minor penalties. Many U.S. firms are dissatisfied with the lax and ineffective enforcement of the IPR laws in China, since such practices usually result in billions of dollars of loss in their annual sales revenues. Industry analysts claim that many U.S. products—such as motion pictures, business software, and sound recordings—are pirated in China, which is causing U.S. companies to lose billions of dollars every year. China also accounts for a significant share of the imported counterfeit products seized by the U.S. Customs and Border Protection. As a result, the U.S. has recently brought a series of IPR cases against China in the WTO, and in most cases obtained favorable rulings. Even though the Chinese government has repeatedly pledged, through agreements, promises, and new regulations, to take immediate action to crack down on the large-scale production, distribution, and exports of pirated materials, many business groups assert that there is an urgent need to establish effective mechanisms to ensure long-term enforcement of the IPR laws and to provide greater market access to U.S. IPR-related products. In fact, they contend that China’s poor IPR protection is one of the most significant obstacles to doing business in China.

China’s Obligations in the World Trade Organization China applied for WTO membership over a period of 13 years, but this effort was unsuccessful, mainly due to U.S. opposition. The opposition was based on a laundry list of economic and political issues, including concerns with human rights, tension between Taiwan and China, China’s nuclear arsenal, objections from labor unions in the U.S., and the use of protectionist policies by China: “As bad as our trade deficit with China is today, it will grow even worse if we approve a permanent trade deal,” said House Minority Whip David Bonior (D., Mich.) back in October 1999. Even with this opposition, on November 15, 1999, an historic agreement was reached between the Chinese and American trade negotiators, which set the stage for China’s formal entry into the WTO.13 One of the major worries of opponents of the normalization of trade relations with China was concern about the growing trade imbalance between the two countries. Many believed that the growing U.S. trade deficit was due to China’s high tariffs and numerous restrictions on American exports. In joining the WTO on December 11, 2001, China agreed to lower its average tariff from 16.7 percent in 2000 to 10 percent in 2005, and to reduce the number of items under import license and quota from approximately 300 to zero in the next five years. In addition, China agreed to liberalize foreign investment in banking, insurance, financial services, the wholesale/retail trade, and telecommunications. All of these industries had been under tight government control until China joined the WTO. In return, the U.S. granted China permanent normalized trade relations status. Without this legislation, China’s trade status would be open

Journal of Applied Business and Economics vol. 16(5) 2014 69 to yearly debate, as it had been in the past. Additionally, as a member of the WTO, China began to enjoy open markets with all WTO members, including the U.S. However, the U.S. identified many areas of concern in its eighth annual China WTO compliance, issued in December 2009. They include: (1) China’s failure to maintain effective IPR enforcement; (2) industrial policies and national standards designed to promote Chinese firms; (3) restrictions and distribution rights; (4) discriminatory and unpredictable health and safety rules on imports; (5) burdensome regulations and restrictions on services; and (6) the failure to provide adequate transparency of trade laws and regulations. In recent years, the U.S. has stated that China’s failure to comply with key areas of its WTO commitment largely stemmed from its incomplete transition to a market-based economy.

China’s Industrial Policies China has implemented numerous industrial policies to promote the development of industries deemed critical for future economic growth. China’s industrial policies consist of the two related criteria: (1) acquisition of technology and development of innovative capacity and (2) development of competitive domestic firms. 14 These policies are designed to change China from a major manufacturing center to a major global source of innovation and to make its industries more competitive in the global market place. As a result, China has focused a large share of its research and development (R&D) on its space program, aerospace development and manufacturing, renewable energy, computer science, and life sciences. The U.S. Department of Commerce believes that Chinese government procurement contracts for R&D and infrastructure projects are $85 billion per year. 15 Some U.S. companies have complained that China has given preferential treatment to locally developed technologies in government procurement. In addition, China has also established a number of restrictive practices and policies in its infrastructure projects against foreign companies. Critics charge that China has extensively used industrial policies and discriminatory government procurement policies to subsidize and protect Chinese firms at the expense of foreign companies. Of course, China has denied all of these charges.

Solutions A seemingly obvious solution to the bilateral trade imbalance between the U.S. and China would be to eliminate the four major causes of the problem discussed in the previous section: (1) the U.S. and China should negotiate a reasonable floating exchange system; (2) China should be urged to enforce its IPR laws more effectively; (3) China should be required to comply more effectively with the WTO commitments; and (4) China should also remove its discriminatory government procurement policies against foreign companies. . Although the two countries have reached a number of agreements on these goals, they have had virtually no impact on the size of their bilateral trade imbalance. Thus, these two countries should look at other measures to solve their trade disputes, in addition to their continuous negotiations on these three issues.

Balance Saving and Investment Account The U.S. trade deficits with other countries, especially with China, are rooted in its macroeconomic conditions. Well-balanced saving and investment is key to resolving the huge U.S. trade deficits. The past two decades have witnessed a declining household saving rate in the U.S. A negative government saving rate, as a result of the budget deficits, set the U.S. economy on course for the great recession of 2007– 2009. The U.S. trade deficit is a two-way affair, reflecting the behavior of borrower and lender alike. As long as Americans save relatively little, foreigners will use their savings to finance profitable investment opportunities in the U.S.—the trade deficit is the result. The U.S. should increase its savings to reduce its overall deficit with China in the long run. In order for the appreciation of the yuan against the dollar to reduce the bilateral trade imbalance, the U.S. must also boost the level of its savings in the long run. If China’s surplus with the U.S. falls through appreciation of the yuan against the dollar, it will have less capital to invest in the U.S. Thus, if the U.S. did not reduce its dependence on foreign savings for its investment needs, the U.S. would need to obtain

70 Journal of Applied Business and Economics vol. 16(5) 2014 investment funds from other countries, thereby making the overall U.S. current account balance remain relatively unchanged. The yuan has appreciated by about 40 percent in nominal terms against the dollar since 2005, but its trade surplus with the U.S. has widened. A similar story holds true in the case of Japan, where the yen rose dramatically throughout the 1990s, only to see Japan’s trade surplus continuing to grow. Thus, the real cause of the imbalance may not be the yuan, but may have to do with a lack of U.S. savings rather than a glut of China’s excess savings, savings over investment.16

Managed Trade Managed trade is government-sponsored trade designed to eliminate the trade imbalance between countries. An extreme example of managed trade would be China’s agreement to reduce its trade surplus with the U.S. by 20 percent per year, so that the two countries’ bilateral trade imbalance would be eliminated completely within five years. The main goal of U.S. trade policy with China in the past has been to open China’s market to U.S. investors and products through a variety of agreements, pressures, and other measures, such as China’s adoption of a market-based exchange system, its compliance with the WTO commitment, the enforcement of U.S. IPRs, and the elimination of other unfair Chinese trade policies. Although good progress has been made on these goals over the past two decades, the bilateral trade imbalance has surged rather than fallen back over time. Thus, China has recently faced U.S. pressures for it to reduce its trade surplus with the U.S. to a manageable level, through some type of managed trade. Perhaps the best approach to getting rid of the U.S. trade deficits with China is through mutual policy actions by both the U.S. and the Chinese governments. The U.S. should try to bring domestic spending down, closer to its domestic output, while China should try to bring its domestic spending up, closer to its domestic output. This mutual cooperation would make the U.S. market less dependent on Chinese products while China, on the other hand, would become less dependent on its exports to the U.S. However, U.S. efforts designed to eliminate its trade deficit with China appear to be extremely difficult, if not impossible, to achieve. Any drastic adjustments of U.S. macroeconomic policies designed to reduce the U.S. trade deficits may not be possible under current economic conditions, such as high oil prices, the housing crisis, the growing budget deficits, and weak foreign markets for U.S. exports.

Change of Composition of U.S. Products for Export to China Traditional U.S. exports to China include oilseeds and grains, waste and scrap, semi-conductors and other electronic components, and aerospace products and parts. Typical U.S. imports include computer equipment, miscellaneous manufactured commodities, communication equipment, and audio and visual equipment. These ranges of U.S. export items to China and import items from China indicate that the U.S. imports more advanced technology and manufactured products from China than the U.S. exports to China. This is at least partly the case because the majority of China’s exports to the world are produced by foreign-invested firms in China, many of which have shifted their production lines to China in the past few years, to gain access to low-cost labor and the state-of-the-art manufacturing facilities in the country. To balance the exports from China, the U.S. needs to change its strategies in order to promote exports to the Chinese market. If the U.S. intends to expand its exports to China, it should pay attention to the changes of demand in the Chinese market. China needs to import advanced technology and equipment worth tens of billions of U.S. dollars each year. Those advanced-technology products include high-performance computers, machine tools, and telecommunication equipment with an encryption capability, and the mobile phone technology known as CDMA (code division multiple access). For political reasons, export licenses for crime control and detection equipment are prohibited, and other high-tech products or programs with China are banned by the U.S. government. For example, the space program is a great opportunity for Sino–U.S. trade and cooperation, and such cooperation in this area could bring about tremendous benefits for American business entities. However, it looks quite unlikely that such cooperation with China will occur in the years to come.

Journal of Applied Business and Economics vol. 16(5) 2014 71 Although the U.S. is the most advanced country in the world in terms of science and technology, it accounts for only a relatively small share of China’s technology imports. At American expense, Japan and the European Union have steadily increased their market share in the Chinese technology market. China has huge reserves of foreign exchange and it is a potential market to which U.S. companies can export their products. Both the U.S. and China should explore new approaches, to maintain a well-balanced trade of goods and services between the two countries now and in the future.

CONCLUSION

Some U.S. politicians argue that the huge U.S. trade deficit with China arose solely because of unfair Chinese trade practices. In their view, Chinese practices directly threaten the world trading system. Chinese officials, on the other hand, point to the low U.S. saving rate, the U.S. budget deficit, and problems with U.S. export products, such as poor quality, high prices, poor after-sales services, and inadequate finance terms. The truth, of course, lies somewhere between these two positions. Economic and other relations between these two superpowers have become increasingly important. In spite of that notion, their relations have become severely strained. As China’s role in the world economy has grown, so the U.S. has become concerned about its international economic position. China has felt U.S. pressures on its unfair trade practices for the past two decades. Nevertheless, it has taken the full advantage of the open U.S. market, but without reciprocity. U.S. measures against Chinese exports have surged, and China’s reaction to the U.S. pressure has recently strained the relationship between these two countries. The U.S. has demanded that China appreciates its yuan against the dollar, complies with its obligations in the WTO, improves its IPR regime, restrains its export growth in the U.S., and invests in the U.S. Although the two countries have reached a number of agreements on these goals, they have had practically no impact on the size of their bilateral trade imbalance. Thus, these two countries should look at other measures to solve their trade disputes, in addition to their continuous negotiations on these issues. Perhaps the best approach to getting rid of the U.S. trade deficits with China is through mutual policy actions by both the U.S. and the Chinese governments. The U.S. should try to bring domestic spending down, closer to its domestic output, while China should to bring its domestic spending up, closer to its domestic output. This mutual cooperation would make the U.S. market less dependent on Chinese products, while China would become less dependent on its exports to the U.S. However, U.S. efforts designed to eliminate its trade deficits with China appear to be extremely difficult, if not impossible, to achieve. Any drastic adjustments of U.S. macroeconomic policies designed to reduce the U.S. trade deficits may not be possible under current economic conditions, such as high oil prices, the housing crisis, the growing budget deficits, and weak foreign markets for U.S. exports.

ENDNOTES

1. Anthony J. Makin, “Is China’s Exchange Rate Policy a Form of Trade Protection,” Business Economics, Vol. 44, No. 2, p. 80. 2. The World Factbook, https://www.cia.gov/library/publications/the-world-factbook/ (Central Intelligence Agency, Washington, D.C.), accessed February 15, 2012. 3. Bank for International Settlement (BIS), BIS Annual Report (BIS, Basle, 2006). 4. Wayne M. Morrison, “China–U.S. Trade Issues,” CRS Report for Congress: Congressional Research Service 7-5700, January 7, 2012. 5. Eswar S. Prasad, “Effects of the Financial Crisis on the U.S.–China Economic Relationship,” Cato Journal, Spring/Summer 2009, p. 223. 6. See note 2. 7. Suk Kim and Seung H. Kim, Global Corporate Finance, 6th edn (Malden, MA: Blackwell, 2006), pp. 99– 100. 8. Morrison, “China–U.S. Trade Issues,” p. 13. 9. David Leonhardt, “China’s Currency Rises, U.S. Keeps Up Its Pressure,” The New York Times, February 16, 2012, p. A14.

72 Journal of Applied Business and Economics vol. 16(5) 2014 10. Wing Thye Woo, “Understanding the Source of Friction in U.S.–China Trade Relations: The Exchange Rate Debate Diverts Attention from Optimum Adjustment,” Asian Economic Papers, Vol. 7, No. 3, pp. 61– 95. 11. Kim and Kim, Global Corporate Finance, p. 71. 12. The information in this section came from Morrison, “China–U.S. Trade Issues.” 13. Kim and Kim, Global Corporate Finance, pp. 74–76. 14. Authur Kroeber, “Chinese Industrial and Trade Policies,” http://www.polsci.indiana.edu/china/papers/kroeber.pdf, February 20, 2012 15. U.S. Department of Commerce Remarks by Secretary of Commerce Gary Locke Chamber of Commerce and U.S.-China Business in Beijing, China, May 18, 2010. 16 “Chinese 16. “Chinese Unlikely to Yield to US Pressure,” Asia Monitor, May 2010, pp. 2–3.

Journal of Applied Business and Economics vol. 16(5) 2014 73

Social Media Marketing: A Myth or a Necessity

Anne Whiting Empire State College

Anant Deshpande Empire State College

INTRODUCTION

In 2012, Lay’s Potato Chips began a very ambitious campaign: to use social networks to create a new flavor of potato chips. Using the title, “Do Us a Flavor”, the company utilized Social Media Marketing, or SMM, to connect with millions of Americans to develop, describe, discuss, and vote on the new chip’s flavor. The campaign was a success: beyond connecting with customers and empowering them to submit flavor ideas, Lay’s received over 12 million entries and billions of mentions on social networks, proliferating the brand name beyond direct fans to current and potential customers (Exil, 2014). The company took an extra step into the social media realm in 2014 by encouraging entrants to market their own flavors via SMM – thus proliferating and advertising the brand and product to an even greater audience. (Exil, 2014; Weldon, 2014) Lay’s isn’t the only company to utilize SMM, which can be defined as “the attempt to use social media to persuade consumers that one’s company, products and/or services are worthwhile” (Ward, 2011), to market its products and services. Other companies that have successfully used SMM in their marketing campaigns include Coca-Cola, Starbucks, and the omnipresent Google (Brady, 2010; Daileda, 2013). With the success of these organizations utilizing social networks as part of their marketing strategy, other companies were quick to follow suit. However, many companies have found that marketing via social networks is not as easy as it first appears, and have either given up the SMM game or revised their marketing plans to reflect the failure of their SMM strategies. Today, numerous web pages and journal articles address the weaknesses and difficulties of the SMM system, and advise managers that SMM is not the marketing dream world some would believe it to be; some even suggest that SMM should not be used by businesses (Brenkert, 2002; Carol, 2013; Garrett & Karnani, 2010; Grensing-Pophal, 2012; Khang, Ki & Ye, 2012; Pofeldt, 2013). However, as we enter the second decade of the twenty-first

74 Journal of Applied Business and Economics vol. 16(5) 2014 century, SMM has moved from being a novelty dream world to a pitfall-prone necessity for most businesses in America today (Platon & Orzan, 2012).

BACKGROUND

Before we discuss what an organization can do to use SMM successfully, let us look at the early days of social media and social networks and the definition of social networks. First, social networking existed before the internet. In 1979 Ward Christensen and Randy Suess created the computerized bulletin board system (CBBS), a system with which companies could contact employees about announcements such as meetings and reports (Borders, 2009; Guna, 2009). The CBBS was intended to send information to numerous recipients without multiple telephone calls and/or memos; however, other employees began using their companies’ CBBS’s for more than just announcements (Guna, 2009). In the 1990s, with the rise of the dot-com era, social networking websites began to emerge. Although Beverly Hills Internet (later known as Geocities) was one of the first social site to come onto the scene, the first network to reflect current social networking sites was SixDegrees, a website that was launched in 1997 and allowed users to make profiles and connect with friends (Borders, 2009; Guna, 2009). More social networking sites appeared during the next decade, the most famous being Friendster.com, Facebook, MySpace, Twitter, and YouTube (Guna, 2009). Businesses quickly realized that social networking sites held potential for marketing new products and creating a buzz about companies and their products. Managers could post about an event, and hundreds of thousands of people could receive the post within minutes. Also, companies figured that they could keep in touch with customers through networking sites, and social media marketing was born. (Guna, 2009)

DEFINITION OF A SOCIAL NETWORK

Platon & Orzan (2012) offer the definition of a social network as an application that is created and sustained through “means of human interaction”. This interaction is often shaped through groups formed around similar cultural/geographical regions, similar interests, or other common needs or personal traits. Furthermore, social networks are typically focused on aspects of work and personal life and may appeal to specific age groups rather than multiple generations. (Platon & Orzan, 2012) Because social networks are formed around fairly homogeneous groups, marketers realized they could tap into specific groups of customers simply by utilizing specific social networks, or through engaging specific groups in larger, less homogeneous networks. In 2012, 1.2 billion individuals were a part of a social network, and studies indicated that 20% of all time online was spent on a social network, up to an average of 5 hours every day (Platon & Orzan, 2012). Marketers, realizing the opportunity to reach this large, attentive audience, have been drawn to social networks as a channel to present their products and companies.

ISSUES WITH SOCIAL MEDIA MARKETING

However, SMM did not live up to numerous companies’ original expectations. Many thought that a Facebook page or Twitter profile would create awareness for a brand or product, replace expensive and time-consuming advertising, and automatically expose new audiences to a brand or product (Hames, 2009; Carol, 2013). Many companies believed the hype, and jumped haphazardly into Social Media Marketing—only to find that SMM is not a one-stop-marketing-solution (Platon & Orzan, 2012). Let us consider the downsides and potential pitfalls of Social Media Marketing. First, SMM tools do not magnetically draw people to the company’s profile (Hames, 2009). The company or product must be known by and be popular with customers before someone will look for the page and connect with the brand or product. Because of this, SMM does not work well at creating awareness about a company or product. Secondly, SMM tools cannot work alone (Hames, 2009). The tools need something to point customers to the brand/product and to the profile. A blog, website, business

Journal of Applied Business and Economics vol. 16(5) 2014 75 card, email signature, or advertisement can point customers to a SMM profile, but only if potential customers see the “pointer”. Thirdly, if not carefully planned, the profile can display images or give impressions that may turn customers away from using the services or products of the company, or even cause a lawsuit to be brought against the company (Afshar, 2014; McCrea, 2009). Forth, the security and privacy of those who the company posts, tweets, or otherwise talks about or shows pictures of on the company’s profile may be at risk (Clark & Melancon, 2013). Customer privacy may be compromised through SMM campaigns that use consumer data available on the consumers’ social profile, and malicious outsiders may go onto the social media website and use this information for unethical or criminal activities (Carol, 2013; Clark & Melancon, 2013). Companies should take care to avoid posting information that may jeopardize customers’ security. In the same way, companies should monitor the discussions happening on their wall or in their “comments” areas: discussions that detract from the brand’s image should not be allowed to turn away potential customers from the company or product. Fifth, social network users see the network as a personal space in which they are in control over their content and interactions; marketers must take care to not overstep the personal space boundaries or risk alienating customers or being labeled as “spam” (Platon & Orzan, 2012). Overt advertising is often similarly labeled as “spam”. Platon & Orzan (2012) point out that it is an exaggeration to expect much from a SMM campaign that was not sufficiently planned or executed: no amount of SMM can make “a poor advertisement to be successful [or] guarantee the sale of unsuitable products”.

BENEFITS OF SOCIAL MEDIA MARKETING

As social media marketing cannot be used to overtly advertise products and services, and may even jeopardize customers and customer relationships, why are companies still pursuing SMM? Research shows that in 2014 over 90% of marketers use SMM, 70% of marketers expect increases in social media spending, SMM advertising will reach $4.6 billion, and socially-active small and medium-sized businesses will monetarily sustain or expand their social advertising efforts (Clark & Melancon, 2013; Oney, 2014; Sass, 2013). The first answer is that SMM builds relationships (Filisko, 2011; Clark & Mashburn, 2013; Gupta, Tyagi & Sharma, 2013; Schlinke & Crain, 2013). This is one reason that joining in on conversations is so important to the success of a SMM strategy. Personal relationships developed will build a better, more loyal fan base for a company or product than will an impersonal marketing campaign, as well as help the company understand its customers and create more useful products for customers to purchase (Clark & Melancon, 2013; Mohammadian & Mohammadreza, 2012). This is vital in today’s relationship-based society. Companies may also be able to shift these relationships over to other products or brands of the company. For example, if a customer becomes a fan of a brand of nail polish, the company might be able to get this customer to try its shampoo offerings as well. However, as cited above, businesses must be careful not to overtly market to their SMM contacts, or customers will write off the SMM campaign as yet another advertisement, with a company that only cares about its products, not its customers (Bloom & Novelli, 1981; Clark & Melancon, 2013; George, 2010). The second answer is that SMM, when used carefully, can result in positive word of mouth (WOM) marketing, which is also vital in today’s world rocked with conflicting and confusing advertising campaigns (Clark & Melancon, 2013; Meiners, Schwarting, & Seeberger, 2010). Nearly 85% of Millennials (those born between 1980 and 1995) state that WOM is the “primary influencer of their purchasing decisions”; other generations are also heavily impacted by WOM marketing (Clark & Melancon, 2013; Garst, 2013). Social media has been demonstrated to make customers WOM marketing advocates, thus advancing the company and the product. One study, for example, showed that customers acquired through a SMM WOM campaign raised a store’s profits by 29%, while those exposed to traditional advertising only raised revenue by 5%. (Meiners et al., 2010) WOM customers are also more likely to purchase from a company again; in one case, there was a 41% higher repurchase rate among WOM-acquired customers vs. regular customers (Meiners et al., 2010). However, WOM can also work

76 Journal of Applied Business and Economics vol. 16(5) 2014 negatively: some 21% of customers have utilized social networks to spread negative WOM about a company or brand and dissuade potential customers (Clark & Melancon, 2013).

