Effect of Marketing Communications on Consumer Purchase Behavior of Fast Moving Consumer Goods (Fmcg) in Kenya by Joy Masimane U

EFFECT OF MARKETING COMMUNICATIONS ON CONSUMER PURCHASE BEHAVIOR OF FAST MOVING CONSUMER GOODS (FMCG) IN KENYA

JOY MASIMANE

UNITED STATES INTERNATIONAL UNIVERSITY- AFRICA

SUMMER 2017

EFFECT OF MARKETING COMMUNICATIONS ON CONSUMER PURCHASE BEHAVIOR OF FAST MOVING CONSUMER GOODS (FMCG) IN KENYA

JOY MASIMANE

A Research Project Report Submitted to the Chandaria School of Business in Partial Fulfillment of the Requirements for the Degree of Masters of Business Administration (MBA)

UNITED STATES INTERNATIONAL UNIVERSITY-AFRICA

SUMMER 2017 STUDENT’S DECLARATION

I, the undersigned, declare that this is my original work and has not been submitted to any other college, institution or university other than the United States International University in Nairobi for academic credit.

Signed……………………….. Date……………………...………

Joy Rhoda Masimane (628499)

This project report has been presented for examination with my approval as the appointed supervisor.

Signed…………………………. Date……………………………

Dr. Peter Kiriri

Signed…………………………… Date………………………………

Dean Chandaria School of Business

ALL RIGHTS RESERVED. Any unauthorized reprint or use of this research report is prohibited. No part of study may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system without express written permission from the author and the university.

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ABSTRACT

This project report discusses the findings from investigating the effectiveness of marketing communication to consumer purchase behavior on Fast Moving Consumer Goods (FMCG). The four objectives covered by this study were: the effects of advertising on consumer purchase decisions, the effects of sales promotions on consumer purchase decisions, the effects of personal selling on consumer purchase decisions, and the effects of social media on consumer purchase decisions. The research was covered amongst purchase decision makers of FMCG products in middle income households in Nairobi. A sample of 395 was covered. Descriptive statistics such as: mean, standard deviation, correlation, and regression were used to measure results.

Results for the first objective, effects of advertising on consumer purchase decisions, show that trial purchase is most influenced by recommendation from friends or family. Marketers refer to this as word of mouth advertisement. A correlation analysis was run between the advertisement channels and purchase decision and it revealed that the Pearson’s r value for all was below 0.5 denoting weak, positive association. A regression analysis was run and results showed R2 is 0.235, which means that 23.5 percent of the total variation in purchase is explained by variability in advertisement channels. A correlation analysis was also done for advertising elements the Pearson r relationship between ‘Celebrities / famous people’ and purchase decision scored 0.522 which was the only strong, positive association.

The second objective investigated the effects of sales promotions on consumer purchase decisions. Results reveal that price discounts are the most used type of sales promotion. Sales promotions also induce temporary switching as ‘The promotion makes me temporarily change brands’ had the highest mean amongst the influence factors tested. A regression analysis was done and R2 is 0.124, which means that 12.4 percent of the total variation in purchase decision is explained by variability in sales promotions. A correlation analysis was run between the sales promotion and purchase decision. The Pearson’s r value for all relationships between ‘all sales promotion types’ and purchase decision, are above zero but below 0.5 denoting a weak, positive associations.

The third objective covered the effects of personal selling on purchase decisions. About 90% have interacted sales persons in supermarkets. A correlation analysis was run iv

between the sales persons and purchase decision and the Pearson’s r relationship between ‘sales women in the supermarket’ and purchase decision was the strongest implying that women have a higher influence than men.

The fourth and last objective studies the effects of social media on purchase decisions. Findings revealed that Facebook followed by WhatsApp are the most used social media networks. Respondents are most satisfied with product information provided on Facebook followed by Twitter. A correlation analysis was done on the different types of information and purchase decision, it was discovered that information on promotions had the strongest positive association. Social media marketing is also found more creative and attractive compared to others

The study provides key conclusions for each marketing strategy. Advertising has more influence on TV and internet channels, positive word of mouth which is a key factor for trial purchase, and use of celebrities has the strongest positive correlation on purchase influence. Sales promotions are driven by price discounts which are the most used, they help customers make after purchase decisions, and they drive temporary brand switching. Personal selling is effective as customer frequently encounter them, are satisfied with information given, and increase brand trust and image. Use of women in personal selling has a higher influence than men. Social media marketing is most effective on Facebook which is the most widely used and social media platforms provide adequate product information.

The main recommendations for each marketing strategy was provided. Advertising budgets should be focused TV and internet. Internet can be used to drive positive word of mouth which promotes trial purchase and TV advertisements should take advantage of celebrity advertising. Marketers should use price discounts to drive temporary switching when they need to boost their sales. Emphasis should be placed on women when selecting sales representatives in supermarkets because they have more influence on purchase. Social media marketing should leverage on Facebook more than other platforms. The recommendation on further studies is that the research should be done on counties outside Nairobi to identify disparities in purchase behavior and get a nationally representative view of the findings.

ACKNOWLEDGEMENT

I would first like to thank The Almighty God for helping me do this research, giving me good health throughout and providing all the resources and support I required to do this work. I am gratefully indebted to my thesis supervisor Dr. Peter Kiriri for his valuable supervision through timely, useful, and insightful feedback, his availability, support, and commitment to helping me successfully complete this thesis. Without his passionate participation and input, this research could not have been successfully conducted. I also acknowledge my parents for their prayers and support towards this project.

DEDICATION

I dedicate this report to my parents who are happy that I have worked towards receiving a second degree and have given all the support needed throughout the 2 years. I also dedicate this thesis to all the market researchers who have specialized in communication pre-testing and post-evaluation, I believe this document will be useful to them.