USING SMM MORE EFFECTIVELY

There are several tactics marketers and businesses can implement to use SMM more effectively. These tactics focus on bringing value to customers as well as businesses, as a one-sided relationship, with only the company benefiting, is rarely effective in creating positive relationships or positive WOM (Clark & Melancon, 2013). First, businesses can post promotion codes or coupons on their social networks or offer loyalty programs where customers earn points or other benefits (Clark & Melancon, 2013; Hames, 2009). These promotion codes or coupons will drive customers into the company’s store, or onto the company’s website. An example of this is Hobby Lobby, which posts a coupon on its Facebook page each week (Hobby Lobby, 2014). The coupon is valid in-store or online, and ranges from specific (30% off of Wilton products) to quite general (40% off any regular-priced item). Second, social networks are a good place to poll customers about their likes and dislikes (Hames, 2009; Phillips, McFadden & Sullins, 2010; Platon & Orzan, 2012). Why do you like this product, and what does it mean to you? This can give companies new marketing ideas and an understanding of who their customers really are and what new products would excite them. Third, in the same way, businesses can use SMM to create and encourage brand pride (Lipsman, Mud, Rich & Bruich, 2012; Davis, 2010; Khang, Ki, & Ye, 2012). If customers like a brand or product, it is advantageous for a company to attract them to the company’s social network account, and help them to express their pride in the brand or product. This will also help to build a community of those who love similar products or experiences, which will further cultivate customers’ pride in the business’s brand and products as well as utilizing one of the main reasons people use social networks (Platon & Orzan, 2012). For example, Harley Davidson released a campaign asking fans to upload pictures of themselves on their Harleys to the company’s website. These images were then used in a tile mosaic on the front of the 2010 Harley catalog. Not only did the campaign show customers’ loyalty to and pride in the Harley Davidson brand, but it brought customers together socially and increased buyers in Harley Davidson dealerships who came to buy the catalog with their faces on the front cover. (Davis, 2010) Fourth, YouTube video contests are another popular way of allowing customers to show pride in a company’s products. However, companies may want to use this tactic with care, as, without careful planning, the campaign can get out of control with too many entries for the company to handle effectively, and/or negative feedback from those who dislike the brand or product outshining those who want to give the product or brand a positive promotion (Armstrong & Kotler, 2010). A good example of this was Chevrolet’s customer-generated advertising competition for its Tahoe SUV. Many of the videos posted complained about SUV’s high operating costs and harmful effects on the environment, generating negative promotion instead of the pride and positive promotion the company desired (Armstrong & Kotler, 2010). Fifth, to further aid in building relationships and creating a sense of community, a company can join communities that are in their “niche” of business (George, 2010; Trusov, Bucklin & Pauwels, 2009). For example, a company that sells pet grooming supplies can join online communities about pets. These micro-communities will get the company’s message out to a new audience, and may positively affect the company’s image or sales as it makes new contacts in these communities. An important aspect of a SMM campaign is that companies should stick with their brand philosophy. A company’s image has sustained the company thus far; it should not be abandoned when the company goes online (George, 2010). Sticking with the brand philosophy and basic ideology of the company will help businesses not to over-promise/under-deliver, avoid creating an image that is not the company, and avert turning away potential clients who might have bought from the company had it let its original image guide its marketing efforts. Most importantly, businesses should remember that the goal of an SMM campaign is to make quality relationships (Clark & Melancon, 2013). Just because a company has 300 “friends” or “followers” doesn’t

Journal of Applied Business and Economics vol. 16(5) 2014 77 mean that it has a strong SMM campaign (Afshar, 2014). Rather, what is the quality of the relationships between the company and those friends, and how can those relationships be strengthened? (Rucker, 2011)

AN EXAMPLE OF SUCCESSFUL SOCIAL MEDIA MARKETING

Now let us look at a company that has used SMM successfully: Starbucks, the coffee shop that reported 2013 revenue at $14.9 billion (Bloomberg Businessweek, 2014). Starbucks has one paramount goal in its campaigns across its social networks: engaging customers (Colvin, 2013). This includes listening to conversations (for example, posting an image on Facebook and then listening to the replies posted by fans), sparking community among fans through posting conversation starters both with the company and with other fans, creating a warm and friendly persona for the brand (mostly through images, background stories, and feel-good-about-us (or yourself) sayings), and initiating customer relationships by offering rallying points for fans to converge around (Allison, 2013; Colvin, 2013; Moth, 2013; Roman, 2013). The company typically refrains from overt marketing, or even promoting itself or its products, although special deals and announcements are sometimes posted (Moth, 2013; Roman, 2013). On Twitter, the company intentionally engages with customers, answering their questions, retweeting what customers tweet, and listening to what customers have to say about the brand. This creates “an open communication channel to speak with the public” (Moth, 2013; Noff, 2010). The company’s Facebook page hosts videos, photos, and blog posts from the company, as well as invitations to special Starbucks events. However, to keep communication two-way, the company has a specific place for friends to open discussions and comment on the content of the company’s Facebook profile. Starbucks encourages reposting, which further spreads the company’s SMM to non-fans. (Moth, 2013; Roman, 2013) The Starbucks YouTube channel is also quite popular: several videos have well over 1 million views (Starbucks Coffee, 2014). The company uploads its commercials, as well as videos explaining the origins of some of its different coffee blends, its history, and its charity work. Furthermore, Starbucks allows viewers to take the videos and embed them anywhere the viewers want on the web. Even though this puts the company at risk of finding their videos in places they would not like to be associated with, so far Starbucks has not found this to be a problem, and has reaped the benefits of allowing people to repost Starbucks videos (Noff, 2010). Lastly, Starbucks oversees a joint campaign between My Starbucks Idea and Ideas in Action. At My Starbucks Idea, which is a social network created by Starbucks, customers are asked to share ideas on anything related to Starbucks and the Starbucks brand. This enables users to feel that they are a part of the decision-making process. Starbucks reviews the ideas posted on My Starbucks Idea. If an idea is implemented, a Starbucks employee can publish a blog post about how the idea was used on one of the company’s blogs, Ideas in Action. This allows customers to see that their ideas are being used within the company, and that they and their ideas are important to the company as a whole. (Noff, 2010) Accepted ideas have included splash sticks, simplified mobile transactions, skinny beverages, and cake pops (Starbucks, 2013).

CONCLUSION

Effective use of SMM is seen as a potential game changer (Gupta et al., 2013). As SMM continues to grow it will play a large role in how clients are informed (Buzzetto-More, 2013). Because of this, it is becoming increasingly important to conduct timely and continued research on SMM. The primary aim of the research was to highlight the fact that SMM, if used carefully, can be an extremely useful tool for the organization. It is our hope that the views presented above will further encourage additional research on the impact of SMM on contemporary business practices. Future research in measuring the success of SMM should focus on its impact on a company’s brand image, and what types of companies have the most success using SMM tools. For example, exactly how much more brand pride is a company experiencing after starting an SMM campaign? How greatly is the

78 Journal of Applied Business and Economics vol. 16(5) 2014 image of the brand changed in the minds of consumers after the integration of SMM into a marketing campaign? Do small, medium, or large-sized companies have the most success using SMM? Do old-style companies have more or less success with SMM than new, hip businesses? How about regional differences between companies and their SMM campaigns? Answers to questions like these will help companies decide how much to spend on SMM strategies, and give them a better idea of whether a SMM strategy would be helpful to their business. Thus, despite downsides to social media marketing, there are upsides as well. Many companies have implemented this strategy into their marketing mix with very favorable results. Brand pride is strengthened, customers are engaged, and relationships are built between a company and its customers. Most importantly for the health of the company, customers relationships are translated to positive WOM and higher sales result. By using the right tactics—and understanding its strengths and limitations— companies can use SMM to market themselves in an increasingly high-tech world to an increasingly high- tech audience. Sometimes, meeting customers where they are (in this case, on the internet) may be half the battle in creating a relationship with a company, multiplying purchases, and increasing brand loyalty. Social media marketing can assist companies in this, and in helping customers show their pride in a company. It is true that social networks, when used with care, can be a valuable, and even necessary, marketing tool for an organization.

REFERENCES

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Journal of Applied Business and Economics vol. 16(5) 2014 81

Innovation in the Automobile Industry: How the Changing Face of Global Competition Affects Motor Vehicle Patenting

Gerald P. W. Simons Grand Valley State University

Paul N. Isely Grand Valley State University

INTRODUCTION

Automobile manufacturers from high income economies are facing increasing competition from manufacturers from low income economies. To illustrate, the China Association of Automobile Manufacturers reports that there are now more than 100 domestic auto manufacturers in the country, whereas there were only 18 in 2001; and the Society of Indian Automobile Manufacturers reports that vehicle production in India in 2011 was more than double that of 2006. Competition, of course, can be in different forms such as price and after-sales service, but competition in the auto industry is also particularly intensive in regards to the quality and features of the vehicles. As a result, the auto industry historically has an extremely high level of innovation, including when measured by patenting. In this paper we look at the patenting behavior of automobile manufacturers. Our focus is on auto manufacturers in countries with large, long-standing auto industries and how their patenting behavior is affected by increased competition from auto manufacturers in countries that are more recent entrants to the global auto industry. The “North-South” theory of product cycles indicates that as manufacturers in the south increase their production, manufacturers in the north will increase their rate of innovation in order to stay ahead of the new competition. It is this conclusion that we examine here with respect to the global auto industry. The study holds relevance for understanding the impact that increased globalization has on business decisions dealing with technology and innovation. The changing nature of global competition, with heightened interconnections between both innovators and imitators, results in a growing emphasis on technological competition rather than just competition in price or quantity.

82 Journal of Applied Business and Economics vol. 16(5) 2014 LITERATURE REVIEW

There is an extensive literature concerning the link between production in one country and innovation in another. Much of this research focuses on the acquisition of technology by reverse- engineering/learning from imported goods, or from knowledge spillovers from patents, R&D programs and research publications. The role that imports play in innovation has been explored for both developed countries (see, for example, Coe and Helpman, 1995, and Keller, 2002) and developing countries (Schiff, Wang and Olarreaga, 2002, and Wang, 2007). Knowledge spillovers from research efforts in other countries is the focus of Jaffe, Trajtenberg, and Fogarty (2000), Hu and Jaffe (2003), and Simons and Isely (2010), for example. Much of the current literature in this area is based on the endogenous growth theories of Romer (1990), Grossman and Helpman (1991a), and Aghion and Howitt (1992). Grossman and Helpman (1991b) extend their earlier work on quality ladders to develop a North-South theory of product cycles that indicates a positive effect on innovation in the North from competition in the South. It is this model that we empirically investigate here. The way in which innovation is measured in the literature varies. Rather than using R&D expenditure (which measures the input to innovation) we follow the growing number of studies using patent data to measure the output of innovation (see, for example, Pavitt and Soete, 1997 and Jaffe, Fogarty, and Banks, 1998).

METHODOLOGY

Data The time period for our study is 1998-2010. We obtain all our patent data from the U.S. Patent and Trademark Office’s online patent database (http://www.uspto.gov), counting the number of granted U.S. patents from the patent classes 180 (Motor Vehicles) and 123 (Internal-Combustion Engines). Included in each patent’s information is the country of origin of the patent’s owner (assignee), which allows us to identify patents originating from North versus South firms. It can take several years for the USPTO to either grant or deny a patent application. So, to test if there is a significant effect from patents filed in 2010 that have not yet been granted, we run a specification of the base model with a 2010 dummy variable. Because this dummy is not significant, we do not include it in the specification of the model shown below. We obtain data on Motor Vehicle R&D spending by company from the EDGAR database and convert into real 2000 values using the U.S. GDP Deflator (available at www.bea.gov). We obtain data on automobile production from the International Organization of Motor Vehicle Manufacturers website (www.oica.net). Our North firms are: Toyota, GM, VW, Ford, Nissan, Honda (“Big 6”) – these consistently rank as the largest auto producers during the entire time period of our study and they consistently patent heavily in the U.S. Our South firms are other manufacturers as listed by OICA (e.g. Tata, Beijing Automotive, Hyundai etc.).

Model We begin by modeling patenting as a Cobb–Douglas style production function:

PATENTS = f(PRODUCTION - PRODUCTION , PRODUCTION , R&D , year) (1) kt nt kt st kt where • Subscript k designates individual North firms, and t is years. • PATENTS is the number of granted auto patents that were applied for in the U.S. in year t, where kt firm k is the assignee/owner. • PRODUCTION is the natural log of auto production by all North firms in year t. nt

Journal of Applied Business and Economics vol. 16(5) 2014 83 • PRODUCTION is the natural log of auto production by firm k in year t. kt • PRODUCTION is the natural log of auto production by all South firms in year t. st • R&D is the natural log of firm k’s spending on motor vehicle research and development in year kt t. • Year is a trend variable

We formulate two versions of the basic model described above: One using auto production levels and the other using the percentage growth in auto production levels. We try three different specifications for these models: Negative binomial, log-log, and Poisson. The results for both models are consistent for all three specifications. We report here only the results of the negative binomial specification for space considerations.

RESULTS

Table 1 gives the regression results for the two model specifications.

TABLE 1 REGRESSION RESULTS

Model 1 Model 2 Production Production growth R&D -0.51 0.04 (2.11)* (0.11) NORTH 1.33 PRODUCTION (0.50) SOUTH -2.26 PRODUCTION (1.45) NORTH -0.04 GROWTH (0.44) SOUTH GROWTH -0.15 (2.62)** YEAR 0.14 0.04 (1.30) (3.20)** CONSTANT -250.37 -159.58 (1.47) (3.22)** Absolute value of z statistics in parentheses * significant at 5%, ** significant at 1%.

Recall that our focus in this paper is to see if the patenting behavior of North firms is influenced by competition from firms in the South. In Model 1, the coefficient on South Production is negative, though not significant. A negative correlation here indicates that as firms in the South increase their auto production, patenting by the Big 6 North firms decreases. In Model 2, the coefficient on South Growth is negative and statistically significant at the 1% level. This indicates that as firms in the South increase their auto production more rapidly, patenting by the Big 6 North firms decreases. These results hold through all three regression specifications (negative binomial, log-log, and Poisson).

CONCLUSIONS

The above results indicate that we are unable to find any support for the traditional North-South innovation model which predicts that greater competition from the South will encourage North firms to

84 Journal of Applied Business and Economics vol. 16(5) 2014 innovate more. In contrast, our results indicate some support for the opposite conclusion – that greater competition from the South has a dampening effect on innovation by firms in the North. The above finding seems to imply that, within the parameters of our study, the large North auto manufacturers do not treat South firms as true competitors, at least not as compared to their North rivals. What could explain this behavior? One possibility is that, through hubris or ignorance, North auto manufacturers have been slow to recognize the threat posed to them by rivals in the South. This has parallels in auto manufacturing history – for example, the recalcitrance of the U.S. “Big 3” to invest heavily in fuel efficient vehicles allowed Japanese manufacturers to capture a substantial share of the U.S. market when consumer tastes shifted in the 1970s and 80s. Another possibility is that, while the thought process behind the traditional North-South model is valid, the difficulty is that increasing global economic integration has blurred the distinction between North and South. North companies increasingly produce in both the North and the South, and a growing number of joint ventures are between North and South companies rather than just North-North or South- South. This means that, although taking place in the South, some of the production that is labeled in our study as South may actually involve North manufacturers. While the findings presented here do not allow us to identify the cause of the apparent contradiction with the North-South innovation model, it does open some avenues for us for future research.

REFERENCES

Aghion, P. & P. Howitt. (1992). A model of growth Through Creative Destruction. Econometrica, 60, 323-361. Coe, D. & E. Helpman (1995) International R&D spillovers. European Economic Review, 39(5), 859-887. Grossman, G., Helpman, E., (1991a). Quality ladders in the theory of growth. The Review of Economic Studies, 58, 43–61. Grossman, G., Helpman, E., (1991b). Quality ladders and product cycles. Quarterly Journal of Economics,106, 557–586. Hu, A. & Jaffe A. (2003) Patent citations and international knowledge flow: the cases of Korea and Taiwan. International Journal of Industrial Organization, 21(6), 849-880. Jaffe, A., Fogarty, M., & B. Banks. (1998). Evidence from patents and patent citations on the iImpact of NASA and other federal labs on commercial innovation. Journal of Industrial Economics, 46(2), 193-205. Jaffe, A., Trajtenberg, M., & M. Fogarty. (2000). Knowledge spillovers and patent citations: evidence from a survey of inventors. American Economic Review Papers and Proceedings, 90(2), 215-218. Keller, W. (2002). Trade and the transmission of technology. Journal of Economic Growth, 7(1), 5-24. Pavitt, K. & Soete L. (1997) International differences in economic growth and the international location of innovation. In Wolff, E. (ed.), The Economics of Productivity, Vol. 1. Cheltenham, UK: Edward Elgar Publishing. Romer, P. (1990). Endogenous technological change. The Journal of Political Economy, 98, S78-S102. Schiff, M., Wang, Y., & M. Olarreaga (2002) Trade-related technology diffusion and the dynamics of north-south and south-south integration. World Bank Working Paper #2861. Simons, G. & P. Isely. (2010). The effect of offshoring on knowledge flows in the U.S. automobile Industry. Economics of Innovation and New Technology, 19(6), 553-568. Wang, Y. (2007.) Trade, human capital and technology spillovers: an industry level analysis. Review of International Economics, 15(2), 269-283.

Journal of Applied Business and Economics vol. 16(5) 2014 85

Factors Associated with Student Performance in Intermediate Accounting: A Comparative Study at Commuter and Residential Schools

Mostafa M. Maksy Kutztown University of Pennsylvania

INTRODUCTION

Several prior research studies have explored various factors (e.g., general academic performance, aptitude, prior exposure to mathematics, prior exposure to accounting, age, gender, motivation, effort, and other intervening variables) that are associated with student performance in college-level courses. It is widely believed that motivation and effort significantly influence individual performance in college. However, as the review of prior research below indicates, few studies have investigated their impact on accounting education. This study investigates the associations between some selected motivation and distraction factors and student performance in the undergraduate Intermediate Accounting II course. The study also investigates whether students’ self-perceived abilities (such as writing, math, reading and listening) have any associations with their performance in this course. Maksy (2012) investigated student performance in the Intermediate Accounting II course at a commuter university. One of the limitations of Maksy’s study was that the study was conducted at a commuter school. He stated “we do not know whether the results will be the same for residential schools.” One of the suggestions for future research was to replicate the study at a residential school. In this study, the author does not only replicate the study at a residential school but also collects new data from students at a commuter school of similar characteristics to those of the residential school to determine whether factors affecting student performance at commuter schools are generalizable to residential schools. It is not the objective of this paper to show the impact of the distinction between these two types of schools on student performance, nor to show that students at commuter schools have different motivations and distractions than students at residential schools. On the contrary, the objective of the paper is to show that there are no major differences and most of the results are generalizable to both types of schools. If major differences exist, a suggestion for future research would be to conduct a new study to investigate the possible reasons for the differences. As proxies for motivation, the study uses a variety of factors: the grade the students intend to earn in the course, intention to take the Certified Public Accountant (CPA) examination, and intention to

86 Journal of Applied Business and Economics vol. 16(5) 2014 pursue graduate studies. As proxies for distraction, the study uses the number of hours of work per week, the type of job (especially if it is not related to accounting or business), and the number of courses taken per semester. To control for prior actual ability, the study uses two other factors: the grades earned in Intermediate Accounting I and overall Grade Point Average (GPA.) Student performance, the dependent variable, is measured once by the letter grade and another time by the total points earned in the course. The study’s objectives are predicated on the assumption that identifying some factors that motivate students to perform well and some factors that distract them from performing well may help us to emphasize the motivation factors and discourage the distraction factors. For example, if educators know that student intention to sit for the CPA exam motivates students to study hard and earn higher grades in the Intermediate Accounting II course, during advising, educators may encourage their students to plan to sit for the CPA exam. Also, if educators know that the type of job (especially if it is not related to accounting) does not have an effect on student performance, they may not discourage their students to have non-accounting-related jobs. Similarly, if working too many hours (within a relevant range of, let us say, 0 to 40 hours a week) does not have an effect on student performance, educators may not advise students that have low grades that they must reduce their work hours per week. Educators may advise their students to make sure, regardless of how many hours they work per week, to devote sufficient time to their study and to make sure that they are using good study habits. Of course, some students heed their educators’ advice and some do not. Educators have no control over that. The remaining parts of the paper present a review of prior research, discussion of the study objectives and hypotheses development, research methodology, and results. The paper ends with conclusions, recommendations, study limitations, and some suggestions for further research.

LITERATURE REVIEW

Many prior studies have explored various factors (e.g., general academic performance, aptitude, prior exposure to mathematics, prior exposure to accounting, motivation, effort, and other intervening variables) that are associated with student performance in college-level courses. The Grade Point Average (GPA) is used frequently as a proxy for prior academic performance and aptitude. Several researchers, using US data, find evidence supporting GPA as a significant predictor of performance in accounting courses (Eckel and Johnson 1983; Hicks and Richardson 1984; Ingram and Peterson 1987; Eskew and Faley 1988; Doran et al. 1991, and Maksy and Zheng 2010). Wooten (1998) finds that aptitude is a significant variable in influencing performance of the traditional students in introductory accounting. In contrast, he finds that current performance of nontraditional students does not seem contingent on previous academic success. Maksy and Zheng (2008) find that the grade in Intermediate Accounting II is a strong predictor of student performance in the Advanced Accounting and Auditing courses. The research findings in the US are supported in Australia by Jackling and Anderson (1998) and in Scotland by Duff (2004). In Wales, Lane and Porch (2002) find that, in introductory accounting, performance can partially be explained by reference to factors in the students’ pre-university background. However, these factors are not significant when the student progresses to upper level accounting classes. In addition, using another measure, pre-university examination performance, Gist, et al. (1996) find no significant association between academic performance and performance in accounting courses at the university level. Because accounting is a subject area that requires accumulation of prior knowledge and considerable quantitative skills, several studies have investigated the impact of prior exposure to mathematical background and accounting courses on performance in college accounting courses. The results are inconclusive. On the one hand, some studies (for example, Baldwin and Howe 1982; Bergin 1983; and Schroeder 1986) find that performance is not significantly associated with prior exposure to high school accounting education. On the other hand, some later studies (for example, Eskew and Faley 1988; Bartlett et al. 1993; Gul and Fong 1993; Tho 1994; Rohde and Kavanagh 1996) find that prior accounting knowledge, obtained through high school education, is a significant determinant of performance in college-level accounting courses. Ambiguity is also present with respect to the influence of mathematical background on performance in accounting courses. For example, Eskew and Faley (1988) and Gul and

Journal of Applied Business and Economics vol. 16(5) 2014 87 Fong (1993) suggest that students with strong mathematical backgrounds outperform students with weaker mathematical backgrounds. By contrast, Gist et al. (1996) do not report the same results. Additionally, Guney (2009) suggests that grades in secondary education mathematics are a very strong determinant of performance in accounting but only for non-accounting majors. Bartlett et al. (1993) concluded that very few educational, demographic or financial characteristics variables appear to have a significant influence on student performance in university accounting examinations. Gracia and Jenkins (2003) observe that students who actively demonstrate commitment and self-responsibility towards their studies tend to do well in formal assessments. Accordingly, they agree with Bartlett et al. (1993) that intervening variables, rather than demographic variables, may be important determinants of student performance in university accounting examinations. They are also in agreement with Lane and Porch (2002) who suggest that other important factors like student motivation may explain student performance. The influence of motivation and effort on student performance has been studied. Pascarella and Terenzini (1991) report that motivation and effort, among other factors, significantly influence individual performance in college. However, using self-reported data, Didia and Hasnat (1998) present counter- intuitive evidence that the more time spent studying per week, the lower the grade in the introductory finance course. However, the significance of this counter-intuitive result was at the weakest level (.10), appeared in only one of the four models they used, and most likely was due to the fact that they did not control for prior actual ability (i.e. GPA) even though it was one of their study variables. In this study, the author uses two prior actual ability factors (GPA and the Grade in Intermediate Accounting I) for control purposes. Also, using self-reported data, Nofsinger and Petry (1999) find no significant relationship between effort and performance. In contrast, Johnson et al.(2002) utilize computerized quizzes and analyze the effect of objectively measured effort on student performance. Their evidence shows that, after controlling for aptitude, ability, and gender, effort remains significant in explaining the differences in performance. Additionally, Maksy and Zheng (2008) find that the grade the student intends to earn (which they used as a proxy for motivation) in Advanced Accounting and Auditing courses is significantly associated with the student’s performance in those two courses. In recent years, there has been increased interest in studying the influence of intervening variables on student performance. Paisey and Paisey (2004) and Guney (2009) show there is a clear positive relationship between attendance and academic performance. Paisey and Paisey also report that the most frequently cited reason for not attending classes was students’ participation in part-time employment. Similarly, Lynn and Robinson-Backmon (2005) find a significant adverse association between employment status and learning outcomes. These authors also indicate that a student’s self-assessment of course learning objectives is significantly and directly related to grade performance. In contrast, Maksy and Zheng (2008) find no significant negative association between the number of hours of work per week and student performance in Advanced Accounting and Auditing courses. Schleifer and Dull (2009) address metacognition in students and find a strong link between metacognitive attributes and academic performance. Metacognition is frequently described as “thinking about thinking” and includes knowledge about when and how to use particular strategies for learning and for problem solving. Despite the fact that prior research has been largely inconclusive or replete with conflicting results, it is not the objective of this study to resolve this diversity of results. The literature review is conducted to show what was done in the past in relation to student performance and to make sure that this study does not repeat a prior study but adds to what was done. The author hopes, in this study, to provide more insight on those areas in which there was general agreement. Since motivation and effort has generally been positively associated with student performance, this study tries to test whether some new selected motivation factors affect student performance. The study also looks at several factors which are commonly viewed as possibly distracting students from performing well and tests whether indeed they are negatively affecting student performance. Moreover, the study investigates the impact of two specific measures of prior abilities on student performance, and also uses them as control variables while testing for the association between motivation and distraction factors and student performance in the Intermediate II course.

88 Journal of Applied Business and Economics vol. 16(5) 2014 STUDY OBJECTIVES AND HYPOTHESES DEVELOPMENT

The first objective of the study is to investigate the association between three selected motivation factors (the grade the student intends to earn in the course, the student’s intention to take the CPA examination, and the student’s intention to attend graduate school) and the student’s performance in the Intermediate Accounting II course in a commuter school and a residential school to determine if the results are generalizable to both types of schools. Commuter schools in the United States are those that do not have any organized on-campus housing for the students. Students live at their privately-owned or rented housing and commute to school using public transportation (trains and/or busses) or their private vehicles. At residential schools, a majority of the students live in organized housing on campus (university-owned dormitories) or in private housing (surrounding the campus) that is approved by the university housing administration. Students walk to the classrooms and do not use any public or private transportation. Student performance is measured in two ways: (1) the letter “grade” and (2) the total “points” (including quizzes, mid-term exams, term projects and the final exam before any upward curving made by the faculty) earned in the course. The author expects a significant association between each of these motivation factors and student performance in the Intermediate Accounting II course whether students attend a commuter or a residential school. The students were asked “what grade do you intend to earn in this course?” A student whose answer is “an A” is assumed to be motivated (for whatever reasons) to study hard to earn an A. Also, a student whose answer is “at least a B” is motivated but not as strongly as a student whose answer is “an A.” On the other hand, a student whose answer is “a C is fine with me” appears to be not that motivated at all. With respect to the second motivation variable, the assumption is that students who intend to sit for the CPA examination are more motivated (to study hard to be able to pass that exam) than students who do not intend to sit for the CPA exam. Similarly, for the third motivation variable, the assumption is that students who intend to go to graduate school are more motivated (to study hard to be able to get accepted at a good graduate school) than students who do not intend to go to graduate school. The second objective of the study is to investigate the association between three selected distraction factors (the student’s number of working hours per week, the student’s type of job if it is unrelated to accounting or business, and the student’s number of courses taken per semester) and the student performance. The assumption is that if the number of work hours per week is too high, the student will not have enough hours to devote to the study of the Intermediate Accounting II course (as well as the other courses the student is taking) and, thus, the student’s performance in this course will suffer, i.e., it will be lower than if the student was not working that many hours or was not working at all. The author also assumes that if the student’s job is related to accounting the student may gain some practical accounting experience that might compensate for the fact that the student is not devoting enough hours to his or her study. In this case, the student’s performance may not be affected negatively as when the student’s job type is not related to accounting at all. Furthermore, the author assumes that if the student is taking too many courses (i.e., more than the usual average number of courses per semester) the student’s performance in these courses (including the Intermediate Accounting II course) will be affected negatively because the student will not be able to devote the appropriate number of hours of study for each course. In light of the above discussion, the author expects that if the student’s number of work hours per week is too high, and/or the type of the student’s job is not related to accounting, and/or the number of courses taken per semester is too high, there will be a significant negative association between these distraction factors and student performance. Of course, distraction factors may offset each other, thereby cancelling out any single factor’s effect. For example, a student who works too many hours per week may take fewer courses, and vice versa, so that there is no negative effect on performance. Similarly, residential school students may work less hours per week but take more courses each semester, while commuter school students may work more hours per week and take fewer courses per semester. For this reason, the study will test the effect of each distraction factor on student performance while once

Journal of Applied Business and Economics vol. 16(5) 2014 89 controlling for the other two factors and another time controlling for the other two factors as well as the prior actual ability factors (the grade in Intermediate Accounting I and overall GPA). The third objective of the study is to investigate whether students make reasonably accurate evaluations of their writing, math, reading, and listening abilities. If they make reasonably accurate evaluations of these abilities, we would expect positive and significant associations between these abilities and students’ performance in the Intermediate Accounting II course. On the other hand, if there are no positive and significant associations between these abilities and students’ performance, this would indicate that students do not make reasonably accurate evaluations of their abilities. In this case, instructors need to continuously give the students feedback about their performance in the course throughout the semester, so students can self- improve. Without such feedback, the author argues that most students will over-estimate their own abilities in these areas and rate them as either “good” or “very good” rather than “average” or “poor.” As indicated in the literature review above, almost all prior studies showed positive and significant associations between prior ability factors (most commonly GPA) and student performance in college courses. The author expects this to be the case in this study as well. With regard to all three objectives of this study, the author uses two prior actual ability factors (the student’s grade in Intermediate Accounting I and the student’s overall GPA) to control their impact on student performance in the Intermediate Accounting II course. Based on the above discussion, the author formulates the following hypotheses:

Motivation Factors: H1: There is a significant association between the grade the student intends to earn and student performance. This is the case whether the student attends a commuter or a residential school.

H2: There is a significant association between the student’s intention to take the CPA Exam and student performance. This is the case whether the student attends a commuter or a residential school.

H3: There is a significant association between the student’s intention to attend graduate school and student performance. This is the case whether the student attends a commuter or a residential school.

Distraction Factors: H4: There is a significant negative association between the student’s number of working hours per week and student performance. This is the case whether the student attends a commuter or a residential school.

H5: There is a significant negative association between the student’s type of job (if it is not related to accounting) and student performance. This is the case whether the student attends a commuter or a residential school.

H6: There is a significant negative association between the student’s number of courses taken per semester and student performance. This is the case whether the student attends a commuter or a residential school.

Self-Perceived Ability Factors: H7: There are no significant associations between the student’s self-perceived (a) writing, (b) math, (c) reading, and (d) listening abilities and student performance in the Intermediate Accounting II course. This is the case whether the student attends a commuter or a residential school.

90 Journal of Applied Business and Economics vol. 16(5) 2014 Control Factors: H8: There is a significant association between the grade the student earned in Intermediate Accounting I and student performance. This is the case whether the student attends a commuter or a residential school.

H9: There is a significant association between the student’s overall GPA and student performance. This is the case whether the student attends a commuter or a residential school.

METHODOLOGY

Survey Questionnaire The author modified a list of survey questions, from Ingram et al. (2002), to include, besides the study variables, some demographic and other information, and distributed it to students in the Intermediate Accounting II course at a commuter school and a residential school. For ethical, confidentiality, and potential risk issues pertaining to participants, the author had to submit a comprehensive 10-page application (together with a copy of the survey instrument) to the University’s Institutional Review Board (IRB) for approval. Prior to that, the author had to take the National Institute of Health (NIH)’s training course titled “Protecting Human Research Participants,” and pass the test given at the end of the course. The certificate of completion of the course was required to be submitted with the application to the University’s IRB. The University’s IRB required the author to include the statement “participation in the survey is completely voluntary” in the survey instructions.

Data Collection and Measurement of Variables The data on the survey questionnaire were collected from all of the 96 students enrolled in the Intermediate Accounting II course at a commuter school and all of the 42 students enrolled in the same course at a residential school. Other than the fact that one school is a commuter school and the other is a residential one, the author selected two schools that are very similar in many respects. First, each school enrolls about 10,000 students, and the College of Business in each school enrolls about 1600 students. Second, both schools are public (or state-supported) universities where public access is a major part of their mission statements. According to the College Board, there are 502 four-year public universities (with enrollment greater than 2000 students) in the United States of America. Of these 502 universities, 246 are residential (most students live on campus) and 256 are commuter universities (See https://bigfuture.collegeboard.org/college-search.) The College Board is a highly respected not-for-profit organization committed to excellence and equity in education in the US. The Board’s mission is to connect students to college success and opportunity (See http://about.collegeboard.org/). Excluding the flagship state university of each of the 50 states (because of exceptionally large student body, high academic rigor, etc.,) the two schools used in the study are representative of about 450 public universities in the U.S. Third, at both universities, faculty members are represented by a union that negotiates compensation and work conditions with the state on behalf of the faculty. With minor exceptions, each faculty member receives the same percent salary increase (if any) each year. Fourth, both universities are non-AACSB accredited but both are in the AACSB candidacy stage, i.e., both received a letter from the Association to Advance Collegiate Schools of Business (AACSB International) notifying them that their application for accreditation has met the minimum requirements and they are candidates for accreditation). Fifth, both universities are located either in or very near one of the largest cities in the United States. Thus, because of the major similarities between the two schools, the author assumes that any differences in the study results, if any, between the two schools should be largely attributed to the fact that one university is a commuter and the other is a residential school. The data was collected in fall 2010 from three sections of the Intermediate Accounting II course offered at the commuter school, and in spring 2011 from two sections of the same course offered at the residential school. All five sections in both schools were taught by the same instructor and, thus, instructor’s effect, if any, on the results at each school should not be a major concern. Because a small number of students failed to list their identification

Journal of Applied Business and Economics vol. 16(5) 2014 91 (ID) numbers on the questionnaire, their responses were excluded from the study. The final sample included 93 useful responses from the commuter school and 40 from the residential school. While all the data representing the independent variables are primary data, the author verified the data representing the control variables (student grades in Intermediate Accounting I and overall GPAs) with the school records using only the students ID numbers (for confidentiality reasons) and with the permission of the Dean of the College of Business. The data representing the two dependent variables (the letter “grade” and total “points” received for the course) were obtained directly from the faculty teaching the course, again using only students ID numbers for confidentiality concerns.

Data Analysis To test the hypotheses, the author used statistical methods that are similar to those used in Maksy and Zheng (2008) which was similar to this study but was conducted at a commuter school only. The author used One-Way Analysis of Variance (ANOVA), and regression analysis to determine the potential associations between the 12 independent variables and the two dependent variables. Because the dependent variable “grade” is ordinal, the author used the Spearman correlations non-parametric test to determine the potential associations between “grade” and the independent variables. The author used the Pearson correlations test to determine the potential associations between “points” and the independent variables. To control for the prior actual ability factors, the grade earned in Intermediate Accounting I (GIA1) and the overall Grade Point Average (GPA), the author used partial correlations. Because the number of job hours (JHours) per week, the job type (JType), and the course load (CLoad) per semester may offset the effect of each other on student performance, the author used partial correlations to determine the association between student performance and JHours while controlling for JType and CLoad. The author repeated the same process to determine the association between student performance and JType while controlling for JHours and CLoad, and the association between student performance and CLoad while controlling for JHours and JType. Furthermore, the author repeated the above three processes while controlling for GIA1 and GPA in addition to the two distraction factors.

RESULTS OF THE STUDY

TABLE 1 presents the ANOVA results using “grade” and TABLE 2 presents the ANOVA results using “points” as a measure of student performance. TABLE 3 presents Spearman correlations for “grade” and TABLE 4 presents Pearson correlations for “points.” TABLE 5 presents partial correlations for “grade” while controlling for GIA1 and GPA and TABLE 6 presents partial correlations for “points” while controlling for the same prior actual ability variables. TABLE 7 presents regression analysis of the 12 independent variables on “grade” and TABLE 8 presents regression analysis of the 12 independent variables on “points.” Part A of TABLE 9 presents partial correlations for each distraction factor with “grade” while controlling for the other two distraction factors and Part B presents partial correlations for each distraction factor with “grade” while controlling for the other two distraction factors as well as GIA1 and GPA. Part A of TABLE 10 presents partial correlations for each distraction factor with “points” while controlling for the other two distraction factors and Part B presents partial correlations for each distraction factor with “points” while controlling for the other two distraction factors as well as GIA1 and GPA. The author analyzes below the results of the study by the type of factors investigated.

Motivation Factors Associated with Student Performance At the commuter school, as TABLES 1 to 8 indicate, of the three motivation variables discussed in H1 to H3, one variable, the grade the student intends to earn in the course, is significantly associated (at the .01 significance level) with student performance (defined as “grade” or as “points) under all tests even after controlling for the prior actual ability factors (GIA1 and GPA). As TABLES 1 to 4 indicate, another motivation variable, intention to take the CPA exam, is also significantly associated with student performance (defined as “grade” or “points”) but at a lower significance level (.05 or .10) under the ANOVA and correlation tests. However, as TABLES 5 and 6, after controlling for the prior actual ability

92 Journal of Applied Business and Economics vol. 16(5) 2014 factors, this significant association totally disappeared. The third motivation variable, intention to attend graduate school, is significantly associated with student performance (at the .05 significance level when performance is defined as “grade” and at the .10 level when it is defined as “points”). After controlling for the prior ability factors, the association either became weaker (.10 level when performance is defined as “grade”) or totally disappeared (when performance is defined as “points”). At the residential school, as TABLES 1 to 8 indicate, of the three motivation variables discussed in H1 to H3, one variable, the grade the student intends to earn in the course, is significantly associated with student performance. That association is stronger (at the .01 significance level however performance is defined) under the ANOVA and correlation tests but weaker (at the .05 level when performance is defined as “points’ and the .10 level when it is defined as “grade”) under the regression tests. However, as TABLES 5 and 6 indicate, after controlling for the prior actual ability factors, (GIA1 and GPA) this strong significant association totally disappeared. As TABLES 1 to 6 indicate, the second motivation variable, intention to take the CPA exam, is also significantly associated with student performance (only when it is defined as “points”) but at a lower significance level (.10 under the ANOVA and .05 under the Pearson correlation test.) However, after controlling for the prior actual ability factors, this significant association totally disappeared. The third motivation variable, intention to attend graduate school, is not significantly associated with student performance (however defined) under any test. The above discussion indicates that the statistical analyses provide some support to H1 (that there is a significant association between the grade the student intends to earn and student performance) but only at the commuter school. The statistical analyses do not provide support to H2 and H3 at either school. This means that intentions to take the CPA exam and/or to go to graduate school are not motivating students to study hard to earn high grades in the Intermediate Accounting II course at either school. In other words, while most students at both schools responded that they intend to take the CPA exam and/or go to graduate school most of them did not earn high grades. It is not quite clear why this is the case. One explanation is that there is no penalty for responding yes for intention to take the CPA exam and/or go to graduate school. So, few students responded “may be” and even fewer responded “no.” In the end, just a few students received an “A” for the course.

Distraction Factors Associated with Student Performance At the commuter school, as TABLES 1-8 indicate, with the exception of a moderate (at the .05 level) significant negative association between course load and student performance (defined as “grade” or “points”) only under the regression tests, none of the three distraction factors has any significant negative association with student performance. After controlling for the other two distraction factors (JHours and JType) as well as prior ability factors (GIA1 & GPA), course load still has a weak (i.e., significant only at the .10 level) negative association with student performance but only when it is defined as “points.” At the residential school, the associations between distraction factors and student performance are not quite as clear as at the commuter school. For example, the regression tests show negative association (significant at the .01 level when performance is defined as “grade” and at the .05 level when it is defined as “points”) between job hours and performance. However, the correlation tests do not show these negative associations between job hours and student performance until the author controls for the prior ability factors or for the other two distraction factors as well as the prior ability factors. The negative association between job hours and student performance is more significant (at the .05 level) when student performance is defined as “points” than when it is defined as “grade (.10 level.) As to the association between job type and student performance, only the regression tests show positive association (at the .05 ;level) and only when student performance is defined as “grade.” When the author controls for the other two distraction factors only or the other two distraction factors as well as the prior ability factors, these moderate positive associations between job type and student performance appear even when student performance is defined as ‘points.” The ANOVA tests and correlations tests show positive (not negative) association between course load and student performance (mostly at the .05 level of significance) even when the author controls for the other two distraction factors. However, once the author controls for prior ability factors, these positive associations between course load and performance totally disappear.

Journal of Applied Business and Economics vol. 16(5) 2014 93 In light of the above discussion, the author can generally state that the statistical analyses provide support to H5 (especially at the residential school) but do not provide support to H4 or H6. An exception to this general statement is that, there is an indication that the students at the residential school who have an accounting-related job will have better performance in the Intermediate Accounting II course than students whose jobs are non-accounting-related (even if they carry the same course load and work about the same number of hours per week.)