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TABLE OF CONTENTS

STUDENT’S DECLARATION ...... ii COPYRIGHT ...... iii ABSTRACT ...... iv ACKNOWLEDGEMENT ...... vi DEDICATION ...... vii LIST OF TABLES ...... xi LIST OF FIGURES ...... xii

CHAPTER ONE ...... 1 1.0. INTRODUCTION ...... 1 1.1. Background of the Study ...... 1 1.2. Statement of the Problem ...... 5 1.3. General Objective ...... 6 1.4. Specific Objectives ...... 6 1.5. Rationale of the Study ...... 6 1.6. The Scope of the Study ...... 8 1.7. Defining of Key Terms ...... 9 1.8. Chapter Summary ...... 10

CHAPTER TWO ...... 11 2.0. LITERATURE REVIEW ...... 11 2.1. Introduction ...... 11 2.2. The Effects of Advertising on Consumer Purchase Intention ...... 11 2.3. The Effects of Sales Promotions on Consumer Purchase Intention ...... 16 2.4. Effects of Personal Selling Affects Consumer Purchase Behavior ...... 21 2.5. Effects of Social Media Affects Consumer Purchase Behavior ...... 25 2.6. Chapter Summary ...... 30

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CHAPTER THREE ...... 31 3.0. RESEARCH METHODOLOGY ...... 31 3.1. Introduction ...... 31 3.2. Research Design ...... 31 3.3. Population and Sampling Design ...... 32 3.4. Data Collection Methods ...... 35 3.5. Research Procedure ...... 36 3.6. Data Analysis Methods ...... 36 3.7. Chapter Summary ...... 37

CHAPTER FOUR ...... 38 4.0. RESULTS AND FINDINGS ...... 38 4.1. Introduction ...... 38 4.2. Response Rate...... 38 4.3. Background Information ...... 39 4.4. Advertising’s Effects on Purchase Intention ...... 44 4.5. Sales Promotions’ Effects on Purchase Intention ...... 54 4.6. Personal Selling’s Effects on Purchase Intention ...... 63 4.7. Social Media’s Effects on Purchase Intention ...... 71 4.8. Consumer Buying Behavior ...... 80 4.9. Chapter Summary ...... 83

CHAPTER FIVE ...... 84 5.0. DISCUSSIONS, CONCLUSIONS AND RECOMMENDATIONS ...... 84 5.1. Introduction ...... 84 5.2. Summary ...... 84 5.3. Discussion ...... 86 5.4. Conclusions ...... 94 5.5. Recommendations ...... 96 ix

REFERENCES ...... 99 APPENDICES ...... 112 APPENDIX 1: INTRODUCTION LETTER ...... 112 APPENDIX 2: QUESTIONNAIRE ...... 113

LIST OF TABLES Table 4.1: Response Rate ...... 38

Table 4.2: Decision Making Capacity ...... 40

Table 4.3: Nationality ...... 41

Table 4.4: House Hold Income ...... 42

Table 4.5: Level of Education ...... 44

Table 4.6: Factors That Influence Trial Purchase ...... 45

Table 4.7: Advertisement Channels of Interaction ...... 46

Table 4.8: Advertisement Channel Influence on Purchase ...... 46

Table 4.9: Correlation on Advertisement Channel and Purchase Decision ...... 47

Table 4.10: Regression on Advertisement Channels and Purchase Decision ...... 48

Table 4.11: Advertisement Elements’ Influence to Purchase ...... 51

Table 4.12: Correlation between Advertisement Elements and Purchase Decisions .... 52

Table 4.13: Sales Promotion Influence on Purchase Behaviour ...... 58

Table 4.14: Regression of Sales Promotion and Purchase Decision ...... 59

Table 4.15: Future Use of Sales Promotion ...... 61

Table 4.17: Satisfaction with Product Information Provided ...... 66

Table 4.18: Sales Information Influence on Purchase ...... 66

Table 4.19: Correlation of Sales Person Influence on Purchase Decision ...... 67

Table 4.20: Regression of Sales Person Influence on Purchase Decision ...... 68

Table 4.21: Correlation on Personal Selling Perceptions and Purchase Decision ...... 70

Table 4.22: Regression on Personal Selling Perceptions and Purchase Decision ...... 71

Table 4.23: Correlation Social Media Information and Purchase Decision ...... 75

Table 4.24: Correlation on Social Media Types of Information and Purchase ...... 77

LIST OF FIGURES Figure 4.1: Gender ...... 39

Figure 4.2: Age ...... 40

Figure 4.3: Occupation Status ...... 42

Figure 4.4: Religion ...... 43

Figure 4.5: Marital Status ...... 43

Figure 4.6: Advertisement Elements of Interest ...... 49

Figure 4.7: Most Important Advertisement Element ...... 50

Figure 4.8: Advertisement Elements to Improve ...... 50

Figure 4.9: Shopping Frequency ...... 55

Figure 4.10: Frequency of Sales Promotions ...... 55

Figure 4.11: Awareness of Sales Promotion Types ...... 56

Figure 4.12: Usage of Sales Promotions ...... 56

Figure 4.13: Sales Promotions Source of Awareness ...... 57

Figure 4.14: Most Attractive Discount ...... 62

Figure 4.15: Impulse Purchase ...... 62

Figure 4.16: Types of Products Customer Buy the Most ...... 63

Figure 4.17: Interaction Incidence with Sales person...... 64

Figure 4.18: Frequency of Sales Person Encounter ...... 64

Figure 4.19: Product Isles with Sales Persons ...... 65

Figure 4.20: Products Inclined to Personal Selling ...... 69

Figure 4.21: Frequent Social Media ...... 72

Figure 4.22: Social Media Share ...... 73

Figure 4.23: Social Media with Product Information ...... 73

Figure 4.24: Types of Product Information from Social Media ...... 75

Figure 4.25: Products Purchased from Social Media ...... 79

Figure 4.30: Most Impactful Marketing Strategy ...... 80 xii

Figure 4.26: Shopping Frequency ...... 81

Figure 4.27: Shopping Outlets ...... 82

Figure 4.28: Shopping Allies ...... 82

Figure 4.29: Shopping Criteria for Purchase ...... 83

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CHAPTER ONE

1.0. INTRODUCTION

1.1. Background of the Study

This study investigated whether marketing communication was relevant in influencing a consumer’s decision to purchase Fast Moving Consumer Goods (FMCG). Explanations from the American Marketing Association (AMA), marketing’s professional organization, define marketing as the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large. Further, Kotler (2011) defines marketing as “the science and art of exploring, creating, and delivering value to satisfy the needs of a target market at a profit. Marketing identifies unfulfilled needs and desires. It defines, measures and quantifies the size of the identified market and the profit potential. It pinpoints which segments the company is capable of serving best and it designs and promotes the appropriate products and services.”

According to Wood (2004), communication is “a systemic process in which individuals interact with and through symbols to create and interpret meanings.” The Oxford dictionary, communication takes place when one individual, a sender, displays, transmits or otherwise directs a set of symbols to another individual, a receiver, with the aim of changing something, either something the receiver is doing (or not doing) or changing his or her world view. This set of symbols is typically described as a message.