Self-Perceived Abilities Factors Associated with Student Performance At the commuter school, as TABLES 1 to 8 indicate, the self-perceived writing ability has no significant association with student performance (however defined) under any test. The self-perceived reading ability has a significant association (at the .05 level) with student performance (but only under the correlation test and only when student performance is defined as “grade.”) However, after controlling for the actual ability factors, that association totally disappeared. The self-perceived math ability has a significant association (at the .01 level under the correlation test and the .05 level under the ANOVA and regression tests) with student performance (but only when student performance is defined as “grade.”) However, after controlling for the actual ability factors, the significance of the association under the correlation test decreased from the .01 level to the .05 level. The self-perceived listening ability has a significant association under the ANOVA and correlation tests (at the .01 level when student performance is defined as “grade” and at the .05 level when student performance is defined as “points.”) However, after controlling for the actual ability factors, the significance of the association under the correlation tests totally disappeared. At the residential school, as TABLES 1 to 8 indicate, the self-perceived ability factors have sporadic and rare associations with student performance. For example, the self-perceived listening ability has no significant association with student performance (however defined) under any test. The self-perceived writing ability has a significant association with student performance (but only at the .10 level and only under the regression test when student performance is defined as “grade.”) Similarly, the self-perceived reading ability has a significant association with student performance (but only at the .10 level and only under the ANOVA test when student performance is defined as “grade.”) Finally, the self-perceived math ability has a significant negative association (at the .05 level) with student performance (but only under the regression test when student performance is defined as “points.”) While the positive, even though weak, associations between self-perceived writing and reading abilities and student performance are understandable, the negative association between the self-perceived math ability and student performance is surprising and counter-intuitive. Possibly, this counter-logical association is a statistical anomaly, but most likely it is caused by the fact that students with lower performance in Intermediate Accounting II have substantially over-estimated their self-perceived math abilities by checking the top-rated “very good” response or the second-highest “good” response. A cross-tabulation analysis between “points” and math ability (which is available from the author upon request) shows that of the 40 students completing the survey, 19 (or 47.5%) checked the top-rated “very good” response, 20 students (or 50%) checked the second highest response “good” and only one student (or 2.5%) checked “average” No students checked “poor” about how they feel about their math ability. Furthermore, of the 19 students who checked the top-rated “very good” response no one earned 90 points or more (out of 100), only three (or 16%) received between 80 and 89 points, eight (or 42%) received between 70 and 79 points, six (or 32%) received between 60 and 69 points, and two (or 10%) received less than 60 points. Similarly, of the 20 students who checked the second highest “good” response only two (or 10%) received 90 or more points, only two (or 10%) received between 80 and 89 points, four (or 20%) received between 70 and 79 points, nine (or 45%) received 60 and 69 points, and three (or 15%) received less than 60 points. The only student who checked “average” received less than 60 points.

Prior Actual Ability Factors Associated with Student Performance At the commuter school, as TABLES 1-4, 7, and 8 indicate, the two variables representing prior actual ability (GIA1 and GPA) have significant associations, at the .01 level, with student performance

94 Journal of Applied Business and Economics vol. 16(5) 2014 (however defined). However, that is not the case at the residential school. For example, the regression tests do not show any significant associations between GIA1 and GPA and student performance. Also, the ANOVA tests do not show any significant associations between GPA and student performance. Only the correlation tests show significant associations (at the .01 level) between GIA1 and GPA and student performance. The ANOVA tests show significant associations, at the .01 level, between GIA1 and student performance when it is defined as “grade” and at the .05 level when it is defined as “points.”

CONCLUSIONS AND RECOMMENDATIONS

One general conclusion of the study is that residential school students may not be as motivated as commuter school students to work hard to earn higher grades in the Intermediate Accounting II course. More specifically, all the tests used in the study provided strong evidence that the majority of the commuter school students who responded that they intend to earn high grades in the Intermediate Accounting II course ended up earning high grades. On the other hand, the study provided only moderate to weak evidence that this was the case with the residential school students. While a larger percentage of the residential school students (than the percentage of the commuter school students) responded that they intended to earn high grades in the Intermediate Accounting II course, a smaller percentage ended up earning such high grades. This indicates that the majority of the students were not really motivated enough to work hard to earn high grades. Other than the above difference, the study results are equally generalizable to commuter and residential schools. For example, speaking of motivation, intention to take the CPA examination and intention to pursue graduate studies do not seem, in this study, to be good motivating factors for either commuter school or residential school students to perform well in the Intermediate Accounting II course. In light of the above general conclusion, the author recommends that, while accounting faculty (at both types of schools) should find ways to motivate their students to study hard to earn high grades, they should keep in mind that informing students to plan to sit for the CPA exam or get admitted to a good graduate school may not be good motivating factors. Thus, accounting faculty should think of other motivating factors that are not tested in this study. Another general conclusion of the study is that, with some limited exceptions noted below, the distraction variables (i.e., working too many hours per week, working in non-accounting related jobs, and taking too many courses per semester) have no significant negative associations with student performance at either the commuter or residential school. That is, they are not distracting the students and preventing them from earning high grades in the Intermediate Accounting II course. One exception to this general conclusion is that there is a moderate evidence (but only under the regression analysis) of a negative association between the course load per semester and student performance at the commuter school. Another exception is that there is a strong evidence (especially when performance is measured as “points) of a negative association between job hours per week and student performance at the residential school. Specifically, of the students who carry the same course load and have the same type of job, those who work more hours have significantly lower performance than those who work less hours or do not work at all. Surprisingly, again only at the residential school, of the students who carry the same course load and work about the same number of hours per week, those whose jobs are accounting-related perform significantly better than those whose jobs are not accounting related. Also, of the students who work in the same type of job and work about the same number of hours per week, those who carry a higher course load perform significantly better than those who carry a lower course load. In light of this conclusion, the author recommends that accounting faculty, when advising their students, should realize that working as few hours as possible will not necessarily lead to earning higher grades and working too many hours (within a relevant range of, let us say, zero to 40 hours a week) will not necessarily lead to earning lower grades. So, faculty need not automatically advise students with lower grades to significantly reduce their work hours, especially if the students have to work anyway to support themselves and/or their families. This is so because lower working hours will not necessarily and automatically lead to higher grades since students may not automatically devote the extra time to studying

Journal of Applied Business and Economics vol. 16(5) 2014 95 or they may have wrong study habits that they need to fix. Furthermore, if students have to work a significant number of hours anyway (even in non-accounting related jobs) to support their families, accounting faculty need not encourage those students to take as few courses per semester as possible, because higher course loads do not seem to lead to lower grades in the Intermediate Accounting II course. On the contrary, there is evidence that higher course loads lead to higher grades at the residential school. A third general conclusion of the study is that, with some minor exceptions noted below, students at both the commuter and residential schools seem to over-estimate their own writing, math, reading, and listening abilities. This may be the case because students taking the Intermediate Accounting II course are not tested in these abilities and are not given feedback about these abilities. Lack of testing and continuous feedback, or delaying the feedback to the end or even close to the end of the semester, may put the students under the impression that they are doing just fine and, thus, they may not make any special effort to improve their performance. One exception to this general conclusion is that there is moderate evidence that students at the commuter school make accurate evaluation of their math ability. Conversely, there is moderate evidence (but only under the regression test) that at the residential school students who earned low points in the Intermediate Accounting II course over-estimated their math abilities than student who earned high points. Giving continuous feedback to students about their writing ability (or about any other abilities in any other course for that matter) helps students improve their overall performance in the course. In light of this conclusion the author recommends that the college of business faculty in general, and accounting faculty teaching the Intermediate Accounting II course in particular, should give continuous feedback to the students at least about their writing and quantitative abilities. This may require faculty, who usually give one or two mid-tem exam(s) in addition to the final exam, to think about giving short weekly quizzes to continuously evaluate student performance. If the class time devoted to these many quizzes is an issue, faculty may consider a combination of in-class and take-home quizzes, or perhaps use an on-line homework system that is now provided by many textbook publishers. The author realizes that some faculty may already be doing this; thus, these recommendations are for those who may not be. As expected and as shown in prior studies with respect to other courses, a fourth general conclusion of the study is that students with high prior actual ability end up earning high grades in the Intermediate Accounting II course at both schools. Specifically, the study provides strong evidence that students’ GPA, and more significantly, their performance in Intermediate Accounting I (particularly at the commuter school), are strong predictors of their performance in the Intermediate Accounting II course.

STUDY LIMITATIONS AND SUGGESTIONS FOR FURTHER RESEARCH

This study is subject to some limitations. One limitation is that the two schools selected for the study school are public (i.e., state-owned or state-supported) universities and, therefore, the results may not be the same for private schools. There are about 430 four-year, for-profit, medium-size (enrollment between 2000-15000 students), private universities in the U.S. (see https://bigfuture.collegeboard.org/college- search). Thus, one suggestion for further research is to replicate the study using two private schools that are representative of the majority of private schools. Another limitation is that the study sample for the residential school is somewhat small relative to the number of variables analyzed and, hence, the results may not be as robust as they would have been if that sample was larger. Therefore, another suggestion for further research is to replicate the study using a somewhat larger sample for the residential school.

REFERENCES

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96 Journal of Applied Business and Economics vol. 16(5) 2014 Bergin, L. J. (1983), “The Effect of Previous Accounting Study on Student Performance in the First College-level Financial Accounting Course”, Issues in Accounting Education, vol.1, pp. 19-28. Buckless, F. A., Lipe, M. G. and Ravenscroft, S.P., (1991), “Do Gender Effects on Accounting Course Performance Persist After Controlling for General Academic Aptitude?,” Issues in Accounting Education, vol. 6, pp. 248-261. Canlar, M. (1986), “College-level Exposure to Accounting Study and Its Effect on Student Performance in the First MBA-level Financial Accounting Course”, Issues in Accounting Education, vol. 1, pp. 13-23. Chen, C. T., Maksy, M. and Zheng, L. (2009), “Factors Associated with Student Performance in Intermediate Accounting II and Contemporary Financial Accounting Issues,” International Journal of Education Research, Vol. 4, No.2, (Spring 2009), pp. 56-71. Didia, D. and Hasnat, B. (1998), “The Determinants of Performance in the University Introductory Finance Course”, Financial Practice and Education, vol. 1, pp. 102-107. Doran, B., Bouillon, M. L. and Smith, C.G. (1991), “Determinants of Student Performance in Accounting Principles I and II”, Issues in Accounting Education, vol. 6, pp.74-84. Duff, A. (2004), “Understanding Academic Performance and Progression of First-year Accounting and Business Economics Undergraduates: The Role of Approaches to Learning and Prior Academic Achievement”, Accounting Education, (December), vol. 13, pp. 409-430. Eckel, N. and Johnson, W.A. (1983), “A Model for Screening and Classifying Potential”, Accounting Education, vol. 2, pp. 1-15. Eskew, R. K. and Faley, R. H. (1988), “Some Determinants of Student Performance in the First College- level Financial Accounting Course”, The Accounting Review (January), pp.137-147. Gammie, E., Paver, B., Gammie, B, and Duncan, F. (2003), “Gender Differences in Accounting Education: An Undergraduate Exploration”, Accounting Education (June), vol. 12, pp. 177-197. Gist, W. E., Goedde, H. and Ward, B.H. 1996, “The Influence of Mathematical Skills and Other Factors on Minority Student Performance in Principles of Accounting”, Issues in Accounting Education, vol. 1, pp. 49-60. Gracia, L. and Jenkins, E. (2003), “A Quantitative Exploration on an Undergraduate Accounting Programme of Study”, Accounting Education (March), vol. 12, pp. 15-32. Gul, F. A. and Fong. S. C. (1993), “Predicting Success for Introductory Accounting Students: Some Further Hong Kong Evidence”, Accounting Education: an international journal, vol. 1, pp. 33- 42. Guney, Y. (2009), “Exogenous and Endogenous Factors Influencing Students’ Performance in Undergraduate Accounting Modules”, Accounting Education (February), vol. 18, pp. 51-73. Hicks, D. W. and Richardson, F. M. (1984), “Predicting Early Success in Intermediate Accounting: The Influence of Entry Examination and GPA”, Issues in Accounting Education, (Spring), pp. 61-67. Ingram, R. W. and Peterson, R. J. (1987), “An Evaluation of AICPA Tests for Predicting the Performance of Accounting Majors”, The Accounting Review (January), pp. 215-223. Ingram, R. W, Albright, T. L. and Baldwin, A. B. (2002) Financial Accounting—a Bridge to Decision Making. Cincinnati, OH: Thomson South-western. Jackling, B. and Anderson, A. (1998), “Study Mode, General Ability and Performance in Accounting: A Research Note”, Accounting Education: an international journal, vol. 1, pp.33-42. Jenkins, E. K. (1998), “The Significant Role of Critical Thinking in Predicting Intermediate Accounting II Students’ Performance”, Journal of Education for Business, vol. 5, pp. 274-280. Johnson, D. L., Joyce, P., and Sen, S. (2002), “An Analysis of Student Effort and Performance in the Finance Principles Course”, Journal of Applied Finance (Fall/Winter), pp.67-72. Kohl, M. Y. and Kohl, H. C. (1999), “The Determinants of Performance in an Accountancy Degree Course”, Accounting Education: an international journal, vol. 1, pp.13-29. Lane, A. and Porch, M. (2002), “The Impact of Background Factors on the Performance of No specialist Undergraduate Students on Accounting Modules – A Longitudinal Study: A Research Note”, Accounting Education, vol. 1, pp. 109-118.

Journal of Applied Business and Economics vol. 16(5) 2014 97 Lipe, M. G. (1989), “Further Evidence on the Performance of Female Versus Male Accounting Students”, Issues in Accounting Education, vol. 1, pp. 144-152. Lynn, S. and Robinson-Backmon, I. (2005), “An Investigation of an Upper-Division Undergraduate Accounting Course and the Factors That Influence Learning Outcomes”, vol. 13, pp.133-140. Maksy, M. and Zheng, L. (2008), “Factors Associated with Student Performance in Advanced Accounting and Intermediate Accounting II: An Empirical Study in a Public University”, Accounting Research Journal, vol. 21, pp. 16-32. Maksy, M. (2012), “Motivation and Distraction Factors Associated with Student Performance in Intermediate Accounting: An Empirical Investigation,” Journal of Accounting and Finance, Vol. 12, No. 3, pp. 188-208. Mutchler, J. E., Turner, T. H. and Williams, D.D. (1987), “The Performance of Female Versus Male Accounting Students”, Issues in Accounting Education, vol. 1, pp. 103-111. Nosfinger, J. and Petri, G. (1999), “Student Study Behavior and Performance in Principles of Finance”, Journal of Financial Education, (spring), pp. 33-41. Paisey, C. and Paisey, N. (2004), “Student Attendance in an Accounting Module – Reasons for Non- attendance and the Effect on Academic Performance in a Scottish University”, Accounting Education, (December), vol. 13, pp. 39-53. Pascarella, E. and Terenzini, P. (1991), “How College Affects Students: Findings and Insights from Twenty Years of Research”, San Francisco, CA: Jossey-Bass Publisher. Rohde, F. H. and Kavanagh, M. (1996), “Performance in First Year University Accounting; Quantifying the Advantage of Secondary School Accounting”, Accounting and Finance, vol. 2, pp. 275-285. Schleifer, L. and Dull, R. (2009), “Metacognition and Performance in the Accounting Classroom”, (August), vol. 24, pp. 339-367 Schroeder, N. W. (1986), “Previous Accounting Education and College-level Accounting Examination Performance”, Issues in Accounting Education, vol. 1, pp. 37-47. Tickell, G. and Smyrnios, K (2005), “Predictors of Tertiary Accounting Students’ Academic Performance: A Comparison of Year 12-to-University Students with TAFE-to-University Students”, Journal of Higher Education Policy and Management, (July), vol. 27, pp. 239 – 259. Tho, L. M. (1994), “Some Determinants of Student Performance in the University of Malaya Introductory Accounting Course”, Accounting Education: an international journal, vol. 4, pp. 331-340. Tyson, T. (1989), “Grade Performance in Introductory Accounting Courses: Why Female Students Outperform Males”, Issues in Accounting Education, vol. 1, pp. 153-160. Wooten, T. (1998), “Factors Influencing Student Learning in Introductory Accounting Classes: A Comparison of Traditional and Nontraditional Students”, Issues in Accounting Education (May), vol. 13, pp. 357-373.

TABLES

Note: Legend Of Independent Variables In All Tables Below: IG: Intended Grade (the grade the student intends to earn in the course); ICPA: Intention to take the CPA exam; IGS: Intention to attend Graduate School; JHours: Number of Job Hours per week; JType: Type of Job; CLoad: Number of courses taken per semester; Write: Student’s self-perceived writing ability; Math: Student’s self-perceived math ability; Read: Student’s self-perceived reading ability; Listen: Student’s self-perceived listening ability; GIA1: Grade in Intermediate Accounting I; GPA: Overall GPA

98 Journal of Applied Business and Economics vol. 16(5) 2014 TABLE 1 ONE-WAY ANALYSIS OF VARIANCE FOR GRADE (All numbers are for Between Groups Only) Complete ANOVA Numbers are Available from the Author Upon Request

Journal of Applied Business and Economics vol. 16(5) 2014 99 TABLE 2 ONE-WAY ANALYSIS OF VARIANCE FOR POINTS (All numbers are for Between Groups Only) Complete ANOVA Numbers are Available from the Authors upon Request

100 Journal of Applied Business and Economics vol. 16(5) 2014

TABLE 3 SPEARMAN CORRELATION COEFFICIENTS FOR GRADEa

Grade IG ICPA IGS JHours JType CLoad Write Math Read Listen GIA1 GPA

Grade .572*** .175 .205** -.015 -.036 -.022 -.077 .280*** .235** .388*** .464*** .469***

IG .464*** .056 .191* -.024 .117 .030 -.054 .115 .149 .402*** .457*** .354***

ICPA .239 .285* .425*** .067 .061 .121 -.027 .057 .282*** .088 .036 .381***

IGS -.046 .029 .360** -.056 .058 .221** -.009 -.027 .200* -.004 .047 .160

Journal JHours -.111 .162 -.045 .037 .343*** -.417*** .086 -.121 .065 -.018 -.055 .063

JType .110 .139 -.019 .140 .724*** -.218** .103 -.053 .090 .121 .114 -.011 of

Applied CLoad .327** .052 .250 .065 -.279* -.218 -.039 .223** .073 .131 .021 .176*

Write -.046 .440*** .228 .234 -.137 -.252 .223 .158 .343*** .182* -.074 .083

Business Math .149 .230 -.003 .008 .051 .049 .206 .146 .228** .352*** .094 .083

Read .196 .336** .162 .179 -.198 -.089 .000 .518*** -.037 .390*** .085 .352*** and Listen .054 .245 .148 .393** -.151 -.006 .276* .172 .274* .203 .315*** .324*** Economics GIA1 .561*** .379** .263* .114 .264* .185 .338** .108 .046 .136 -.052 .264***

GPA .535*** .328** .337** -.045 .070 .030 .286* .173 .013 .310** -.067 .789***

vol. ***, **, * Indicate significances at .01, .05, and .10 levels respectively. a Commuter school coefficients are above the diagonal and residential school coefficients are under the diagonal. 16(5)

2014

101

102

Journal

of TABLE 4 Applied PEARSON CORRELATION COEFFICIENTS FOR POINTSa

Points IG ICPA IGS JHours JType CLoad Write Math Read Listen GIA1 GPA Business Points .547*** .218** .184* -.025 -.010 -.029 -.106 .157 .156 .241** .466*** .532***

IG -.679*** .096 .199* .006 .132 .016 -.027 .126 .171 .399*** .451*** .381*** and

Economics ICPA .382** .273 .398*** .011 .039 .131 -.002 .051 .288*** .082 .063 .375***

IGS -.075 .056 .391** -.023 .038 .262** -.006 .002 .186* -.022 .030 .144

JHours -.242 .158 -.127 .036 .447*** -.361*** .129 -.138 .067 .007 -.040 .072 vol. JType .073 .071 -.060 .121 .566*** -.223** .116 -.066 .079 .135 .115 -.018 16(5)

CLoad .390** -.094 .314** .016 -.360** .328** -.042 .220** .068 .114 .024 .196* 2014 Write .009 .404*** .173 .272* -.119 -.175 .071 .131 .359*** .165 -.065 .101

Math .014 .244 -.068 .056 .034 .022 .120 .161 .210** .320*** .103 .086

Read .202 .299* .168 .192 -.148 .057 -.090 .570*** -.062 .355*** .081 .360***

Listen .155 .203 .123 .365** -.175 -.003 .146 .273* .264* .250 .319*** .321***

GIA1 .485*** .356** .273* .111 .224 .123 .318** .091 .043 .135 -.059 .264***

GPA .505*** .334** .387** -.054 .020 .021 .317** .199 .008 .292* -.077 .776***

***, **, * Indicate significances at .01, .05, and .10 levels respectively. a Commuter school coefficients are above the diagonal and residential school coefficients are under the diagonal.

TABLE 5 PARTIAL CORRELATION COEFFICIENTS FOR GRADE WHILE CONTROLLING FOR GIA1 AND GPAa

Grade IG ICPA IGS JHours JType CLoad Write Math Read Listen

Grade .350*** .092 .177* -.109 -.083 -.102 -.123 .233** .031 .126

IG .187 -.042 .175* .001 .111 -.058 -.036 .075 .050 .242**

ICPA .083 .176 .376*** -.020 .055 .063 -.048 .024 .176* -.034

IGS -.116 .041 .473 -.035 .043 .241** -.021 -.010 .146 -.073

Journal JHours -.316 .115 -.138 -.047 .462*** -.386*** .118 -.141 .043 -.001

JType .144 .042 -.066 .085 .548*** -.222** .132 -.075 .095 .122 of

Applied CLoad .159 -.249 .227 .006 -.455*** -.382** -.066 .211** -.004 .065

Write -.190 .389** .102 .325** -.098 -.168 .021 .132 .348*** .172

Business Math .149 .250 -.075 .044 .015 .012 .118 .170 .195* .299***

Read .048 .255 .056 .265 -.118 .082 -.186 .540*** -.059 .285*** and Listen .089 .247 .167 .373** -.185 -.001 .181 .297* .266 .289* Economics ______***, **, * Indicate significances at .01, .05, and .10 levels respectively. a Commuter school coefficients are above the diagonal and residential school coefficients are under the diagonal. vol.