Marketing communication has therefore been defined as a process for planning, executing and monitoring the brand messages that create customer relationships (Duncan, 2005). Marketing communication has also been defined as the coordination and integration of all marketing communication tools, avenues and sources within a company into a seamless program that maximizes the impact on consumers and other end users at a minimal cost (Clow & Baack, 2007). Marketing communication therefore represents the voice of a brand and the means by which companies can establish a dialogue with consumers concerning their product offerings. It allows marketers to inform, persuade, incite, and remind consumers. It can provide detailed product information or ignore the product all together to address other issues (Keller, 2001). 1

Marketing communication activities come in a wide variety of flavors based on audience, media platform and business in today’s evolving and dynamic marketplace. One of the most important changes in today's marketplace is the increased number and diversity of communication options available to marketers to reach customers (Häuser & Daugherty, 2011). The need for an organization to properly coordinate its marketing communication strategies in order to deliver clear, consistent and competitive messages about itself and its products is therefore highly imperative for every result driven organization (Häuser & Daugherty, 2011). Innovative and creative marketing communications have great impact on a companies’ products/services sales. This study specifically investigated how these four marketing communication strategies: advertising, sales promotions, personal selling and social media, affect the urban Kenyan consumers’ purchase behavior.

The marketing communication theory implies that studying the effects of marketing communications on customer's response require understanding how organizational customers under different circumstances, exposed to different situational factors and to different types of communications respond to these factors. Consumers obviously vary on a host of different characteristics demographic (e.g. age, gender, race, etc.), psychographic (e.g. attitudes towards oneself, others, possessions, etc.), behavioral (e.g. brand choices, usage, loyalty, etc.) that often serve as the basis of market segmentation and the development of distinct marketing programs (Häuser & Daugherty, 2011). But customers may differ in their prior knowledge, especially in terms of what they know moving from the general to the specific. (Barnham, 2012). Hence, consumer purchase behavior is influenced by different factors, like cultural, social, personal and psychological factors. A successful company marketer knows and needs to analyze very well all the factors that affect consumer purchase behavior (Zemack et al., 2012).

Consumer purchase behavior is considered to be an inseparable part of marketing. Marketers’ point of view issues specific aspects of consumer behavior that need to be studied which include: the reasons behind consumers making purchases, specific factors influencing the patterns of consumer purchases, analysis of changing factors within the society and others. Blackwell et al., (2006) informs that consumer purchase behavior is itself is a complex, dynamic issue which cannot be defined easily and

commonly. Therefore, the concept of consumer buying behavior has been defined in different ways by different researchers.

Kotler and Keller (2011) state that consumer purchase behavior is the study of the ways of buying and disposing of goods, services, ideas or experiences by the individuals, groups and organizations in order to satisfy their needs and wants. It also investigates behavior which is the earlier process of buying, the process of purchasing and the next purchase after buying (Yakup & Savl, 2011). Buyer behavior has also been defined as a process, which through inputs and their use though process and actions leads to satisfaction of needs and wants. Alternatively, consumer buying behavior “refers to the buying behavior of final consumers, both individuals and households, who buy goods and services for personal consumption” (Kumar, 2010).

Consumer buying behavior has been defined as a process of choosing, purchasing, using and disposing of products or services by the individuals and groups in order to satisfy their needs and wants. Similar definition of consumer buying behavior is offered by Schiffman and Kanuk (2000) in which they describe it as behavior that consumers express when they select and purchase the products or services using their available resources in order to satisfy their needs and desires.

In light of all these definitions, Kotler and Keller (2011) highlight the importance of understanding consumer buying behavior and the ways how the customers choose their products and services can be extremely important for manufacturers as well as service providers as this provides them with competitive advantage over its competitors in several aspects. For example, they may use the knowledge obtained through studying the consumer buying behavior to set their strategies towards offering the right products and services to the right audience of customers reflecting their needs and wants effectively. Egen (2007) also informs on the importance of understanding the consumer behavior, he says that better awareness of consumer buying behavior is a positive contribution to the country’s economic state. In agreement to Kotler and Egen, this study investigated how marketing communication affects purchase behaviour specifically in the FMCG sector.

Fast moving consumer goods (FMCG) manufacturing is very much as the name suggests: high quality products that fly off the production lines as fast as they fly off supermarket shelves (Anunda, 2012). They can also be referred to as consumer- packaged goods (CPG); products that are sold quickly and at relatively low cost. Typical products include personal care products (bathing soaps, toothpaste, lotion, hair food, shoe polish etc.), home care products (pesticides, laundry, toilet care, etc.), foods (snacks, grains, etc.) and beverages (cold, hot, alcoholic and non-alcoholic). Major players include companies like Procter & Gamble, Unilever Kenya, Nestle Foods, GlaxoSmithKline and Reckitt Benckiser which produce a wide portfolio of products, while other companies like Weetabix and British American Tobacco Kenya Limited tend to focus on single product areas (Anunda, 2012).

Kenya has 47 FMCG manufacturers hence faced with intensified competition because of consumers who are more value conscious and less brand loyal, dwindling product life cycles and increasingly powerful retailers; this mean that many new FMCG products fall by the wayside. Despite the critical nature of the product launch process, very little is known about what even makes a new product launch in the FMCG industry successful in Kenya (Saronge, 2004). However, it is notable that today, consumers use many sources of information, and the value of the marketing communication has grown considerably. Highly targeted, the marketing communication campaigns are based on the strengths of existing communication tools, to favorably influence the behavior of the target audience (Saronge, 2004).

Research suggests that customers go through a five-stage decision making process in any purchase. Five Stage Model initially proposed by Cox et al. (1983) is considered to be one of the most common models of consumer decision making process and it involves five various stages. These stages are: recognition of need or problem, information search, comparing the alternatives, purchase and post purchase evaluation. It is therefore imperative to have impactful communication at all stages in order to positively influence how consumers make decisions on the FMCG products they purchase frequently. A marketer’s communication should influence their decision making before, during and after the purchase process. Before the communication to

make them consider, during to influence action and after for repeat purchase (Cox et al, 1983).

1.2. Statement of the Problem

This research established the effectiveness of marketing communication on consumer purchase behavior of fast moving consumer goods (FMCG). FMCG companies spend a large share of their annual budgets in marketing communication activities which include advertising, sales promotions, direct marketing and personal selling amongst others. The purpose is to achieve profitability by driving sales of their products (Kotler & Keller, 2011). It is therefore imperative to understand the buyer in order to accurately meet their dynamic and evolving needs to achieve this objective. An understanding of the influence of purchase factors such as cultural, social, personal, psychological factors is essential for marketers in order to develop suitable marketing mixes to appeal to the target customer (Kotler et al, 1994).

In a global context, marketing communication plays an important role in developing sales and market share (Kotler & Keller, 2011). In agreement, the companies that spend the highest amount overall on marketing communication such as advertising in the USA would be the toy and gaming industries. For example, Ninja Corporation is an award- winning design company, making the most innovative toys and outdoor activity products for children worldwide. It owns 52% of the market while their 2 main competitors only have about 15% each. In order to keep its products on top of their consumers' minds, they constantly use advertisements as reminders. As much as this can be generalized for every market, such findings have not been extensively covered in the Kenyan market.

In the Kenyan context, we have witnessed both positive and negative scenarios where marketing communication has been deemed effective or ineffective. In reference to the Reja study done in 2014 by Consumer Insight, a Kenyan based market research Company, the maize flour brand ‘Soko Ugali’ had the lead market share in 2014. This was a great shift in the market where the ‘Jogoo’ brand always had the outstanding lead over the years. It is notable that in the same year, ‘Soko Ugali’ had massive communication on this new brand on all traditional media; TV, radio, print and outdoor.

They have remained Jogoo’s closest competitor to date (2017). This creates a hypothesis that communication affects purchase behavior. The same study showed a different scenario in the juices category where Afia juice, a brand that has zero marketing communication to end consumers, had an outstanding market share while brands like Minute Maid that spend billions yearly to advertise did not match up to Afia juice’s lead market share. In light of these 2 scenarios in the Kenyan context, the need to establish the effectiveness of marketing communication on consumer purchase behavior of fast moving consumer goods (FMCG) in Kenya cannot be overlooked.

1.3. General Objective

The general objective of this study was to determine the effect of marketing communication on consumer purchase behavior in the FMCG sector in Kenya.

1.4. Specific Objectives

1.4.1. To establish the effects of advertising on consumer purchase decisions on FMCG products

1.4.2. To determine the effects of sales promotions on consumer purchase decisions on FMCG products

1.4.3. To evaluate the effects of personal selling on consumer purchase decisions on FMCG products

1.4.4. To assess the effects of social media on consumer purchase decisions on FMCG products

1.5. Rationale of the Study

This study was important to management teams in FMCG, consumers, marketing professionals, academicians and researchers, authorities and regulators.

1.5.1. Management in FMCG

FMCG organizations such as beverages, beauty, personal care, home care, and foods, will benefit from this study because they will be able to understand which touchpoints are most relevant to consumers and they will be able to prioritize these touchpoints 6

when allocating budgets and planning the marketing strategies mix in the campaigns. Finance managers and related persons in FMCG organizations will understand the extent to which marketing communication provides short term and long term financial benefits to the business as a whole and will see the relevance of allocating adequate budgets to such activities.

1.5.2. Customers and Consumers

This study will be relevant to consumers who are keen on or have interacted with marketing communication such as advertisements, sales promotions, personal selling and social media. The findings will verify to them that we have captured the feedback accurately and the existence of this information can be used to support the demands they create. For example, if the findings show that the do not have enough information they can use the facts and Figures to advocate for FMCG companies to provide more information on particular topics.

1.5.3. Marketing Professionals

This study will be of assistance to the marketing industry at large; as professionals will know where they stand when it comes to the effectiveness of their communication. They will get to know what they are doing right and what they are neglecting, how consumers feel and what needs to change. It will also spur healthy competition among FMCG companies.

1.5.4. Academicians and Researchers

This study will be beneficial to scholars and market researchers that specialize in studying consumer behavior because they will appreciate the role that communication plays in influencing the buyer’s behavior. In light of this it will be key factor to study when researching on different consumer categories.

1.5.5. Authorities and Regulators

Associations such as Marketing Society of Kenya (MSK) and Marketing and Social Research Association Kenya (MSRA) as policy makers will benefit from this study by

having a confirmation on which communication touchpoints consumers interact with the most and also clarify which components of these touchpoints influence their purchase the most. This enables them to advice Marketing firms, Advertising agencies, and Market Research firms on key areas of focus when undertaking marketing activities and advising their clients on the same. They can also be used as variables to be tested overtime and creating benchmarks and norms to be surpassed when undertaking future research activities.

1.6. The Scope of the Study

The geographical scope covered by this study was Nairobi which was considered an urban area with highly active FMCG products consumers. Interviews were conducted in the town center to ensure a random selection of respondents who are coming from all parts of Nairobi and are within the target criteria.

The population scope of this study was purchase decision makers in the household – who are defined as the people who largely decide the brands to buy in the household and also pay for the shopping. Respondents were middle income males and females with a household net income of KES 40,000 and above. They were within the ages of 18-45 years old because this age group consists of people who are both decision makers of FMCG goods and are likely to be influenced to switch brands by marketing communication. Interviews were conducted amongst 385 respondents in Nairobi; this sample was statistically calculated and established as viable to represent a large population distribution of 250,000. The sample can also be analyzed across the various demographic groups under this study.

The scope of time for the research was from January 2017 to August 2017. The project proposal and questionnaire were developed from 9th January and finalized on 30th June, the questionnaire was programmed on a mobile platform for data collection from 3rd to 7th July, data collection was done from 10th to 18th July, data analysis and reporting was done from 19th July to 19th August.

The main limitation for this study was respondent hostility as they were not willing to sacrifice 30 minutes of their time when leaving a retail outlet to go through a survey

they had not planned for. Those who agreed were skeptical about anonymity on the information shared. These limitations were mitigated by helping them to understand the benefits of their participation in this study and assure them of confidentiality through anonymity.

1.7. Defining of Key Terms

1.7.1. Marketing Communication

Marketing communication is defined as a process for planning, executing and monitoring the brand messages that create customer relationships (Duncan, 2005).

1.7.2. Consumer Buying Behavior

Consumer buying behavior is the study of how and why people consume products and services. It also investigates behavior which is the earlier process of buying, the process of purchasing and the next purchase after buying (Yakup & Savl, 2011).

1.7.3. Advertising

Advertising is any paid form of non-personal presentation & promotion of ideas, goods, or services by an identified sponsor. In simple words, Advertising is a means of informing and communicating essential information (Kotler & Armstrong, 2008).

1.7.4. Sales Promotion

Sales promotion is any initiative undertaken by an organization to promote an increase in sales, usage or trial of a product or service (i.e. initiatives that are not covered by the other elements of the marketing communications or promotions mix). Sales promotions are varied (Kotler, 2011).