16(5)

2014

103 104

Journal

of TABLE 6 Applied PARTIAL CORRELATION COEFFICIENTS FOR POINTS WHILE CONTROLLING FOR GIA2 AND GPAa

Points IG ICPA IGS JHours JType CLoad Write Math Read Listen Business Points .335*** .043 .143 -.055 -.055 .162 -.166 .109 -.042 -.016

IG .163 -.042 .175* .001 .111 -.058 -.036 .075 .050 .242** and

Economics ICPA .246 .176 .376*** -.020 .055 .063 -.048 .024 .176* -.034

IGS -.101 .041 .473*** -.035 .043 .241** -.021 -.010 .146 -.073

JHours -.374** .115 -.138 -.047 .462*** -.386*** .118 -.141 .043 -.001 vol. JType .045 .042 -.066 .085 .548*** -.222** .132 -.075 .095 .122 16(5)

CLoad .267 -.249 .227 .006 -.455*** -.382** -.066 .211** -.004 .065 2014 Write -.093 .389** .102 .325** -.098 -.168 .021 .132 .348*** .172

Math .002 .250 -.075 .044 .015 .012 .118 .170 .195* .299***

Read .094 .255 .056 .265 -.118 .082 -.186 .540*** -.059 .285***

Listen .228 .247 .167 .373** -.185 -.001 .181 .297* .266 .289*

______***, **, * Indicate significances at .01, .05, and .10 levels respectively. a Commuter school coefficients are above the diagonal and residential school coefficients are under the diagonal.

TABLE 7 REGRESSION ANALYSIS FOR GRADE

Journal of Applied Business and Economics vol. 16(5) 2014 105 TABLE 8 REGRESSION ANALYSIS FOR POINTS

106 Journal of Applied Business and Economics vol. 16(5) 2014

TABLE 9 PARTIAL CORRELATION COEFFICIENTS OF EACH DISTRACTION FACTOR WITH GRADEa

Part A Part B

Grade JHours JType CLoad Grade JHours JType CLoad

Grade -.068 .001 -.019 Grade -.128 -.046 -.159

- JHours -.257 JHours .436***(.008) Journal JType .379**(.019) JType .409** (.013) of

Applied CLoad .351**(.031) CLoad -.159

______

Business Part A: While controlling for the other two distraction factors.

Part B: While controlling for the other two distraction factors as well as prior actual ability factors (GIA1 & GPA). and ***, **, * Indicate significances at .01, .05, and .10 levels respectively. Exact significance level is in parenthesis. Economics a Commuter school coefficients are above the diagonal and residential school coefficients are under the diagonal.

vol.

16(5)

2014

107

108

Journal

of Applied TABLE 10 a PARTIAL CORRELATION COEFFICIENTS OF EACH DISTRACTION FACTOR WITH POINTS

Business Part A Part B

Points JHours JType CLoad Points JHours JType CLoad and

Economics Points -.033 -.002 -.040 Points -.099 -.045 -.201*(.061)

JHours -.280*(.088) JHours -.420***(.011)

vol. JType .340**(.037) JType .350**(.036)

16(5) CLoad .396**(.014) CLoad .187

2014 ______

Part A: While controlling for the other two distraction factors.

Part B: While controlling for the other two distraction factors as well as prior actual ability factors (GIA1 & GPA).

***, **, * Indicate significances at .01, .05, and .10 levels respectively. Exact significance level is in parenthesis.

a Commuter school coefficients are above the diagonal and residential school coefficients are under the diagonal

Hypermarket Corporate Brand Extension Personality

Hasliza Hassan Multimedia University, Cyberjaya, Selangor, Malaysia

Muhammad Sabbir Rahman International Islamic University Malaysia, Kuala Lumpur, Malaysia

Abu Bakar Sade UCSI University, Kuala Lumpur, Malaysia

INTRODUCTION

Hypermarkets were originally introduced as a modern retailing concept based on self-service. Due to the strong competition within hypermarket retailing, most hypermarket players are trying to provide unique products and services for the consumer. Extending the existing hypermarket corporate brand into a product brand is known as ‘hypermarket corporate brand extension product’. Offering an enhancement to the basic self-service shopping concept to create a better shopping experience is known as ‘hypermarket corporate brand extension service’. Corporate brand extension of products and services is able to provide a competitive edge to a particular hypermarket retailer since the consumers can only purchase and consume them if they go to a particular hypermarket outlet. This research is an extension of a conceptual study in which it is expected that there is a significant relationship between hypermarket corporate brand extension for both products and services with brand personality (Hassan and Rahman, 2012a).

HYPERMARKET CORPORATE BRAND EXTENSION

Hypermarkets are a modern retailing concept that provide everything under one roof. This retailing concept is similar to supermarkets and shopping centres. However, hypermarkets focus more on fast moving consumer products, especially basic household necessities (Hassan, Sade and Rahman, 2013). The extension of an existing corporate brand to a new product or service by using the same brand is known as corporate brand extension. An extension that is using a corporate brand will transfer the

Journal of Applied Business and Economics vol. 16(5) 2014 109 intangible attributes or organizational characteristics to the new product or service. Hence, a positive perception of the corporate credibility, fitness of extension and the attribute of extension will enhance the perception of quality and consumer choice of the corporate brand extension (Keller and Aaker, 1998). Furthermore, the extension of the hypermarket retail brand to a product brand that is available on the shelf as well as the extension of the basic self-service shopping concept into a better shopping experience is known as hypermarket corporate brand extension of the products and services. Brand extension can be categorized into 1) function-oriented, which focuses on performance and 2) prestige-oriented, which focuses on the consumer’s self-image (Pitta and Katsanis, 1995). For this research, the extension of the hypermarket corporate brand of a product is more suitable to be considered as function oriented while the service extension in shopping experience can be considered as a combination of both function-oriented and prestige-oriented, since it enhances the basic functions of hypermarkets and improves the overall performance of the particular hypermarket. Hypermarket retailers will tend to introduce a corporate brand extension once the business manages to grow organically (Burt, Davies, Dawson and Sparks, 2008). The uniqueness of a hypermarket retailing brand is that it can be extended into both the products and services that are offered. It is also impossible to purchase a hypermarket corporate brand extension product or have a similar shopping experience if the consumer does not go to the particular hypermarket outlet. For example, it is impossible to purchase Tesco chilli sauce if the consumer does not go to the Tesco hypermarket. The shopping experience that is offered is also unique because it is impossible for two different hypermarket players to offer exactly the same shopping experience to the consumer. Hypermarket retailers should make an overall improvement rather than focusing on a particular element, since the consumer will assess the hypermarket in general rather than in isolation (Swoboda, Haelsig, Morschett and Schramm-Klein, 2007). Perception, motivation and the importance of having a corporate brand extension by the hypermarket retailer is dependent on the experience. Due to the increasing cost of living, nowadays, consumers are looking for more value for almost all their daily expenses. The availability of hypermarket corporate brand extension of a product that is slightly more affordable than a well-known manufacturing brand has become an alternative for those people who are living on a tight budget or who are price conscious. The enhancement of the basic self-service concept into a better shopping experience has made hypermarkets the best place to purchase basic necessities for the household at an affordable price while enjoying a modern shopping environment. Since there is an expected growth and opportunity of sales performance, gross margin and differentiation, many hypermarket retailers have extended the corporate brand (Au-Yeung and Lu, 2009). This branding concept has been followed by hypermarket players in Malaysia. The introduction of the “1 Malaysia” brand by the local government, which is adopting a similar branding concept has stimulated hypermarket players to aggressively extend the corporate brand (Hassan and Rahman, 2013b).

HYPERMARKET CORPORATE BRAND EXTENSION OF PRODUCT

Hypermarket retailers are able to increase their margin by selling corporate brand extension products rather than only selling well-known brand products (Beldona and Wysong, 2007). One of the unique aspects of hypermarket corporate brand extension products is that it is highly controlled by the particular retailer and cannot be seen in other places. The benefits have encouraged more hypermarket retailers to embark on developing the corporate brand extension (Munusamy and Hoo, 2008). Hypermarkets can prioritise placing the corporate brand extension products on the shelves, which definitely provides competition to the brands of other manufacturers. Producers who do not have a stronger competitive position than the existing manufacturer brands are able to sell their products using the hypermarket corporate brand (Gomez and Rubio, 2008). The product brand is positioned according to the target consumer (Burghausen and Fan, 2002). Consumers usually perceive the product of corporate brand extension as almost the same as the core brand product. The high similarity of corporate brand extension with the core brand products will attract consumers (Buil, de Chernatony and Hem, 2009). Products that are highly related with a brand name are mostly judged as typical (Boush, 1993). Brand reliability will

110 Journal of Applied Business and Economics vol. 16(5) 2014 determine the level of risk of a particular corporate brand extension based on the core brand (DelVecchio, 2000). Thus, a new corporate brand extension product will be accepted if the level of certainty is high (Grime, Diamantopoulos and Smith, 2002). The hypermarket corporate brand extension can be extended to basic household necessities, especially for daily consumable food. Examples of daily consumable food that are usually purchased by the consumers include 1) beverages, such as coffee, cordial drink, soda, tea and juice; 2) carbohydrate products, such as bread, noodles, rice and spaghetti; 3) cereals, such as cornflakes and oats, 4) frozen foods, such as doughnuts, pizza, and spring rolls; and 5) light foods, such as chocolate, cookies and snacks. The theories from Aaker and Keller (1990) as well as Garvin (1987) were adapted to develop the instrument for hypermarket corporate brand extension product. The theory from Aaker and Keller (1990) is known as the dimensions of fit, which consists of 1) transfer, 2) complement, and 3) substitute. Another theory is from Garvin (1987), which is known as the eight dimensions of quality, which consists of 1) performance, 2) features, 3) reliability, 4) conformance, 5) durability, 6) serviceability, 7) aesthetics, and 8) perceived quality.

HYPERMARKET CORPORATE BRAND EXTENSION OF SERVICE

Service is able to give a high return to the company instead of just focusing on the product (Bjurklo, Edvardsson and Gebauer, 2009) since it will enhance the level of satisfaction (Martinez-Ruiz, Jimenez- Zarco and Cascio, 2011). There are many reasons why consumers go shopping (Fiore and Kim, 2007; Sit, Merrilees and Birch, 2003). Shopping activity is not just for the sake of acquiring household necessities, and is also far beyond basic economic needs (Dholakia, 1999). Malaysians tend to shop for leisure and pleasure. Approximately 48 per cent of urban young adults who are between 18 to 44 years old spend their leisure time window-shopping, which represents 49 per cent of the Malaysian population (Lee, 1995). Leisure shopping is a subset of leisure retailing. The perception of leisure shopping depends on the characteristics of the individual, the objectives, social group and the nature of the location (Howard, 2007). Hence, the shopping environment should be enhanced with a variety of other shopping provisions (Hare, 2003). The hypermarket corporate brand extension of services for this research looks at the extension of the basic self-service shopping concept into a better shopping experience. The basic self-service concept can be extended by providing and enhancing the facilities in the hypermarket, such as 1) automatic price checkers; 2) covered parking area; 3) food and beverage area, such as cafeteria, food court, kiosk and restaurant; 4) indoor facilities, such as seats, washrooms and wheelchairs; and 5) safety and security, such as baggage counter, CCTV and assistance from a security officer. The theories from Lages and Fernandes (2005), and Zeithaml, Parasuman and Berry (1990) were adapted to develop instruments for hypermarket corporate brand extension service. The theory by Lages and Fernandes (2005) is known as service personal value (SERPVAL), which consists of 1) peaceful life, 2) social recognition, and 3) social integration. The other theory, which is adapted from Zeithaml, Parasuman and Berry (1990), is known as service quality (SERVQUAL) to rate the quality of service industries. It consists of 1) reliability, 2) assurance, 3) tangible, 4) empathy, and 5) responsiveness.

BRAND PERSONALITY

Brand personality is defined as “the set of human characteristics associated with a brand”, (Aaker, 1997), which refers to human personality traits that are related to a particular brand (Azoulay and Kapferer, 2003). This trait represents the characteristics of an individual (Ferrandi and Valette-Florence, 2002). Brand personality exists when the consumer captures the dimensions of a brand as a person’s personality (Batra, Lehmann and Singh, 1993). It is an association of functional (Maehle and Shneor, 2010; Okazaki, 2006), physical or attribute elements (Maehle and Shneor, 2010), expressive stimulation (Okazaki, 2006) and the self-concept that represents the brand image (Maehle and Shneor, 2010) of hypermarket corporate brand extensions. A brand has a personality that is similar to that of human beings (Louis and Lombart, 2010; Smothers, 1993). Brand personality is able to influence consumer behaviour

Journal of Applied Business and Economics vol. 16(5) 2014 111 since the traits resemble human personality traits (Louis and Lombart, 2010). The brand is acknowledged to have a personality if the consumers view it as being similar to human characteristics (Beldona and Wysong, 2007). Brand personality demonstrates and expresses a consumer’s personality since it is parallel to the individual and perhaps the social self-concept of particular consumers (Kotler and Keller, 2005). People build a relationship with brand that matches the self-concept in the society (Maehle and Shneor, 2010). Hence, personal identification and status will assist in developing the brand. The growth and profitability of the corporate brand extension can be achieved through guarantee and social image (Del Rio, Vazquez and Iglesias, 2001). The brand personality theory from Aaker (1997) consists of five brand personality dimensions – sincerity, excitement, competence, sophistication and ruggedness – each of which has two to four facets, which are further broken down into two to three traits that represent the facet. This research adopts two traits from each of the brand personality dimensions: 1) down to earth, 2) independent, 3) confident, 4) good looking, 5) outdoorsy, 6) cheerful, 7) trendy, 8) intelligent, 9) smooth, and 10) rugged, as measurement items in the brand personality instrument.

RESEARCH METHODOLOGY

A total of 785 survey data were collected from hypermarket consumers throughout Malaysia based on proportionate quota convenience sampling of hypermarket distribution outlets by state. The distribution of hypermarket outlets throughout Malaysia by state is approximately proportionate to the Malaysian population and growth rate by state. This is because hypermarket retailers tend to be attracted to locations where there is a high population and growth rate. Each of the questionnaire surveys was administered by the researcher. In order to ensure the respondents were able to understand all the questions, a brief explanation and guidance was given by the researcher to assist the respondents to understand and provide more precise feedback. Missing data can also be reduced through close monitoring by the researcher. None of the respondents was forced to participate to ensure the feedback was more precise (Hassan and Rahman, 2012c). Statistical Package of Social Sciences (SPSS) was used as a tool for data entry, exploratory factor analysis (EFA) and to test the reliability. Analysis of Moment Structures (AMOS) was also used for confirmatory factor analysis (CFA) and structural equation modeling (SEM).

RESULTS AND ANALYSES

Once the data were entered into SPSS, the expectation and maximization method was used to solve missing data before proceeding with the main analyses. The collected data were analysed through exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM). Exploratory factor analysis was used to determine the underlying measurement items from each construct without losing any crucial information to ensure the data could be easily managed for the following analyses. The reliability of the underlying measurement items was tested using Cronbach’s Alpha (α). Confirmatory factor analysis was used to validate the results from EFA and strengthen the reliability test. The relationship of hypermarket corporate brand extension of product and service with brand personality was analysed through structural equation modeling.

Exploratory Factor Analysis (EFA) All 785 collected data were analysed through exploratory factor analysis. At the beginning of the analysis, 29 measurement items were run concurrently. The estimation value for loading was 0.50. Those measurement items with less than 0.50 in the anti-image correlation table and communalities table were removed. The data were rotated twice to meet the estimated value for all measurement item loadings. The value for the Keiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) for the first rotation was 0.925, followed by 0.911 for the second rotation. Hence, both rotations met the estimation of the KMO value, which is supposed to be more than 0.60 (Pallant, 2007). Parallel to this, the Bartlett’s Test of Sphericity value for both rotations was significant (ρ-value<0.05). Most measurement items were also correlated

112 Journal of Applied Business and Economics vol. 16(5) 2014 with each other with a value of more than 0.30. The final component matrix for the EFA outcome is shown in table 1. Four measurement items loaded onto each component. The four measurement items for the product constructs are durability, serviceability, aesthetics, and perceived quality, while the four measurement items that loaded onto the service constructs are assurance, tangible, empathy and responsiveness. Independent, confident, good looking and trendy loaded onto the brand personality component. The sum of squares for product, service and brand personality are 4.490, 1.919 and 1.262, respectively. The percentages of trace are 37.416% (product), 15.995% (service) and 10.516% (brand personality), which contribute to a total of 63.927% for all components. The reliability for all of the loaded measurement items was tested by Cronbach’s Alpha (α), which is supposed to be more than 0.60 (Nunnally, 1978). The results of the reliability test for all constructs met the estimation value, as shown in table 2. Hence, all twelve underlying measurement items were used as indicators for confirmatory factor analysis (CFA).

TABLE 1 COMPONENT MATRIX

Component Variables Brand Communalities Product Service Personality Durability (P8 ) 0.592 0.695 Serviceability (P9 ) 0.668 0.687 Aesthetics (P10) 0.681 0.702 Perceived quality (P11) 0.635 0.609 Assurance (S5) 0.652 0.572 Tangible (S6) 0.681 0.620 Empathy (S7) 0.654 0.676 Responsiveness (S8) 0.627 0.583 Independent (BP2) 0.632 0.604 Confident (BP3) 0.616 0.731 Good looking (BP4) 0.517 0.666 Trendy (BP7) 0.574 0.526 Total Sum of Square (eigenvalue) 4.490 1.919 1.262 7.671 Percentage of trace 37.416% 15.995% 10.516% 63.927%

TABLE 2 RELIABILITY

Constructs Cronbach Alpha (α) Product 0.774 Service 0.814 Brand Personality 0.692

Journal of Applied Business and Economics vol. 16(5) 2014 113 Confirmatory Factor Analysis (CFA) The results of exploratory factor analysis and the reliability test were validated through confirmatory factor analysis (CFA). The initial measurement path for CFA is as shown in figure 1. This measurement path was modified to enhance the fitness of the model through 1) absolute fit, which consists of chi- square (ρ-value), normed chi-square (CMIN/DF), goodness-of-fit index (GFI) and root mean square error of approximation (RMSEA); 2) incremental fit, which consists of normed fit index (NFI), Tucker-Lewis index (TLI) and comparative fit index (CFI); and 3) parsimony fit, which consists of adjusted goodness of fit index (AGFI). The initial CFA path was modified by adding covariance relationships between errors in both product and service constructs. In addition, the indicators for product and brand personality were each deleted to enhance the fitness of the model, as shown in figure 2. The initial and modified fitness value for absolute fit, incremental fit and parsimony fit is as shown in table 3. All the values meet the estimated statistical fitness value based on a combination of various statistical theories.

FIGURE 1 INITIAL CONFIRMATORY FACTOR ANALYSIS

114 Journal of Applied Business and Economics vol. 16(5) 2014 FIGURE 2 MODIFIED CONFIRMATORY FACTOR ANALYSIS

TABLE 3 MODEL FIT OF CONFIRMATORY FACTOR ANALYSIS

Statistics Indexes Expected Initial Modified Categories Value Value Value Absolute Fit ρ-value Less than 0.0001 0.0001 (Hair et al., 2010; 0.05 Hu and Bentler, 1995; Wheaton et al., 1977) CMIN/DF Within 1 to 11.462 3.730 (Hu and Bentler, 1995; 5 Marsh and Hocevar, 1985) GFI More than 0.882 0.972 (Chau and Hu, 2001; Hair 0.90 et al., 2010) RMSEA Less than 0.116 0.059 (Brown and Cudeck, 0.08 1993; Hair et al., 2010) Incremental Fit NFI More than 0.833 0.959 (Bentler and Bonnet, 0.80 1980) TLI More than 0.799 0.954 (Tucker and Lewis, 1973) 0.80 CFI More than 0.845 0.969 (Bagozzi and Yi, 1988; 0.90 Hair et al., 2010) Parsimony Fit AGFI More than 0.819 0.949 (Chau and Hu, 2001) 0.80

Journal of Applied Business and Economics vol. 16(5) 2014 115 Structural Equation Modeling (SEM) The path diagram for structural equation modeling was developed based on the modified CFA path diagram by changing the covariance line between the product and service to brand personality constructs into a causal line, as shown in figure 3. Similar to the CFA, two additional covariance relationships between errors were added to the product construct in the SEM path diagram to enhance the fitness value of the model, as shown in figure 4. There was no deletion of measurement items for the SEM analysis. The initial and modified fitness value for absolute fit, incremental fit and parsimony fit is as shown in table 4. All the values met the estimated statistical fitness value based on a combination of various statistical theories.