1.7.5. Personal Selling

Personal selling is the oral presentations made by the individual salesperson. In this case a conversation with one or more prospective buyers who intended to create sales (Kotler, 2003).

1.7.6. Social Media

Social media marketing is about using social networking platforms such as Facebook, Twitter and Pinterest to deliver a message. It takes advantage of these channels’ massive user bases, innate engagement opportunities, and potential for virality to connect to consumers directly and spread information rapidly (Włodarczyk, 2014).

1.8. Chapter Summary

This chapter has explained how the study was undertaken to establish the outcomes of consumer purchase patterns of fast moving consumer goods as a result of marketing communication. They key objectives covered were to understand how they interact with advertising, sales promotions, personal selling and social media, and how each has impacted their purchase behavior. This study is beneficial to FMCG management teams, consumers, marketing professionals and academicians & researchers. The study was covered amongst purchase decision makers in the household who had a household net income of KES 40,000 within the ages of 18 to 45 years old from both gender. 385 interviews were conducted in Nairobi and the full study took a period of 4 weeks.

Chapter two covers the literature review that was done on how each marketing communication strategy (advertising, sales promotions, personal selling and social media) affects consumer purchase behavior. The literature review used the available literature in books and journals. Chapter three presents the research methodology that was used to actualize the study objectives by establishing the research design which took a quantitative approach, the population and sampling design, data collection method, research procedures and data analysis methods. Chapter four provides the results and findings while chapter five presents a discussion on the findings of the research as guided by the specific research objectives, and thereafter conclusion and recommendation of the study was given

CHAPTER TWO

2.0. LITERATURE REVIEW

2.1. Introduction

This chapter analyses the literature available in books and journals on how the four marketing communication strategies in this study affect consumer purchase behavior on FMCG goods. The marketing communication strategies are the independent variables under study and they include: advertising, sales promotions, personal selling and social media. They therefore guide the 4 priorities for this chapter: Section 2.2 discusses how advertising affects consumer purchase behavior of FMCG goods, section 2.3 discusses how sales promotions affects consumer purchase behavior of FMCG goods, section 2.4 discusses how personal selling affects consumer purchase behavior of FMCG goods and finally section 2.5 covers how social media affects consumer purchase behavior of FMCG goods.

2.2. The Effects of Advertising on Consumer Purchase Intention

Etzel, et al., (2007) describes advertising as consisting of all activities involved in presenting to a group a non-personal, oral or visual, openly sponsored identified message regarding a product, service, or idea. The message, called an advertisement, is disseminated through one or more media and is paid for by the identified sponsor. These media include broadcast (television and radio), online (emails, websites, social media) and print (newspapers, billboards, brochures, etc.). This study and the literature review in this section mainly observes purchase behavior as a result of advertising exposure regardless of the channel used.

Advertisements are made to not only promote products and the brand but also increase the likelihood that people will buy the products. A positive attitude toward an advertisement predicts a positive attitude toward the brand and also increases the likelihood that the consumer will want to purchase products from the brand in the future (Storme et al., 2015). According to a study done by Saadeghvaziri et al., (2013), the results show that attitude toward online advertising is a statistically significant and positive predictor of web users’ purchase intention. Infact, attitude

toward Web advertising explained 38 percent of the variance in respondents’ purchase intention. Web advertising consists of different forms such as emails, pop up messages, Web sites, and banner ads. These findings suggest that marketers should invest time and money into providing consumers with the afore mentioned beliefs that will likely lead to forming positive attitudes. These positive attitudes, in turn, will likely result in favorable consumer behavior (Saadeghvaziri et al., 2013). In agreement to this, Massey, et al., (2013) research findings show that if respondents like an advertisement, this will improve their attitude towards the advertiser, and this in turn will improve their attitude towards the brand. This is important because one's attitude towards the brand strongly influences purchase intent across all four cultural groups, for both the ethical and unethical advertisements.

On the other hand, in a study done by Fam-Kim, et al., (2013) in five Asian cities (Hong Kong, Shanghai, Jakarta, Bangkok and Mumbai) more than two thirds of the respondents in each city claimed they would not purchase the advertised product/service if it consists disliked executions (Fam-Kim, et al., 2013). The dislike attributes in this case were: style, meaningless, character, exaggeration, irresponsive, violent and hard sell (Fam-Kim, et al., 2013). Massey, et al., (2013) also suggest that when advertising to culturally conservative groups, caution is required. According to his findings, such groups have lower purchase intent when they do not like the advertisement. Massey’s results therefore suggest that advertisers should factor in this additional stage of evaluation, i.e. building attitude towards the advertiser and the brand into their communication strategy (Massey, et al., 2013). This confirms that advertising can affect either negative or positive purchase behavior.

Besides positive and negative purchase behaviors, Massey, et al., (2013) also finds that advertising has both indirect and direct effects. He confirms that regardless of cultural group, or the perceived ethicality of the advertisement, the effects of the antecedent variables on purchase intent are mainly indirect, and operate via the universal paths. These universal paths are consistent with persuasive hierarchy models (Vakratsas & Ambler, 1999), as they represent a hierarchy in which earlier effects are a

precondition to actions such as purchase. Hence according to the persuasive hierarchy models, if mothers think an advertisement is ethical (cognitive response), they will in turn, like the advertisement (affective response), and will intend to purchase that product (conative response).

2.2.1. The Effects of Advertising Media Channels on Purchase Intention

As earlier established, advertising media include broadcast (television and radio), online (emails, websites, social media) and print (newspapers, billboards, brochures, etc.). Consumers once had a limited number of media channels from which to obtain product information and were forced to rely on word of mouth (WOM) and print media (e.g. newspapers, magazines) to learn about products in which they were interested (Woo et al., 2015). This changed radically in the twentieth century, as the number of media channels increased with the advent of radio and television (TV), revolutionizing the ways in which consumers could access information (Woo et al., 2015). Over the past two decades, the advent of the internet has again fundamentally altered the quantity and quality of information available to consumers. As a type of “new media,” the internet contains all of the information that was available from older media and, when used in conjunction with personal media devices such as smartphones and Tablets, allows consumers to obtain information anywhere, at any time (Woo et al., 2015).

In tandem with this evolution in information and communications technology (ICT), consumer purchasing behavior and corporate advertising strategies have also changed. Consumers are now able to gather information through various media channels at each stage of the purchase decision making process (need recognition, information search, alternative evaluation, purchase decision, and post purchase behavior). Accordingly, companies must determine the appropriate media channels through which to promote their products in order to reach target consumers. Different media channels will generate different marketing and communication results (Chen & Hsieh, 2012). Internet ads and searches are more effective marketing channels for attracting younger consumers, while newspapers, magazines, and WOM are likely to be more useful for targeting older consumers (Woo et al., 2015).

Facebook is the most popular social medium in the world. Duffett (2015)’s results confirm that advertising on Facebook has a positive influence on the behavioural attitudes (intention to purchase and purchase) of Millennials who reside in SA. The usage characteristics, log on duration and profile update incidence, as well as the demographic influence of ethnic orientation also resulted in more favorable perceptions of Facebook advertising. It is estimated that Millennials will have a combined purchasing power of $2.45 trillion world wide by 2015. It can be assumed that social communications in the form on online reviews, posts and word of mouth (WOM) will play a large part in driving purchase decisions (Priyanka, 2013). However, Reuters and Ipsos (2012) revealed that one in five Facebook users had purchased products as a result of advertisements and/or comments that they viewed on Facebook. This rate increased to nearly 30 per cent who were aged 18 34. Facebook & ComScore (2012) disclosed that 4 per cent of consumers bought something within a month after being exposed to earned brand impressions from a retailer. The exposure also increased consumers’ intention to purchase. Rich Relevance (2013) revealed that consumers who made purchases, owing to Facebook advertising, were double in comparison to Pinterest and Twitter. Facebook also had the greatest income per session.

In regards to traditional media, Woo’s et al., (2015) results show that consumers’ preferences for media channels in each product category when socio demographics and lifestyle variables are held constant gives relatively larger estimates for broadcast TV and WOM, while those for newspapers and magazines are relatively smaller; this suggests that consumers’ product purchase decisions are affected most by broadcast TV and WOM and least by newspapers and magazines. SMS advertising has also been taken up as an advertising channel and it has had positive attitudes and acceptance. Yusta, et al., (2015) reports that future willingness to receive mobile advertising messages will depend on the attitude towards the brands involved in the campaign. Recent report reveals that South Korea has one of the toughest jurisdictions for data privacy compliance in the world, supported by the fact that up to 80 per cent of Koreans experienced the theft of their personal details in the past decade (Parsons & Colegate, 2015). However prior research demonstrates that such consumers tend to accept the advertising positively if they have trust in the advertisers (e.g. Izquierdo et al., 2015) Contrary to the previous researches (Drossos et al., 2013), the Dix et al., (2016) study

did not find any positive relationship between consumers’ intentions to receive SMS advertisements and their behavioural response to those advertisements. This may happen due to the excessing SMS advertising that consumers receive every day.

2.2.2. The Role of Celebrity Advertising on Purchase Intention

An advertising spokesperson can have a significant effect on the attraction and retention of viewers’ attention. Attention enables the development of brand awareness, which forms attitude, influencing purchase intentions (Chang & Chang, 2014). According to Felix and Borges (2014), consumers who pay more visual attention to a spokesperson in an advertisement may develop more positive attitudes either toward the spokesperson or toward the advertisement. The attitude toward the advertisement influences the attitude toward the brand, which in turn influences purchase intentions (Antioco et al., 2012). When the aim of the advertising campaign is to form attitude, then selecting a celebrity as the FMCG advertising spokesperson is recommended, but if the aim of the FMCG advertising campaign is to enhance brand awareness, it is recommended to select a non-celebrity spokesperson (Pilelienė & Grigaliūnaitė, 2017).

Research suggests that a celebrity spokesperson in an advertisement elicits more positive attitudes than a non-celebrity spokesperson, however the level of purchase intentions does not differ for the brand advertised by a celebrity compared to the brand advertised by a non-celebrity spokesperson. (Pilelienė & Grigaliūnaitė, 2017). The study found that celebrities’ likeability and their attractiveness have the greatest impact on both consumers Attitude and their purchase behavior (Mansour & Diab, 2016).

The effects of advertising creativity on perceived value are fully mediated by perceived product quality. What is more, the effects of advertising creativity on retailer brand attitude and retailer purchase intention are fully mediated by perceptions of product quality and value. The latter results clearly support our proposed logic that the creativity signal primarily affects quality perceptions of the focal object (namely, the product), and that these perceptions are then transferred into positive evaluations of the retailer. The results thus support our proposed logic that advertising creativity serves as a quality cue, thereby affecting the perceived value for a product, as well as retailer attitude and purchase intention (Modig & Rosengren, 2014)

2.3. The Effects of Sales Promotions on Consumer Purchase Intention

Sales promotion consists of a diverse collection of mostly short‐ term incentives designed to motivate consumers or the trade to purchase a product immediately and/or in larger quantities by lowering the price or adding value (Lamb et al., 1996). These include coupons, samples, premiums, contests, point‐ of‐ purchase displays, and frequent‐ buyer programs, etc. It is estimated that FMCG manufacturers spend about $1 trillion annually on promotions. In addition, promotions play an important role in the FMCG industry as a significant driver of sales (Nielsen, 2014). For example, Cohen et al., (2017) illustrate the effectiveness of temporary price reductions in boosting sales using real data by investigating the prices and sales for a particular brand of ground coffee in a supermarket over 35 weeks. They observe that this brand was promoted during 8 out of 35 weeks (i.e., 23% of the time); and that promotional sales accounted for 41% of the total sales volume (Cohen et al., 2017).

Schultz and Block (2014) also confirm that sales promotions affect consumer purchase behavior. One of the questions they asked in their study in the U.S. was “What sales promotional tool most influenced your purchase behavior toward ______brand?” 55.8 per cent of the respondents reported it was “coupons in newspapers or inserts” that influenced or greatly influenced them. This was followed by “product samples delivered to the home”, the third one was “product samples in the store” at 48.2 per cent, and the fourth most impactful promotional activity, store loyalty cards, with 47.7 per cent of all respondents. The most surprising promotional tool found in this analysis was the rapid and continuing growth of consumers reporting that retail store shopper cards had a major influence on their purchase behaviors; an approximately 70 per cent growth rate

Heilman et al., (2011) also reports on free sampling as very effective in inducing trial, especially among lower educated consumers. For consumers who are planning to buy the product in the promoted category, free sampling can encourage switching from the planned to the promoted brand. For consumers who do not have such previous plans, free sampling can “draw” them into the category and encourage category purchase. Samplers' interactions with the person distributing the sample or 16

with other samplers at the scene also seem to boost post sample purchase incidence (Heilman et al., 2011)

Lowe and Barnes (2012) also confirm that sales promotions induce purchase intention in their study on line extensions. They established that it would be more effective to promote line extensions with a Buy One Get One Free (BOGOF) sales promotion than with a 50 per cent off promotion. This superiority of the BOGOF promotion to the 50 per cent off promotion in the context of line extensions is based on the findings that: the BOGOF promotion, which requires consumers to buy at least two products, is more likely to accelerate purchase quantity and induce stockpiling than is the 50 per cent off promotion, which does not make any quantity related requirements (Lowe & Barnes, 2012).