FIGURE 3 INITIAL STRUCTURAL EQUATION MODELING

FIGURE 4 MODIFIED STRUCTURAL EQUATION MODELING

116 Journal of Applied Business and Economics vol. 16(5) 2014 TABLE 4 MODEL FIT OF STRUCTURAL EQUATION MODELING

Statistics Indexes Expected Initial Modified Categories Value Value Value Absolute Fit ρ-value Less than 0.0001 0.0001 (Hair et al., 2010; 0.05 Hu and Bentler, 1995; Wheaton et al., 1977) CMIN/DF Within 1 to 8.923 3.619 (Hu and Bentler, 1995; 5 Marsh and Hocevar, 1985) GFI More than 0.934 0.974 (Chau and Hu, 2001; Hair 0.90 et al., 2010) RMSEA Less than 0.101 0.058 (Brown and Cudeck, 0.08 1993; Hair et al., 2010) Incremental Fit NFI More than 0.898 0.961 (Bentler and Bonnet, 0.80 1980) TLI More than 0.908 0.956 (Tucker and Lewis, 1973) 0.80 CFI More than 0.908 0.971 (Bagozzi and Yi, 1988; 0.90 Hair et al., 2010) Parsimony Fit AGFI More than 0.884 0.950 (Chau and Hu, 2001) 0.80

The relationship analysis of hypermarket corporate brand extension of product and service towards brand personality was based on regression weight. Based on the analysis, there is a significant relationship between hypermarket corporate brand extension of product towards brand personality (ρ- value<0.05). Parallel to this, there is also a significant relationship between hypermarket corporate brand extension of service and brand personality (ρ-value<0.05). This result confirms the previous conceptual study in which a significant relationship was expected between hypermarket corporate brand extension of product and service with brand personality (Hassan and Rahman, 2012a). Table 5 shows the standardized and unstandardized relationships analysis of this research through SEM analysis.

TABLE 5 RELATIONSHIP ANALYSIS RESULT

Constructs Standardized Unstandardized Result(s) Product – Brand personality 0.189 *** Positively significant Service – Brand personality 0.356 *** Positively significant Note: *** ρ-value<0.05

Journal of Applied Business and Economics vol. 16(5) 2014 117 DISCUSSION AND RECOMMENDATIONS

This research has proven that hypermarket corporate brand extension of both products and services is able to influence brand personality. Hence, purchasing and consuming hypermarket corporate brand extension products and services show the individual personality of the consumers. This finding is an extension of previous conceptual studies in which it was expected that both products and services would influence brand personality (Hassan and Rahman, 2012a). As a result of the finding, in order to develop and enhance the brand personality of the hypermarket corporate brand, it is important to emphasize both products and services. This is because, consumers will not go to hypermarkets just to purchase or consume a particular product or service but to purchase and consume both product and service offerings in the hypermarket. Hence, both products and services should be perceived as complementary from the perspective of consumers. This research can be further extended by looking at the impact of brand personality on the hypermarket corporate brand value, which will provide a better insight into how far the hypermarket corporate brand can be extended. The availability of a corporate brand extension product in hypermarkets that is slightly more cost effective than other comparable well-known manufacturing brands is expected to assist society to reduce the expense of basic household necessities, and, at the same time, enjoy the modern shopping experience through the service extension that is provided by the hypermarket.

CONCLUSION

The conceptual research of hypermarket corporate brand extension of both product and service as parallel independent constructs to brand personality (Hassan and Rahman, 2012a) is proven through this research. It is confirmed that there is a significant relationship between hypermarket corporate brand extension of product and brand personality. Hence, purchasing a hypermarket corporate brand product does represent the personality of the consumer. Parallel to this, there is also a significant relationship between hypermarket corporate brand extension of service and brand personality. Therefore, consuming service in hypermarkets also represents the personality of the individual consumers.

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ACKNOWLEDGEMENTS

This research project was supported by the Multimedia University, Malaysia, through an internal research grant. Special appreciation is given to the Research Management Centre of the University for approving this research project under the Mini Fund Research 2013-2014 (Project ID: IP20130829002 / MMUI/130025).

120 Journal of Applied Business and Economics vol. 16(5) 2014

Is Economic Liberalization Causing Environmental Degradation in India? An Analysis of Interventions

Avik Sinha Indian Institute of Management Indore

Joysankar Bhattacharya Indian Institute of Management Indore

INTRODUCTION

Association between global climatic shift and atmospheric emission level has been a topic of interest for researchers around the world for a stint period. Considering the greenhouse gas (GHG) emissions around the world, carbon dioxide (CO2) emission from fossil fuel combustion accounts to nearly 57 percent of the entire GHG emission (Intergovernmental Panel on Climate Change, 2007). Moreover, considering the atmospheric lifetime of GHGs, CO2 can be considered as more harmful than sulphur dioxide (SO2) or nitrogen dioxide (NO2), because atmospheric lifetime of CO2 is as high as 30-95 years (Jacobson, 2005), in comparison with a day to two weeks’ atmospheric lifetime of SO2 (Prospero, 2002), or less than a day in case of NO2 (Beirle, Platt, Wenig & Wagner, 2003). The economic growth that India has achieved over last two decades is a result of fossil fuel based energy consumption (Cheng, 1999). From this perspective, reduction in electricity consumption can in turn reduce the level of atmospheric emission of GHGs. However, this uncomplicated solution is unrealistic in nature, as it may cause harm to the economic growth pattern, as a developing nation like India, cannot resort to such alternatives. Consequently, researchers across the world are looking for a sturdy solution to this problem, before the situation goes out of hand. If we look at the patterns of ongoing and existing research in this domain, we can categorize them into three distinct categories, which we will discuss one by one. While reflecting on the association between environmental degradation and economic growth, the first and foremost hypothesis, which was mostly discussed by researchers, is Environmental Kuznets Curve (EKC) hypothesis. After Kuznets (1955) found inverted U-shaped curvilinear association between income inequality and economic development, Grossman and Krueger (1991) have found its resemblance, while establishing an

Journal of Applied Business and Economics vol. 16(5) 2014 121 association between environmental degradation and economic growth in a free trade regime, and they have coined the term “Environmental Kuznets Curve”. Later on, the study on EKC hypothesis was carried out by several researchers in diverse contexts (Hayami & Ruttan, 1970; Shafik & Bandyopadhyay, 1992; Antle & Heidebrink, 1995; De Bruyn, van den Bergh & Opschoor, 1998; Hill & Magnani, 2002; Dinda, 2004; Klump & Cabrera, 2008; Kijima, Nishide & Ohyama, 2010 along with others). Nevertheless, these studies failed to reach a consensus regarding reaching the turnaround point of EKC, and with graduation of time, they were proved out to be questionable in nature. The second category of research in this field is to formulate a bivariate framework to analyze the association between economic growth and drivers of economic growth, which most of the researchers have identified as energy consumption, electricity consumption, or fossil fuel consumption. This category of research was the first to find out the missing feedback link in EKC hypothesis, which was silent about what can possibly be the negative consequences of environmental degradation on economic growth, in terms of the drivers of achieved economic growth. In this category, the first study was carried out by Kraft and Kraft (1978), who introduced GNP as an indicator of economic growth, while considering the causal association between energy consumption and economic growth of U.S. for 1947-1974. They have found the causal association running from GNP to energy consumption. Subsequent to that, research in this direction was carried out in several contexts, like, for U.S. (1947-1979) by Yu and Hwang (1984), for Tanzania (1960-81) and Nigeria (1960-84) by Ebohon (1996), for India (1955-1990), Pakistan (1955- 1990), Indonesia (1960-1990), Malaysia (1955-1990), Singapore (1960-1990), and Philippines (1955- 1991) by Masih and Masih (1996), for G-7 countries (1950-1992) by Soytas and Sari (2003), for Bangladesh (1971-1999) by Mozumder and Marathe (2007), for China (1971-200) by Shiu and Lam (2004) are few among those. With graduation of time, this bivariate framework was starting to gain obsolesce, and in place of that, multivariate framework of this feedback analysis was gaining significance, like, for U.S. (1974-1990) by Yu and Jin (1992), for Israel (1973-1994) by Beenstock, Goldin and Nabot (1999), for India (1973-1995), Indonesia (1973-1995), Thailand (1971-1995), the Philippines (1971- 1995) by Asafu-Adjaye (2000), for Greece (1960-1996) by Hondroyiannis, Lolos and Papapetrou (2002), for India (1907-2000) by Ghosh and Basu (2006) to name a few. Ozturk (2010) has provided with an extensive literature survey on the nexus between economic growth and energy / electricity consumption. Though divergent results exist considering diverse contexts, the literature mostly shows the evidence that there is an unexplained feedback link between economic growth and drivers of economic growth, with respect to the background of EKC hypothesis. As an extension of the previous category of research, the third category of research had emerged, in which the missing feedback link of EKC hypothesis has been analyzed in a new direction. In this category of research, nexus between GHG emission, economic growth, and the drivers of growth has been analyzed by several researchers. Nordhaus (1977) stated that ignition of fossil fuels brings about emissions of CO2 into the atmosphere, and it stays in the atmosphere for a long while. Owing to the discerning assimilation of emission, the amplified atmospheric accumulation brings about augmented global temperature. This was empirically verified by other researchers as well (Manabe & Wetherald, 1975; Wetherald & Manabe, 1988). In a study of an uneven panel data of 130 countries for 1951-1986, Holtz-Eakin and Selden (1995) have found out that growth in annual emission level will continue at a rate of 1.8% up to 2025. Moreover, in countries with lower per capita income GHG emission rises because of growth in population and industrial development. Later on, studies on this perspective were carried out in several contexts. Kander (2002) attributes energy consumption as the reason behind CO2 emission growth in Sweden, for 1800-2000. Frankel and Rose (2005) have established that trade openness and democracy have positive effect on environmental quality by lowering atmospheric emission level. Soytas, Sari and Ewing (2007) have established a causal association between CO2 emission growth and growth in energy consumption in United States, for 1960-2004. Zhang and Cheng (2009) have established a causal association between CO2 emission growth and growth in energy consumption in China, for 1980-2007. Halicioglu (2009) has established the same in case of Turkey for 1960-2005. Chang (2010) has established that economic growth, which leaves apart other social aspects, results in increase in fossil fuel based energy consumption, and thereby CO2 emission.

122 Journal of Applied Business and Economics vol. 16(5) 2014 This paper investigates causal association between fossil fuel consumption, economic growth, and CO2 emission, using the interventions of industrial value added, energy waste, and urbanization. Span of the study has been taken as 1971-2010, as it covers twenty years before and after economic liberalization. Global rank of India as the third highest energy consuming country after China, the U.S. and fourth highest CO2 emitting country after China, the U.S. and the European Union, makes itself an obvious choice as a context of this study. Although Sinha and Mehta (2014) have identified that CO2 emission and economic growth holds a bidirectional causal association for India, devoid of testing this association being linked with fossil fuel consumption and associating this causality with India’s economic liberalization perspective, may leave out several policy implications, which may prove out to be significantly consequential considering India’s stand regarding environmental degradation. Choice of interventions for this study has been done keeping in mind the economic liberalization perspective of India, and the chosen interventions are (1) industrial development, for which industrial value added has been taken as proxy, (2) energy efficiency, for which combustible energy waste been taken as proxy, and (3) urban development, for which urbanization has been taken as proxy. These interventions can have possible effect on the causal associations to be estimated, at several levels. Likewise, industrial development can have effect on all the causal associations, energy efficiency can have effect on the causal associations concerning fossil fuel consumption, and urbanization can have effect on the causal associations concerning economic growth and CO2 emission. We have analyzed the causal associations between fossil fuel consumption, economic growth, and CO2 emission, before and after applying the interventions. Data has been taken from country-level indicators of the World Bank database. In the subsequent sections, we will discuss about the econometric methodology, analysis of the data, and conclusive policy implications.

ECONOMETRIC METHODOLOGY

In this section, we will discuss about the econometric methodologies applied to look into the association between fossil fuel consumption, economic growth, and CO2 emission, applying economic liberalization related interventions. To start with, we should check the integration characteristics of the data. For this purpose, unit root tests have been applied. If variables in the dataset are I(1) in nature, then cointegration test is used to look into the long run equilibrium association among them. Based on the findings of aforementioned test, order of integration will be found, and that will ensure the applicability of error correction model (ECM), based on which directions of causality among the variables are found. In the subsequent sections, we will discuss these methodologies one by one.

Investigation for Integration In most of the cases, time series economic data exhibits non-stationary nature, as their central tendencies are found to be upwards over a long period. However, in order to investigate the considerable long run association among the variables, carrying out non-stationarity test becomes essential. This test primarily focuses on order of integration, at which point considered variables become stationary in nature. The test is carried out on the level data, and subsequently on differentiated forms of the variables. For this purpose, we will apply augmented Dickey-Fuller test (Dickey & Fuller, 1981), Phillips-Perron test (Phillips & Perron, 1988), and Kwiatkowski-Phillips-Schmidt-Shin test (Kwiatkowski, Phillips, Schmidt & Shin, 1992). These three tests will be conducted for checking the serial correlation, heteroscedasticity, and deterministic trend present in variables under consideration. Following are the test statistics considered for each of the cases: 1/2 2 22 Augmented Dickey-Fuller (ADF) test: σ 1  λσ− T . SE () π  (1) 2 .t − 22   λ π = 0 2  λσ   2 Phillips-Perron (PP) test: 122T . SE ()π (2) Tπ−−() λσ2 2 σ

Journal of Applied Business and Economics vol. 16(5) 2014 123 T Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test: −222 (3) TS∑ t / λ t=1 T 2− 12 Where,σ = limT Eut (4) T →∞ ∑  t=1 T 2 −12 λ = lim ET St (5) T →∞ ∑  t=1 T (6) Sutt= ∑ t=1

Investigation for Cointegration Cointegration is an econometric methodology to investigate the subsistence of long run equilibrium association among variables. This is imperative from an algebraic perspective, as progression of the variables over a long timeframe adjusts the inconsistencies being appeared along the shorter durations. In accordance with Dickey, Jansen and Thornton (1991), if the cointegrated association among variables is not present or weak in nature, then probability of existence of variability in their long-term movement is very high. In view of the existence of this cointegrated association among variables, conducting a regression analysis becomes significant. However, for any number of non-stationary time series variables to be cointegrated, it is imperative for their linear combination to be stationary in nature (Engle & Granger, 1987). However, it is seemingly not appropriate to stick to a methodology, which is capable of analyzing the cointegrated association between only two variables. That is the reason behind our preference of the cointegration testing methodology by Johansen and Juselius (1990) over the one that of by Engle and Granger (1987), as scope of our analysis is not confined by bivariate nature of analysis. Trace and maximum eigenvalue statistics are the two major components of this cointegration analysis (Johansen, 1988, 1991). We will discuss about both of these two statistics. Consider Yt as an (n X 1) vector of I(1) integrated variables and εt as an (n X 1) vector of error terms. Then the vector autoregressive model (VAR) of order N can be expressed as per the following: N (7) ∆Yt =µε + Π∆ YYt−−1 +∑ Γi ∆ ti + t i=1 N Where, (8) Π=∑ Ai −Ι i=1 N (9) Γ=−ij∑ A ji= +1

Precisely, ∏ contains the information about coefficients, which determine the nature of long run association among variables under consideration. Rank of this matrix, which determines number of cointegrating vectors among variables, is calculated through two statistics, namely trace and maximum eigenvalue. The trace test embarks upon the null hypothesis of having cointegrating vectors equal to the rank of the matrix (say r) aligned with the alternate hypothesis of having cointegrating vectors of number n (< r). In case of the maximum eigenvalue test, it embarks upon null hypothesis of having cointegrating vectors equal to the rank of the matrix (= r) against the alternative hypothesis of having cointegrating vectors exactly one more than the rank of the matrix (= r + 1). The test statistics are as per the following: n Trace statistics (JJT) = −T ∑ ln() 1−η (10) ir= +1

Maximum eigenvalue statistics (JJME) = −T ln() 1−ηr+1 (11)

124 Journal of Applied Business and Economics vol. 16(5) 2014 Where, η = ith principal canonical correlation.

Investigation for Causality Association In this section, we will make use of Granger causality test (Granger, 1969) to investigate the causal association encompassing parameters. The quadrivariate Granger causality test based on error correction model (Toda & Phillips, 1993) can be formulated in the following manner:

∆ln FF a bbbb11, 1 12, 1 13, 1 14, 1 ∆ln FF t 1 t−1 ∆  bbbb ∆ ln EGt a2  21, 1 22, 1 23, 1 24, 1  ln EGt−1 = +   + ... ∆lnCEa b b b b ∆lnCE − t 3  31, 1 32, 1 33, 1 34, 1  t 1 ∆ln IN a ∆ln IN t 4 bbbb41, 1 42, 1 43, 1 44, 1  t−1 (12) bbbb 11, nnnn12, 13, 14, ∆ln FFtn− λε11    bbbb ∆ln EG λε + 21, nnnn22, 23, 24, tn− + 22  []ECTt−1 bbbb ∆lnCE − λε  31, nnnn32, 33, 34, tn 33  ∆ln IN λε bbbb41, nnn 42, 43, 44, n tn− 44 

Where, FF stands for fossil fuel consumption, EG stands for economic growth, and CE for CO2 emission, and IN stands for interventions. ECTt-1 is the lagged error correction term, and ε1, ε2, ε3, and ε4 are reciprocally exclusive white noise residuals.

ANALYSIS

Before Applying Any Intervention Analysis of collected data starts with checking the stationarity nature of variables under consideration, for which unit root tests have been conducted. The results of unit root test are recorded in Table 1. It can be visualized that the level data show no indications of stationarity, which confirms existence of unit roots in all three variables under consideration. Subsequently, we moved towards differencing them and conducting unit root tests on the differentiated variables. It is evident from the results that all the three variables are showing stationary nature after first differentiation. This result also confirms that the variables are integrated to order one, i.e. I(1) in nature.

Journal of Applied Business and Economics vol. 16(5) 2014 125 TABLE 1 UNIT ROOT TEST RESULTS

ADF PP KPSS Level Intercept FF -1.033828 -0.988481 0.759594 EG 0.501026 0.383000 0.769768 CE -0.699015 -0.753285 0.779263 Intercept and Trend FF -0.642654 -0.824338 0.159340 EG -0.822281 -1.177163 0.109605 CE -1.685673 -1.685673 0.163886 First Difference Intercept FF -2.674129b -5.501225a 0.243062 EG -5.492174a -5.583894a 0.201027 CE -6.523463a -6.522925a 0.131064 Intercept and Trend FF -5.598336a -5.585270a 0.146613 EG -5.468922a -5.505470a 0.173178 CE -6.481731a -6.480215a 0.076280 a Value at 1% significance level b Value at 5% significance level

Once it has been established that the variables are integrated of order one, it is needed to test the cointegration association between them. The cointegration testing methodology by Johansen and Juselius (1990) have been applied on the variables. The results are recorded in Table 2. The results show that a brawny long run association subsists among the variables. Null hypotheses of having no cointegrating vectors have been rejected by both the statistics, and they show that two cointegrating vectors are present between the variables. Based on these results, we can proceed for further analysis.

TABLE 2 COINTEGRATION TEST RESULTS

Trace test Maximum Eigenvalue test

Null Alternate JJT Critical Value Null Alternate JJME Critical Value r ≤ 0 r > 0 72.80044a 24.27596 r ≤ 0 r = 1 51.22548a 17.79730 r ≤ 1 r > 1 21.57496a 12.32090 r ≤ 1 r = 2 17.69679a 11.22480 r ≤ 2 r > 2 3.878171 4.129906 r ≤ 2 r = 3 3.878171 4.129906 a Value at 1% significance level “r” symbolizes the number of cointegrating vectors

As we have seen the being of cointegration vectors among variables under consideration, we can proceed to formulate the ECM. The results of causality test are recorded in Table 3. Lag length selection criterion are provided in Table 4. Sequential modified LR test statistic (each test at 5% level), final prediction error, Akaike information criterion, Schwarz information criterion and Hannan-Quinn information criterion have been used for this purpose. We can see that unidirectional causality exist from growth in CO2 emission to growth in fossil fuel consumption, economic growth to growth in fossil fuel consumption, and economic growth to growth in CO2 emission.

126 Journal of Applied Business and Economics vol. 16(5) 2014 TABLE 3 CAUSALITY TEST RESULTS

Independent Variable Error Correction Term Dependent Variable ∆FF ∆EG ∆CE ∆FF - 33.45745a 79.34645a 0.020211a ∆EG 4.477120 - 5.929292 -0.861451a ∆CE 8.405823 11.88948b - -0.799292a a Value at 1% significance level b Value at 5% significance level

To set off this study, it is imperative to look into the long-run stability of the associations among the variables. For this purpose, we have carried out a series of diagnostic tests to check serial correlation (LM test), heteroscedasticity (White test) and stability test (Ramsey RESET test). The results those are recorded in Table 5, confirm the constancy of the model analyzing the associations among fossil fuel consumption, economic growth and CO2 emission, in terms of having no serial correlation and heteroscedasticity among the variables, and the associations are stable in nature, along with high explanatory power.

TABLE 4 LAG LENGTH SELECTION CRITERIA

Lag LogL LR FPE AIC SC HQ

0 62.28295 NA 6.78e-06 -3.387597 -3.254282 -3.341577 1 223.4780 285.5454 1.14e-09 -12.08445 -11.55119* -11.90037 2 234.4352 17.53155 1.03e-09 -12.19630 -11.26309 -11.87415 3 239.4244 7.127450 1.34e-09 -11.96711 -10.63395 -11.50690 4 242.7908 4.232060 1.97e-09 -11.64519 -9.912086 -11.04692 5 276.6093 36.71727* 5.30e-10* -13.06339* -10.93034 -12.32706*

TABLE 5 DIAGNOSTIC TEST RESULTS

Variables R2 Adj. R2 LM White Ramsey RESET

FF 0.991530 0.991072 0.170870 0.161051 1.671982 EG 0.969589 0.967945 0.376619 0.351976 0.413365 CE 0.995600 0.995362 0.714497 0.871002 2.481871

Without considering the economic liberalization scenario, it can be said that for a developing nation like India, achieving the economic growth is the primary objective, and it for most of the times calls for overlooking sustainable development aspects. This economic growth is primarily driven by continuous consumption of fossil fuel, and demand for more growth entails consumption of more fossil fuel. This association is reflected by the unidirectional causal association from economic growth to growth in fossil fuel consumption, which is an extension of the results achieved by Paul and Bhattacharya (2004). This continuous economic growth brings forth environmental pressure in terms of CO2 emission, which is

Journal of Applied Business and Economics vol. 16(5) 2014 127 reflected by the unidirectional causal association from economic growth to growth in CO2 emission. However, unidirectional causal association from growth in CO2 emission to growth in fossil fuel consumption indicates the missing feedback link of EKC hypothesis, which we have already discussed. Introduction of several environmental protection legislations and regulatory bodies in India like, the Forest (Conservation) Act, 1980, Air (Prevention and Control of Pollution) Act, 1981, several state-level water conservation acts indicate the significance of this causal association, which reflects the effect of environmental degradation on the driver of economic growth.