2.3.1. Discounts and Price Cuts as Common Promotion Tactics

A promotion tactic that is commonly used by retailers is temporary price reductions. Somervuori and Ravaja (2013) measured psychophysiological responses to different price levels and found that low prices induced positive emotions. Further to this, Luong and Slegh (2014) report that consumers perceive an attractive difference between certain percentage level discounts (e.g. 10 and 50 per cent) but not at other levels (e.g. 50 and 75 per cent). Price discounts are interesting because they are more complicated for consumers to interpret and because they signal meanings unique from price. By their very nature, price discounts are harder for shoppers to evaluate than price because they require use of a more demanding cognitive process (Biswas et al., 2013).

When customers see a product that sells at list price, they receive only one piece of price information. In contrast, price discounts require customers to process several pieces of price information to calculate the selling price and then evaluate the deal; price discount information to process includes percentage and/or dollar amount of price discount, original price and calculation of the selling price. In the case of the percentage discount amount, customers also need to conduct an additional cognitive task to Figure out how much money they actually save, such as subtracting the selling price from the original price (Biswas et al., 2013).

Competition among potentially more convenient brick and mortar establishments and online retailers has resulted in significant price reductions, especially for online retailers (Petro, 2012). However, in higher risk channels like online stores, price discounts may have more significant effects on customers’ negative perceptions than price promotions in brick and mortar stores (Lee & Stoel, 2014). Experiential information is missing from an online shopping experience because shoppers are unable to inspect or try to use the products. Thus, many online shoppers rely heavily on price related information to evaluate products; and shoppers may infer a highly discounted product as having some unknown defect or risk. (Lee & Stoel, 2014).

When it comes to bundled offerings, Harris and Blair (2012) report that consumers may not process item price information when evaluating bundled offerings unless circumstances are conducive to doing so. This finding adds to an emerging perspective to the bundling literature, which has by and large assumed that consumers process item information. We find that responsiveness to bundle discounts (and the likelihood of piecemeal price processing) is, in general, enhanced when item information is more salient and when the purchase situation is unfamiliar (Harris & Blair, 2012)

Huang and Yang (2015) present two cases where as in their first experiment, the results show that offering quantity discounts e.g. “4 for 30% off” can result in greater willingness to buy a single product at the full price than offering promotions with a low quantity discount e.g. “2 for 30% off”. In their second experiment, the results show that when the missed quantity discount is based on dollars rather than on the number of pieces “Buy $100, get 30% off” versus “Buy 4, get 30% off”, the effect of purchase quantity on willingness to buy is enhanced (Huang & Yang, 2015).

In his journal, Huang et al., (2014) finds that price promotion activities at Starbucks in Taiwan had a favorable effect on customer quality evaluations and positively influenced repeat purchase intentions. Prior to Huang’s paper, Tong et al., (2012) examined ladies' buying behavior during shoes sales promotions in Malaysia, using the highly validated Belk's Model. Despite the multiracial society 18

in the country, ethnic group interaction on the model did not indicate impact on consumer differences affecting the sales promotion purchase during a specific festive season sale, all ethnic groups take full advantages of the sale (Tong et al., 2012).

2.3.2. Sales Promotion and Impulsive Purchase Behavior

Numerous studies confirm the global prevalence of impulse buying behavior and that over half of consumers visiting a shopping mall will make impulse purchases (Hultén & Vanyushyn, 2014). Clothes have been reported to be among the most frequently impulse purchased items (Pornpitakpan & Han, 2013). Consequently, it is the norm for retailers to design stores and display products in ways which encourage impulse buying i.e. through sales promotions (Hultén & Vanyushyn, 2014). Further to this, Tifferet and Herstein (2012) found that female shoppers make more impulse purchases than men. Which can be explained by females being more susceptible to sensory cues from touching an item than men.

Horváth and Birgelen (2015) investigated the behavior and purchase decisions of compulsive buyers and found that they value emotional and social benefits but often decide to buy “more and cheaper” items to achieve variety in their purchases. For example, clothes have been reported to be among the most frequently impulse purchased items (Pornpitakpan & Han, 2013) a person who enters a shop to buy a suit may make impulse purchases of other items such as shirts and ties which are perceived as inexpensive in comparison with the main item (the suit). They liked to try and buy new things – whether a different cut from their usual brand or a different brand. They engage in more brand switching than non-compulsive buyers, even if they were satisfied with a brand. The higher degree of sale proneness characterizing compulsive buyers and the transaction value they experience from price promotions (Kukar et al., 2012) hint at this higher brand switching tendencies.

Liu (2013) points out that when the time to purchase is short, consumers become easily influenced by a good deal even if the total utility is less or negative. Additionally, consumers experiencing short versus long time to purchase process positive and negative information differently. When consumers experience limited

information processing, they make decisions simply based on the attractiveness of the deal rather than the overall desirability of the product (Liu, 2013). For example, consumers increasingly rely on consumer ratings as important sources of information when making online purchase decisions (Godes & Silva, 2012). While high consumer ratings typically enhance the attractiveness of a product, products with low consumer ratings usually suffer from less consumer interest and, subsequently, lower sales and profitability. However, an effective price promotion may compensate for the negative features of the product and potentially increase consumers’ perceptions of the product’s value.

2.3.3. Sales Promotion as a Dependent Channel in Influencing Purchase Intention

As earlier established, price deals are recognized as the most powerful form of sales promotion; however, marketers should be cautious in determining a price promotion scheme. It is suggested that price promotions be used in combination with advertising or other sales promotion tools to increase brand awareness and image and to diminish negative effects on brand evaluations (Huang et al, 2014). Huang confirmed that shoppers with a favorable view on direct mail marketing and TV commercials respond more positively to in store promotion. Hence, the interactive effect of the three promotional channels increases the shoppers' general impulse purchase tendency (Hultén & Vanyushyn, 2014).