After Applying Interventions In the previous section, we have observed and analyzed the causal associations between growth in fossil fuel consumption, economic growth, and growth in CO2 emission, without applying any of the interventions. In this section, we will try to observe and analyze the causal associations between the aforementioned variables. Before proceeding with the same, nature of stationarity of interventions to be applied needs to be checked, for which unit root tests have been conducted. The results of unit root test are recorded in Table 6. It is evident that industrial value added (VA) and energy waste (EW) shows stationarity after first differentiation, and urbanization (U) after second differentiation.

TABLE 6 UNIT ROOT TEST RESULTS

ADF PP KPSS Level Intercept VA -2.080991 -2.092590 0.641612 EW 2.078668 1.983462 0.773819 U -0.050929 -2.431774 0.779276 Intercept and Trend VA -3.774718c -2.137560 0.150428 EW -2.744440 -2.744440 0.129813 U -3.106875 -3.053289 0.162470 First Difference Intercept VA -6.955758a -6.955758a 0.185251 EW -5.320134a -5.314678a 0.411684 U -1.729873 -1.735705 0.392286 Intercept and Trend VA -7.141064a -7.117887a 0.053183 EW -5.759701a -5.754942a 0.130744 U -1.198140 -1.198140 0.180611 Second Difference Intercept U -5.871812a -5.871812a 0.259918 Intercept and Trend U -6.045956a -6.046176a 0.066328 a Value at 1% significance level b Value at 5% significance level c Value at 10% significance level

Hence, it can be said that, in case of analysis considering first two interventions, variables are I(1) in nature, and for the third case, variables are I(2) in nature. After determining the order of integration among the variables and the interventions to be applied, it is needed to test the cointegration association between them. Like previous case, cointegration-testing methodology by Johansen and Juselius (1990) has been applied. The results are recorded in Table 7. Results show that brawny long run associations subsist among the variables and interventions. In all the three cases, null hypotheses of having no cointegrating vectors have been rejected by both the statistics, and they show that two cointegrating vectors are present between the variables and interventions in the first two cases and one cointegrating vector in the third case. Based on these results, we can proceed for further analysis.

128 Journal of Applied Business and Economics vol. 16(5) 2014 TABLE 7 COINTEGRATION TEST RESULTS

Cointegration test using industrial value added (VA) Trace test Maximum Eigenvalue test

Null Alternate JJT Critical Value Null Alternate JJME Critical Value r ≤ 0 r > 0 2.56494a 40.17493 r ≤ 0 r = 1 26.19012a 24.15921 r ≤ 1 r > 1 6.37482a 24.27596 r ≤ 1 r = 2 14.85490 17.79730 r ≤ 2 r > 2 11.51992 12.32090 r ≤ 2 r = 3 8.902062 11.22480 Cointegration test using energy waste (EW) Trace test Maximum Eigenvalue test

Null Alternate JJT Critical Value Null Alternate JJME Critical Value r ≤ 0 r > 0 52.11154a 40.17493 r ≤ 0 r = 1 25.97830a 24.15921 r ≤ 1 r > 1 26.13324a 24.27596 r ≤ 1 r = 2 15.25316 17.79730 r ≤ 2 r > 2 0.168605 4.129906 r ≤ 2 r = 3 0.168605 4.129906 Cointegration test using urbanization (U) Trace test Maximum Eigenvalue test

Null Alternate JJT Critical Value Null Alternate JJME Critical Value r ≤ 0 r > 0 51.79920a 40.17493 r ≤ 0 r = 1 31.83152a 24.15921 r ≤ 1 r > 1 19.96769 24.27596 r ≤ 1 r = 2 13.37663 17.79730 r ≤ 2 r > 2 6.591052 12.32090 r ≤ 2 r = 3 5.867035 11.22480 a Value at 1% significance level “r” symbolizes the number of cointegrating vectors

As we have seen the being of cointegration vectors among variables under consideration, we can proceed to formulate the ECM. The results of causality test are recorded in. The results are recorded in Table 8. Lag length selection criterion are provided in Table 9. Like the pervious section, sequential modified LR test statistic (each test at 5% level), final prediction error, Akaike information criterion, Schwarz information criterion and Hannan-Quinn information criterion have been used for this purpose. We can see that causality associations, those we have found in the previous section, have changed largely. For the case of industrial value added intervention, bidirectional causal associations exist between growth in fossil fuel consumption and growth in CO2 emission, and growth in fossil fuel consumption and economic growth, and unidirectional causal association exists from growth in CO2 emission to economic growth. In case of energy waste intervention, bidirectional causal associations exist between growth in fossil fuel consumption and growth in CO2 emission, and growth in fossil fuel consumption and economic growth. Considering intervention of urbanization, bidirectional causal association exists between growth in fossil fuel consumption and growth in CO2 emission only. Now we will analyze these effects of interventions one by one. Let us take the case of the intervention of industrial value added to start with. If the technological and industrial advancement aspects are left behind, it may prove out to be critical for a nation to depend only on legislative actions to mitigate environment degradation. One of the major aspects of economic liberalization was introduction of new technologies in Indian industrial domain, which accelerated economic growth. However, it acted as a double-edged sword considering India’s atmospheric emission situate, i.e. catalyzing the growth in fossil fuel based electricity consumption, thereby increasing the atmospheric CO2 emission level, and on the other hand, introducing several green technologies to resist emission level. Therefore, the feedback effect of atmospheric emission started to be visibly impactful on

Journal of Applied Business and Economics vol. 16(5) 2014 129 the driver of economic growth, i.e. fossil fuel consumption, along with the growth itself. This was indicated by the unidirectional causal association from growth in CO2 emission to economic growth. During the first decade of the study, CO2 emission per unit of GDP has an average of 0.247, whereas during last decade of the study, the same was 0.174. Therefore, it is quite visible that during first half of the study economic growth was causing growth in CO2 emission, with a very less amount of feedback effect, which became predominant during the second half of the study, indicating the negative growth elasticity of emission. That is the reason the direction of causal association between economic growth and growth in CO2 emission was altered after applying the intervention of industrial value added. Economic growth was being fueled by fossil fuel consumption, and prospective industrialization was demanding consumption of more fossil fuel. This was indicated by the bidirectional causal association between growth in fossil fuel consumption and economic growth. Hence, legislative actions and technological advancements were acting together towards mitigation of the environmental damages being caused by continuous consumption of fossil fuel. Moreover, during this period, carbon trading in India was gaining prominence, due to which several industries started to keep their carbon footprint intact. This phenomenon has been indicated by the bidirectional causal association between growth in fossil fuel consumption and growth in CO2 emission.

TABLE 8 CAUSALITY TEST RESULTS

Causality analysis using industrial value added (VA) Independent Variable Error Correction Term Dependent Variable ∆FF ∆EG ∆CE ∆VA

∆FF - 6.043383a 15.44336a 3.253523 -0.057634a ∆EG 5.363790b - 6.511133a 1.316253 -0.039150a ∆CE 5.902941b 0.664738 - 3.668833 -0.105296a ∆VA 15.37052a 0.148082 11.35078a - -0.203438a Causality analysis using energy waste (EW) Independent Variable Error Correction Term Dependent Variable ∆FF ∆EW ∆EG ∆CE ∆FF - 1.874914 5.491500c 16.64088a 0.026368a ∆EW 1.192637 - 8.083179b 1.392324 0.009243a ∆EG 5.886544c 4.151562 - 1.267882 0.140973a ∆CE 7.698427b 1.851769 3.358084 - -0.008365a

Causality analysis using urbanization (U) Independent Variable Error Correction Term Dependent Variable ∆FF ∆CE ∆EG ∆U ∆FF - 22.20717a 1.456352 5.394364b 0.057529a ∆CE 9.432851a - 2.330795 8.479220a -0.168497a ∆EG 3.361662 2.125647 - 6.708732a 0.255174a ∆U 4.546411 2.449022 3.889956 - -0.005942a a Value at 1% significance level b Value at 5% significance level c Value at 10% significance level

130 Journal of Applied Business and Economics vol. 16(5) 2014 Now, let us look at the impact of the second intervention, i.e. energy waste. By far, fossil fuel based energy consumption amounts to nearly 73 percent of the total energy consumption in India. Hence, for India, fossil fuel consumption is the primary reason for greenhouse blanket formation. From this perspective, it can be said that, whenever energy conservation practices are considered, it majorly poses impacts on the driver of economic growth and the externalities caused by growth. In this case, the externality is negative in nature, and is having the form of CO2 emission. Therefore, to have a control over this negative externality, it is required to have energy efficiency, which can be indicated by lowering of combustible energy waste, the intervention used in this case. Considering India, formation of Petroleum Conservation Research Association (PCRA) in 1977, and Bureau of Energy Efficiency in 2001 are two major steps in bringing forth energy efficiency in Indian industrial scenario. Due to this, we can see that 10.86 percent growth rate of CO2 emission per unit of fossil fuel consumption during first half of the study had come down to 0.84 percent during second half of the study, indicating a nearing zero fossil fuel consumption elasticity of emission. This phenomenon has been indicated by the bidirectional causal association between growth in fossil fuel consumption and growth in CO2 emission. Moreover, we can also see that the 2.16 percent average growth rate of fossil fuel consumption during first half of the study has come down to 1.37 percent during second half of the study. Indicating energy efficiency, the diminishing growth of fossil fuel consumption can have a possible causal effect on economic growth, due to which it became imperative to fuel economic growth via alternative and nuclear energy resources, as fossil fuel consumption per unit of GDP has come down to 2.99 percent in 2010 from 8.49 percent in 1971. This phenomenon has been addressed by the bidirectional causal association between growth in fossil fuel consumption and economic growth.

TABLE 9 LAG LENGTH SELECTION CRITERIA

Lag length selection using industrial value added (VA) Lag LogL LR FPE AIC SC HQ 0 144.1993 NA 4.87e-09 -7.788853 -7.612906 -7.727443 1 321.7469 305.7763 6.21e-13 -16.76371 -15.88398* -16.45666 2 346.0661 36.47882* 4.05e-13* -17.22589* -15.64237 -16.67320* 3 357.7530 14.93326 5.65e-13 -16.98628 -14.69897 -16.18795 4 377.5353 20.88132 5.54e-13 -17.19640 -14.20531 -16.15243 Lag length selection using energy waste (EW) Lag LogL LR FPE AIC SC HQ 0 142.6689 NA 5.30e-09 -7.703831 -7.527884 -7.642420 1 357.3252 369.6858 8.60e-14 -18.74029 -17.86056* -18.43324 2 378.3599 31.55201* 6.74e-14* -19.01999* -17.43647 -18.46730* 3 391.1091 16.29068 8.86e-14 -18.83939 -16.55209 -18.04106 4 409.4867 19.39855 9.38e-14 -18.97148 -15.98039 -17.92751 Lag length selection using urbanization (U) Lag LogL LR FPE AIC SC HQ 0 198.0480 NA 3.27e-10 -10.48908 -10.31493 -10.42769 1 431.9886 404.6539 2.52e-15 -22.26965 -21.39889 -21.96267 2 473.0346 62.12368* 6.73e-16* -23.62349* -22.05611* -23.07092* 3 482.4400 12.20162 1.05e-15 -23.26703 -21.00303 -22.46886

Journal of Applied Business and Economics vol. 16(5) 2014 131 Finally, we will look at the impacts of the third intervention, i.e. urbanization. Once economic liberalization was set in, industrialization gained pace in India, due to which migration of rural populace towards urban areas was taking place. Attributing to this, urban infrastructure was being faced with huge pressure in terms of high demand of energy and high atmospheric emission. This was the time, when several slum areas were formed around the industrial belts in the form of shadow cities, which did not have proper sanitation facilities, and the inhabitants used to burn firewood and coal for their daily cooking purpose. Therefore, their daily existence called for direct and derived demand of fossil fuel consumption. However, their lifestyle pattern resulted in increase in CO2 emission in the industrial regions of India, and this was causing harm to the hygiene level of labor force in terms of increasing respiratory diseases. To reconcile this, Maharashtra government passed Slum Rehabilitation Act, 1995, which was an extension of Maharashtra Slum Areas (Improvement, Clearance and Redevelopment) Act, 1971. Primary focus of this act was improvement of the lifestyle of slum dwellers. This entire phenomenon has been addressed by the bidirectional causal association growth in fossil fuel consumption and growth in CO2 emission. Last but not the least, it is imperative to look into the long-run stability of the associations among the variables. For this purpose, we have carried out a series of diagnostic tests to check serial correlation (LM test), heteroscedasticity (White test) and stability test (Ramsey RESET test), which we have conducted in the previous section as well. The results those are recorded in Table 10, confirm the constancy of the model analyzing the associations among the variables under consideration and the applied interventions, in terms of having no serial correlation and heteroscedasticity among the variables, and the associations are stable in nature, along with high explanatory power.

TABLE 10 DIAGNOSTIC TEST RESULTS

Diagnostic test using industrial value added (VA) Variables R2 Adj. R2 LM White Ramsey RESET FF 0.991827 0.991146 0.495824 1.104635 2.763048 EG 0.974500 0.972375 0.172943 1.199927 0.805265 CE 0.995732 0.995376 0.006168 0.324445 0.005513 VA 0.785456 0.767577 0.479902 1.821885 1.942132 Diagnostic test using energy waste (EW) Variables R2 Adj. R2 LM White Ramsey RESET FF 0.991558 0.990855 1.936786 1.089764 2.099352 EW 0.979841 0.978161 1.924509 0.625894 0.542318 EG 0.969700 0.967175 0.255680 0.241500 0.048571 CE 0.996203 0.995887 0.242843 1.021475 0.161094 Diagnostic test using urbanization (U) Variables R2 Adj. R2 LM White Ramsey RESET FF 0.991593 0.990892 0.208022 1.158355 0.059731 CE 0.996742 0.996471 1.051292 1.153361 2.457134 EG 0.981339 0.979784 0.153274 0.362476 1.344177 U 0.995146 0.994741 0.584963 1.910610 1.041783

132 Journal of Applied Business and Economics vol. 16(5) 2014 TABLE 11 OVERALL RESULTS OF CAUSALITY ANALYSIS

With Energy With Pair-wise variables With no intervention With Value Added Waste Urbanization

FF & CE FF <= CE FF  CE FF  CE FF  CE FF & EG FF <= EG FF  EG FF  EG NA EG & CE EG => CE EG <= CE NA NA

CONCLUSION

So far, we have analyzed the impacts of economic liberalization associated interventions on the causal association among fossil fuel consumption, economic growth, and CO2 emission, for the period of 1971- 2010. The final consolidated results are recorded in Table 11. We have visualized that the causal associations between the variables depend largely on the contextual interventions, which are industrial value added, combustible energy waste, and urbanization in this case for Indian economic liberalization context. Analysis of missing feedback link for EKC hypothesis has been carried out by researchers several times and in diverse contexts. However, in the literature of ecological / environmental economics, it has hardly been tried to encapsulate the changes in aforementioned feedback mechanism after incorporating contextual interventions, which is the primary focus of this paper. From that perspective, it can possibly add substantive value to existing body of knowledge in terms of pre-and-post analysis of contextual variables, while considering any cointegration and causality analysis, and beyond. From the environmental degradation perspective of India, this paper can bring forth significant policy implications, as the effects of economic liberalization of India has been captured here both in terms of data and parametric interventions. Prior to economic liberalization set in, environmental degradation in India was handled primarily by legislative actions, as due to lack of modern technologies it was tough for the industries to combat this issue in a more effective manner. The problems became more prevalent once the economic liberalization was set in, because it harnessed several problems, namely rapid industrialization, rural-urban migration, formation of slum areas in industrial belts, high demand of energy and fossil fuel, and high level of atmospheric emission. As to keep their carbon footprint intact, developed nations most of the times try to dump their obsolete and polluting technologies in developing and underdeveloped nations at a low cost, which deems as a lucrative alternative for the latter parties. In doing so, developing and underdeveloped nations worsen their carbon footprints by causing more harm to the environmental aspects, through technology-driven economic growth. Hence, an endogenous and green growth is desired, rather than exogenous technology-driven growth. As India is gradually moving towards commercialization of nuclear power and thriving to discover more alternative energy resources, it can be expected that continuous evolution and improvement of technology, legislative actions by government, and increasing awareness of citizens regarding protection of their ecological surrounding, it can be expected that, India can achieve the desired level of carbon footprint very soon. A number of social organizations and non-governmental organizations (NGO) are coming forward and taking initiatives at regional or state level for environmental protection. These initiatives need to be replicated across the nation by the support of public-private partnership, as it is not the sole responsibility of Indian government to combat this predicament. Continuous involvement of citizens is also required, as this may have the possibility to create an unparallel level of ecological awareness. From EKC hypothesis, we know that environmental degradation starts falling at a particular level of income growth, which is catalyzed by the awareness level of the citizens, and if government can involve the people and industry in replicating the ecological protection initiatives nationwide, only then we can achieve the aspiration of a clean environment.

Journal of Applied Business and Economics vol. 16(5) 2014 133 REFERENCES

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136 Journal of Applied Business and Economics vol. 16(5) 2014

Global Leadership and Emotional Quotient

Geoffrey VanderPal

INTRODUCTION

The new realities of the business arena ask for culturally attuned and emotionally sensitive global leaders who can react to the challenges of the particular foreign environments of various countries and complex interpersonal work situations. Scholarly works and practical experiences show that two emerging concepts are essentially relevant to the development of efficient global leaders: emotional and cultural intelligences. A modern perspective of cognitive intelligence emerged from psychology has gained popularity within the area of leadership science. Termed emotional intelligence, it attempts to link emotional abilities to leadership effectiveness (Salevey & Mayer, 1990; Cavallo & Brienza, 2004; Ozcelik et al., 2008; Momeni, 2009). Emotional intelligence abilities are referred to as the capacity to reason “about emotions and the ability to use emotions and emotional knowledge to enhance thought” (Mayer et al., 2008, p. 511). Compared to the traditional approach of intelligence as calibrated by the IQ, cultural and emotional intelligences facilitate the understanding of cross-cultural leadership and shed light on the potential redesign of the leadership programs in multinational companies. Alon and Higgins (2005) argued that emotional intelligence (EQ), analytical intelligence (IQ) and leadership behaviors are moderated by cultural intelligence (CI) in the achievement of worldwide success. The past few decades have been marked by extensive debates of both academic and practitioners on the topic of a leader’s emotional intelligence relative to leadership and organizational effectiveness (Chopra & Kanji, 2010; Adams, 2013). Daniel Goleman, the foremost contributor to the field of emotional intelligence and leadership underlined that leaders with a high EI level are crucial to organizational success. They have the ability to seize employees’ feelings related to their work environment, solve the issues that arise, manage their own emotions to gain the staff confidence and understand the political and social agreements within a company (Goleman, 1998, 2001). The literature also notes a successful global leader ability to increase the performance of the organization by establishing a particular work climate.

Journal of Applied Business and Economics vol. 16(5) 2014 137 Reuven Bar-On is another prominent scholar researching the emotional intelligence constructs and the creator of the emotional quotient term. From a slightly different perspective, Bar-On refers to the emotional intelligence as to the concern of understanding oneself and others, adapting to and coping with immediate surroundings to achieve success when dealing with environmental requirements (Bar-On, 1997). No matter of the discrepancies between definitions of emotional intelligence, what is clear is that EI is distinct of what is known as standard intelligence, or IQ. Emotional intelligence quotient is defined as an array of skills that prove one’s ability to identify and understand own behaviors, moods and impulses and conduct him to best respond to the requirements of a certain context (Kasapi & Mikiotis, 2014). McGarvey (1997) defines EI as the talent to relate with people and grasp their emotions, a quality vital for the management of employees, attraction of customers and investors. Rich academic evidences document that across all job types, the EI is the most powerful predictor of success. Stys and Brown (2004) highlighted the existence of three main models of emotional intelligence. The model of Salovey and Mayer (1997) defines EI as a pure cognitive ability. A second model by Bar-On (1997) considers EI as form of mixed intelligence, driven by cognitive skills and personality aspects, influencing the general well-being. The third model established by Goleman (1998) also suggests that EI is a mixed intelligence that involves cognitive ability and personality features. However, compared to Bar-On model, Goleman construction indicates how cognitive and personality aspects lead to work environment success. The emotional intelligence model of Salovey and Meyer (1997) is calibrated using the Mayer- Salovey-Caruso Emotional Intelligence Test (MSCEIT), a performance indicator that requires the participant finish tasks associated with EI. Both Bar-On and Goleman models apply self-report measures of emotional intelligence. Bar-On model uses the Emotion Quotient Inventory (EQ-i), and Goleman’s construct is captured based on the Emotional Competency Inventory (ECI), the Emotional Intelligence Appraisal (EIA), and the Work Profile Questionnaire – Emotional Intelligence Version (WPQei) (Stys and Brown, 2004). Harms and Credé (2010) suggest that EI can be approached as either a trait or an ability. In the first case, emotional intelligence is an innate factor that enables and promotes wellbeing. In the second case, EI is important to comprehend and manage emotions, as well as understand and integrate them into cognitions. Debates about the positioning of emotional intelligence has led Mikolajczak et al. (2009) to build a tripartite model of emotional intelligence introducing three levels of EI: knowledge (what individuals know about emotions and the management of emotion-laden situations), abilities (what one can do), and traits (what people actually do). The literature includes large scientific investigations that measure the impact of emotional intelligence on life quality, occupational success or stress resistance (inter alia, Nelis et al., 2009). However, until recently, EI was treated mainly as concept applying to leadership and performance, with only limited touch on the larger area of organizational behavior. Scholarly works that focus on relating the emotional intelligence to social interactions are scarce. Few studies have strongly indicated EI as a catalyst of these processes and resulting outcomes (Douglas et al., 2004). Extensive researches (George, 2000; Caruso et al., 2002; Palmer & Gignac, 2012) show that while technical knowledge is a major determinant of successful leadership, social skills and emotional abilities have been linked to managerial effectiveness. As stated by Nazarova (2004), emotional intelligence is the critical element of leadership. Nowack (2006) argued that successful managers and leaders reveal higher emotional abilities than those lacking emotional competencies.

LITERATURE REVIEW

It is widely acknowledged that successful global leadership requires multiple intelligences. For instance, Riggio et al. (2002) provided strongly documented evidences that global leaders’ skill have to go beyond a high IQ. The authors posit that intelligence is a multidimensional concept manifested in several forms and that multiple types of intelligences are needed for a highly performing leader. Based on

138 Journal of Applied Business and Economics vol. 16(5) 2014 extensive research of the literature, Alon and Higgins (2005) highlighted three forms of intelligence that represent the core of global leadership and hence, of development of global leaders, as presented below: 1. Rational and logic-based verbal and quantitative intelligence, familiar to most individuals and calibrated by classical IQ test; 2. Emotional intelligence, that gained popularity and become a prominent driver of success in the past decade, measured by EQ tests; 3. The most recently added form of intelligence, cultural intelligence, captured by innovative CQ tools.

The literature that investigates leadership abilities has also been connected to emotional intelligence (Bar-On, 2006). Extensive researches (inter alia, Kouzes & Posner, 2007; Anand & UdayaSuriyan, 2010) explore the link between leadership practices and managers EI document a positive correlation between the two notions. Based on Bass and Avolio (1997) transformational/transactional leader construct, Parker and Sorenson (2008) identified a positive relationship between EI and leadership. While these connections do not essentially link EI of a manger with the engagement levels of subordinates, a conclusion can be formulated on the existence of a potential relationship. Theoretical and empirical evidences outline indicate a positive connection between EI and transformational leadership (Brown & Moshavi, 2005). Thor and Johnson (2011) emphasized that both leadership constructs comprise factors that may affect the engagement degree of subordinates, and in the presence of a link between EI and successful leadership, one may also identify a relationship between a manager EI and the followers level of engagement. The scholarly works have developed a large number of theories that outline which features define the most effective leader. The academic research studies two distinct types of managers: transformational and transactional (Mandell & Pherwani, 2003). Transformational leaders raise interest among subordinates, create a different working environment, increase the visibility of the company goals, offers assistance in order to improve the performance of the organization employee and motivate staff to put the best interest of the company over their own interests. Alternatively, transactional leaders reward or discipline subordinates in accordance to their performance. As described by Bass and Avolio (1994), transactional leaders focus on work guidelines, task accomplishment and employee positive outcomes. Given the similarities that exist between the features of transformational leaders and emotional intelligence (empathy, inter and intrapersonal skills, self-awareness), large academic evidences document a clear relationship between the concepts (inter alia, George, 2000; Daus & Ashkanasy, 2005). Based on the research of 62 independent studies with 7,145 subjects, Harms and Crede (2010) investigated the assumption that emotional intelligence has a positive impact on transformational leadership. The authors used five different indicators of EI, with the first three including the MSCEIT (Mayer et al., 2003), the Emotional Intelligence Scale (Wong & Law, 2002), and EQ-i (Bar-On, 2006). The most popular measures of transformational leadership applied in the work of Harms and Crede (2010) used were the Multifactor Leadership Questionnaire (Bass & Avolio, 1995) and the Leadership Practices Inventory (Kouzes & Posner, 2007). The independent works underlying the analysis included a mixture of both self-rated (most common) emotional intelligence and leadership competencies and subordinate or peer rated skills. The findings revealed a significant gap in the comparison between the self and others rated EI and leadership characteristics. Complementing a leader’s emotional intelligence that enhances performance, employee engagement has also been a central topic in organizational science as a determinant of success at the workplace (Robinson et al., 2004; Harter et al., 2009). As leadership is a dynamic exhaustible reality, success highly depends on the followers and situational context (Marques, 2006). The essential characteristic of a performing leader is given by his skills in successfully analyzing cases and formulating the optimal response at a given time. George (2000) highlighted the critical role of emotions in the leadership process. In addition, Marques (2006) documented that the ability to control emotional impulses, understand and manage them greatly supports successful relationship development and the solving of conflicts.

Journal of Applied Business and Economics vol. 16(5) 2014 139 Intrapersonal and interpersonal abilities associated with emotional intelligence are a skill set most commonly cited by scholarly works (inter alia, Dulewicz & Higgs, 2003; Rosete & Ciarrochi, 2005; Downey et al., 2006). The literature offers extensive empirical evidence on the positive effect of EI on leadership effectiveness (Goleman, 1995, 1998; Wong & Law, 2002; Coetzee & Schaap, 2004; Leban & Zulauf, 2004; Srivastava & Bharamanaikar, 2004; Kerr et al, 2006). However, there are also studies that indicate the absence of any statistical significance between the two concepts (Schulte, 2002; Weinberger, 2003; Barchard, 2003; Brown, 2005; Barbuto & Burbach, 2006; Brown et al., 2006). Other scientific arguments suggest that an immediate supervisor is a determinant of employee work engagement, from the perspective of emotional and rational commitment to the company (Corporate Leadership Council, 2004; Harter et al., 2010; Palmer & Gignac, 2012). As documented by leadership development analyses, EI abilities hold a larger share of behavioral skills in the context of successful leaders compared to technical competencies (Goleman, 1995; Leban & Zulauf, 2004). Cavallo (2004) findings revealed that the more successful performing manages possessed the highest intelligence scores. There is a consensus that traditional indicators of IQ alone do not have the predicative power to estimate how well will managers perform in their careers (Goleman, 1998; Austin, 2010). Emotional intelligence impact on management performance is one of the main discussion points in the current leadership debates. As indicated by Goleman (1997) important leadership skills highly depend on the on the competencies to understand and control emotions at workplace; hence the ability accompanied with EI will influence the capability to lead people. In addition, since the managers’ emotions influence their employees’ behavior, the EI is treated as one of the major factors to distinguish between successful and unsuccessful managers (Bagshow, 2000; Dulewicz and Higgs, 2000). Schutte et al. (1998) suggested that EI of managers is powerfully connected with a modern corporate culture including greater optimism, less depression and lower impulsivity levels in the working environment. George (2000) study showed that emotional intelligence fostered by managers would lead to increased employee motivation, cooperation, financial results and productivity. Given that, the EI cannot be delineated from the notion of leadership, which explains the rationale for considering this link in any organization. Emotionally intelligent individuals perform a successful leadership (Zeidner, 2001). As suggested by Lunenburg (2011), multiple intelligences construct and EI gained impressive consideration at present times, mainly by means of leadership capability. Goleman et al. (2002) argued that excellent leaders moving their organization ahead reveal high emotional intelligence. EI supports mangers efforts to orientate their employee in a successful and resourceful manner by enhancing their competitive edge. Singh (2009) conclusions show that increased emotional intelligence facilitates problem solving in any circumstance, encourages and stimulates employees. Under the current working environment, relationship construct proves especially important and significant. Presently, employees are particularly attracted to a leader’s ability to understand, cooperate and create powerful connections that enhance their performance. Law et al. (2004) demonstrated that highly skilled people in using emotions to improve their outcomes would be capable to help others develop their skills and express emotions in order to build something constructive. Jordan et al. (2002) stressed that emotionally intelligent individuals possess valuable skills to create cohesive and performing teams in a more efficient manner compare to less emotionally intelligent people. EI-leaders have the ability to optimally solve any issue arising at a certain moment, and adjust their style in order to obtain the finest outcomes from every employee of the company. Highly emotionally intelligent people stand out as successful leaders because of their predisposition to a more transformational leadership style (Zafra et al., 2008). EI exceeds the cognitive ability in solving issues and establishing the leader entrepreneur, board of director/executive (Chopra & Kanji, 2010). Emotionally intelligent leaders, who understand their feelings, differentiate and treat successfully with the moods of others, always succeed (Badea and Pana, 2010). An emotional intelligent manager is sensible to emotional consciousness, self-esteem, impressionability, improvement, innovation, risk-taking, service direction, communication, building of relationships and mutual flexibility (Kulkarni et al., 2009).

140 Journal of Applied Business and Economics vol. 16(5) 2014 According to Ruderman et al. (2001), highly emotionally intelligent leaders are successful leaders; they hold the necessary skills of EI and control of their competencies associated with leadership performance that drives improved results. Dijk and Freedman (2007) highlight that excellence managers reveal leadership abilities for the reason that leadership features are expressed in measures of emotional intelligence. There is a plethora of papers that document a substantial impact of leaders’ emotional intelligence on the performance of employees and companies (George, 200; Ruderman et al., 2001; Bradberry& Greaves, 2003; Caruso, & Salovey, 2004; Voole et al., 2004; Lopes et al., 2004; Killian, 2011; Brackett et al., 2011). Carmeli (2003) findings showed that leaders’ emotional intelligence is a catalyst of positive work attitudes, altruistic behavior and improved outcomes. Rahim and Malik (2010) described that improved emotional intelligence influences the intellectual capital, a major factor driving competitive advantages. Managers possessing superior EI always create a powerful relationship with their subordinates by building a supportive and helpful working place which leads to not only enlarged employee performance, but also to company success (Affandi and Raza, 2013). Hence, leaders’ ability to improve commitment by creating appropriate circumstances that drives employee perception of significant work (Christian et al., 2011). According to Jang (2009) work schedule flexibility, working environment support, managerial assistance and work-life balance represent underlying exogenous factors. This can happen only in the context of emotionally intelligent leaders. Work environment characteristics and well-being are positively connected with each other (Sicking et al., 2010). The factors that are substantially linked with wellbeing include increased control of work methods and techniques, limited observation intensity and motivating team leader (Holder, 2002). Leaders possessing extensive emotional intelligence facilitate the creation of better working conditions and offer autonomy to employees, improving the overall quality of the working environment. Casey and Grzywacz (2008) described how managers that implement flexible working provisions and other guidelines increasing flexibility within the company help improve the health, performance and motivation from employees. The skills of highly emotionally intelligent leaders are crucial for the creation of an encouraging environment that facilitates constructive empowerment schemes driving subjective wellbeing (Akerjordet & Severinsson, 2008). Managers that explore the potential of emotional intelligence constantly and equally connect with improved performance of their subordinates (Kafetsios et al., 2011). Rego et al. (2007) highlighted that emotionally intelligent leaders encourage the creativity of the employees of the organization. According to Feather (2009), efforts to enlarge and boost EI are crucial for leadership success, as managers drive employees to perform their jobs more efficiently and increase work motivation. Iordanoglu (2007) describes the positive impact of emotional intelligence on leadership roles in terms of performance valuation, motivation, assistance, development and improvement. Hence, managers can raise the level of emotional intelligence within their organizations by accentuate EI competencies not only in support and mentoring initiatives, but also in their selection and promotion programs (Berman& West, 2008). Goleman et al. (2002) study provided strong evidences that linked EI to the performance of managers within the US and found that the essential leadership abilities were connected to emotional intelligence. In addition, the authors suggested that as much as 79% of the success of leaders in the US was driven by superior EI competencies. A large number of analyses showed that the level of leaders EI influences they conduct, driving success more or less. In a similar way, organizational cultural intelligence is critical, at least for the US companies, when managers move into or work with new organizations. Alon and Higgins (2005) stressed that many times, the absence of CI leads to individual and corporate failures. As presented by McKay (2007), leaders may apply more effective methods and techniques in order to change their negative spirit and created alternative solutions to a complex array of potential issues. It is not too difficult to build scenarios of managers well served by the experience of a plethora of moods and feelings. In addition, scenarios can also be constructed around a leader’s effectiveness that may by

Journal of Applied Business and Economics vol. 16(5) 2014 141 hindered by the painful experience of certain emotions. Jones and George (1998) stressed that managers who manifest anger frequently may encounter hardships in establishing good connection with followers that may engender their confidence. Highly emotionally intelligent leaders have superior abilities to help their subordinates maintain positive moods while interacting with customers and performing emotional tasks (O’Boyle et al., 2010). The study of Goleman (1998) indicated that excellent leaders have 15% IQ and technical abilities, and 85% emotional intelligence. Various researchers (Bliss, 2005; Besterfield et al. (2003) have investigated the strength of this relationship through the features of leaders. Based on the characteristics that managers share, they are considered to be individuals with vision that struggle to change the current paradigm, from different perspectives than that of their subordinates. They are referred to as “quality leaders” as they strive to improve the overall quality in the company, starting with their own traits. While the concept of “quality leadership” is based on the personality of managers, “emotional intelligence” construct relies on the fact that leaders have no other option than bring their one mood and attitudes to the workplace (Kasapi & Mihiotis, 2014). Hence, the implications for their subordinates are not only related to the job features, but also to the ethical and behavioral competencies that leaders possess. This essentially means that both the dimensions of emotional intelligence and the characteristics of excellent managers blend in a manner that would finally lead to the improvement of relationships between leaders and their followers (Kasapi and Mihiotis, 2014). Robbins and Judge (2012) indicated empathy as a core aspect of emotional intelligence. Empathetic managers understand better other needs, listen to their subordinates and seize the reactions of other. In addition, Champy (2003) emphasized that the caring part of empathy, is what inspires employee to stay with the leader when faced with difficulties. Schwalbe (2001) stressed that beyond the importance of basic leadership skills, having highly performing leaders within an organization is one of the crucial factors in managing human resources, particularly in the context of team-basis projects. The research of Bar-On and Orme (2003) of one of the UK major restaurant groups presented solid arguments that emotionally intelligent managers drive superior effectiveness. Their performance exceeded the results of others in terms of improved guest satisfaction, decreased turnover and 34% greater profit growth (Bar-On and Orme, 2003). Freedman and Everett (2004) study is a remainder for managers that their success begins and ends with their inner resources: leaders who do not develop self-awareness risk fall into an emotionally dangerous routine that threatens their overall potential. The reluctance to explore their own competencies not only weakens leaders’ motivation, but also can severely affect their ability to inspire subordinates. A study of Dulewicz and Higgs (2000) demonstrated that EI exceeds the importance of intellect and other management competencies. In addition, to raise the value of EI in leadership, the same authors (2003) identified superior emotional intelligence levels among managers that increased with the leadership scales within an organization. Deeter-Schmelz et al. (2008) described the potential of EI to change an efficient sales manager into an excellent leader. The ability of emotionally intelligent individuals to turn all positive emotions into performance and to reverse the impact of negative emotions transforming them into challenging objectives could unlock huge opportunities for any company (Law et al., 2004). Dulewicz and Higgs (2000) emphasized that managers with a balance mix of IQ and emotional intelligence perform a superior leadership compared to those that not.

CONTROVERSY

The literature also includes studies that question the existence of a significant impact of EI on leadership effectiveness (Buford, 2001; Collins, 2001; Schulte, 2002; Weinberger, 2003; Brown et al., 2006). For instance, the findings of Antonakis (2004) and Goleman (1998) point out to the claims that emotional intelligence are twice as important as IQ or technical skills for leadership success as the major thesis against emotional intelligence. Moreover, according to Mayer and Caruso (2002), although EQ is

142 Journal of Applied Business and Economics vol. 16(5) 2014 an important leadership asset, it co-exists with other capabilities and weaknesses. In addition, Weinberger (2009) indicated the absence of any relationship between a manager’s emotional intelligence and leadership style or the leaders’ perceived effectiveness. Given that the construct of emotional intelligence is still at an early stage, the criticism of some researchers should not come as a surprise. The most popular themes that emerged from the critics of emotional intelligence refer to the notion’s arguable resemblances to personality features, the absence of a clearly-shaped measurement system, its inability to accomplish psychometric requirements, large claims on performance enhancement, and the cunning to replicate emotional intelligence relative to certain assessment instruments (Conte, 2005; Landy, 2005; Locke, 2005; Day& Carroll, 2008). For illustrative purposes, a recent study of Day and Carroll (2008) described how by faking EI, some indicators create a false reality that an individual is highly emotionally intelligent when motivated to behave so. The largest part of emotional intelligence criticism has been focused on the Goleman (1998) and Bar- On (2006) models. For example, Landi (2005) explored Goleman’s findings and suggested that his scientific effort has not been peer reviewed and the results returned from the ECI instrument to measure its construct has not been transparently shared with the academic community. The author argued this made the outstanding performance claims Goleman indicated to be generated by improved EI questionable. Conte (2005) perspective is similar; the author highlighted the limited peer evaluation done on ECI and the minimal predictive and discriminant validity arguments provided, concluding that this instrument is of little significance and consideration. Daus and Ashkanasy (2005) went even further and highlighted that Goleman and Bar-On models adversely influenced the establishment of emotional intelligence as a legitimate concept with a high potential for incremental validity. Matthews et al. (2002) have also criticized the lack of consistency between instruments used to capture emotional intelligence. The comparative analysis of MSCEIT and the EQ-I showed that the amount of overlap ranged from 4% (Bracket &Mayer, 2003) to 13% (Mayer et al., 2000), which essentially means that the two are measuring different constructs. Although the ability-based model of Mayer and Salovey (1997) has been less exposed to negative attention, it does not lack criticism. To summarize, the study of the literature indicates the absence of a theoretical consensus and clarity relative to the most performing measurement system among EI, leadership success and decision making process. Although the extensive debate between the supporters and critics of emotional intelligence could be treated as a drawback within the area of research, a counter argument stresses this is a sign of the positive evolution and validity of the concept. The early evidences show a significant potential for further investigation in analyzing the connection between EI and leadership effectiveness, although more improvement of the notions and measurement system may be required to create a stronger case for the correlation between the two. As the exploration of the subject continues, a construct is likely to appear as the forerunner in the EI area. Early indications point out that the ability-based model has received only limited negative attention from the perspective of trait based factors; moreover, some scholarly works suggested this is the only EI model that should be seriously considered by researchers (Daus & Ashkanasy, 2005).

EPILOGUE

More than two decades ago, the concept of emotional intelligence captured the interest of the academic community, business arena and individuals in general. Presently, we note the existence of three models of emotional intelligence, each with its own instruments and measurement system. The pioneering model of EI was built around cognitive abilities and differentiated through a result of performance-based indicator of EI. The other two are mixed models that focus on both cognitive competencies and personality traits and apply self-report measures. Researchers indicate that the connection between EI/communication model and leadership/companies decision-making has to be explored in various industries and professions, so that scholarly efforts must be able to provide comparable findings. Studies (inter alia, Rosete & Ciarrochi, 2005; Rigoglioso, 2006;

Journal of Applied Business and Economics vol. 16(5) 2014 143 Kerr et al., 2006) that describe the relation between emotional intelligence and an efficient leader have built a new environment for the implementation of human resources procedure in the selection, improvement and performance management used by companies. In order to have the best equipment to deal with change, companies need to increase resilience and emotional intelligence (Reid, 2008). A large number of scholarly works recommend the embedding of emotional intelligence tests into the selection process to improve the level of EI. Other specialists suggest the assessment of emotional intelligence of current and potential managers, the inclusion of EI into performance ratings and succession planning (Conrad, 2008). Complemented by traditional recruitment evaluation techniques, EI estimation can be a powerful indicator of the outcomes of the organization employee (Chrusciel, 2006). This can be a valuable procedure to save monetary resources and time in ensuring that the best-equipped individual is selected for the position. Directions of future research of emotional intelligence of leaders may refer to the investigation of many other related factors such as job satisfaction, organizational engagement or followers’ motivation. The embedding of emotional intelligence constructs into practice has generated only a limited amount of empirical research that leaves open space for new future analysis areas. For instance, these may include the development of a reliable instrumentation, consensus relative to the definition of emotional intelligence, determining if the concept is a unique measure or it quantitative exploration. The issues related to the best instrument to capture emotional intelligence have generates extensive debates in the academic community. The vast majority of tools are build based on self-reports. The MSCEIT proposed by Mayer et al. (2002) is one of the pioneering attempts to establish a performance- based instrument. However, the literature indicates that additional efforts are needed in order for this instrument to be used to identify critical relationships between variables and further investigate the field of emotional intelligence, leadership and management. Some academic opinions stress that the very concept of emotional intelligence needs to be explored further. The explanation is that the construct is subject to in-depth analyses in some works and only narrowly in others. A more closely aligned definition is required for clarity purposes and assistance to future the academic arena and business environment. The myriad of definitions is supported by different hypotheses and calibrated in many different ways. The understanding of an author’s perspective implies careful reading. Confusion may also emerge in practice. In the absence of a critical reading and of an understanding of the multiple dimensions of emotional intelligence, practitioners’ efforts are directed to what might be a corporate fad.

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