A study done by Millward Brown in 2012 with South African millennials established that Facebook advertisements that are connected to a physical in store promotion would actively draw those who are not inclined to make online purchases to the actual store to purchase. 45% per cent agreed that they purchase brands on sale as opposed to their preferred brands (Symphony, 2013). Marketers should therefore attempt to stimulate interactivity and word of mouth by proactively endorsing the sharing of marketing communication content between Facebook users by linking it to competitions, discounts, giveaways and other sales promotions, which would stimulate an increase in behavioral activities (Millward Brown, 2012).

2.4. Effects of Personal Selling Affects Consumer Purchase Behavior

Firms nowadays face an austere competitive marketplace where achieving sales goals has become a perpetual challenge. For example, empirical evidence suggests that in 2013, only 55 per cent of salespeople achieved their sales quotas (CSO Insight, 2014). The sales force plays an important role in driving firm profitability (Lassk et al., 2012) and have now been transformed to knowledge brokers (Bendixen et al., 2014) through personal selling.

Kotler (2013) notes that personal selling is a useful vehicle for communicating with present and potential buyers. Personal selling involves the two-way flow of communication between a buyer and seller often in face to face encounter designed to influence a person’s or group’s purchase decision (Kotler, 2013). A good example of personal selling is found in department stores on the perfume and cosmetic counters. A customer can get advice on how to apply the product and can try different products.

In a study done in Nigeria, findings from the study revealed clearly that personal selling is more persuasive among the marketing communication mix element. It aims at consolidating customers and maintaining the buyer seller exchange relationship. Personal selling as a business strategy, helps representatives of a company to explain to their clients/customers how well the products/services can satisfy their needs. Therefore, Organizations should focus more on the customer by adopting personal selling as a marketing strategy (zoltanpolla.com, 2017).

2.4.1. The Role of Technology in Personal Selling

With advances in technology, personal selling also takes place over the telephone, through video conferencing and interactive computer links between buyer and seller. Despite this, it still remains a highly human intensive activity despite the use of technology and scholars argue that technology cannot replace the unique functions of the salesperson (Ahearne & Rapp, 2010). Effective selling is highly dependent upon the salesperson’s selling skills and affective commitment to the selling situation (Simintiras et al., 2013).The evolving state of internet marketing (Cummins et al., 2013) is a result of worldwide growth in the number of Web sites and users, from 130

Web sites and 14 million users in 1993 to almost 673 million Web sites and 2.8 billion users in 2013 (Internet live statistics, 2014). Evolving Internet, social media and other technology enabled tools and the interaction patterns being created by such tools are transforming how salespeople interact with prospects and customers, and how organizations manage their sales force (Dixon & Tanner, 2012).

Since personal selling messages are not controlled, they may lead to inconsistency which in turn leads to confusion of the client (Cravens, 2012). However, managing the organization’s or an individual’s presence across multiple platforms has been revolutionized to relatively simple processes with the advent of social media management tools (e.g. Hootsuite, Buffer, TweetDeck, SproutSocial) and mobile technologies (Andzulis et al., 2012). Emergent buyer–seller interactions, engagement platforms and sales technologies have important ramifications for the value creation process for consumers, salespeople and the organization (Kuruzovich, 2013).

The evolving internet is also reducing the role of personal selling in purchase behavior. In the past, sales persons played a role in both the pre-purchase and purchase stages (Rippé et al., 2015). We now see that their personal selling role has reduced in the pre purchase stage as consumers now have continuous and excess information as the norm; mobile devices are used to multi task and manage the overabundance of available information that guides their purchase behavior even in terms of which channel to buy from (Parment, 2013). In store, it is these salespersons’ skills that allow connections with the consumer through verbal and nonverbal communication that adapts to consumer’s needs, thereby increasing trust (Orth et al., 2013) with trust leading to retail store patronage. Retail salespeople often have the most interaction with a retail firm’s customers, yet compensation and training are minimal.

Liu et al., (2013) reported that the store trust was a stronger influencing factor for the in-store purchases than the online trust was for internet purchases. His overall findings sustained the notion that salespeople with higher levels of active empathetic listening (AEL) will have higher quality relationships, and be regarded as more trustworthy. Further, when levels of trustworthiness are high the level of

relationship quality is higher which results in higher sales performance (Drollinger, 2012).

2.4.2. Building Trust for Effective Personal Selling

For retail services, consumers always look for tangible cues like store image (e.g. Hsu et al., 2010) and behavior of sales staff that may help them reduce perceived risk (Jayawardhena & Farell, 2011). The appearance of frontline salespersons represents the first impression of the company in the customer's mind. Their service behavior and relationship with the customer also add value to the product/service and provide psychological and social utilities (Lloyd & Luk, 2011). In this connection, frontline sales personnel serve as risk reliever. This risk reduction role will be more significant if the sales staff is perceived as trustworthy (Sirdeshmukh et al., 2002; Wong & Sohal, 2003)

Trust has been conceptualized as existing when one party has confidence in an exchange partner's reliability and integrity (Arnott, 2007). The importance of trust in retailing overall has not received much attention, although a few studies have reported a significant role of trust in the retail context in company‐ customer relationships (Too et al., 2001), in salesperson‐ customer relationships (Ball et al., 2004), and in online retailers (Nassir et al., 2008). Risk emerges when the consumer feels uncertain of the outcome associated with the purchase from a retail outlet. Previous empirical studies have proved that this is a common phenomenon in the retail sector e.g. (Diallo, 2012) and the types of risks frequently experienced by consumers include financial, physical, time, and psychosocial (Mitchell & Harris, 2005).

A consumer's trust in the firm / company / brand could be affected by the perceived trust of the business context in which it operates (Grayson et al., 2008). Grayson et al. (2008) refer interpersonal trust and organization‐ specific trust as narrow‐ scope trust and the level of trust of an industry, or a country, as broad‐ scope trust. Interpersonal trust is more influential in shaping exchange activities in a business context where broad‐ scope trust is low because it will perform a safeguarding function to reduce the perceived risk inherent in the purchase (Grayson et al., 2008). In China, the emergence of various types of retail formats in the past 15 years has

offered more choices for shopping to Chinese consumers; but the blooming of China's retail industry has resulted in the emergence of some retailers who operate their business in an unethical, and sometimes even illegal, way by selling fake products and over‐ priced products. Such behavior by some retailers has prompted Chinese consumers to have concern about fake products, product safety, over‐ priced products and poor product quality (Lloyd & Luk, 2011); as such, broad‐ scope trust of the local retail sector is low and interpersonal trust thus will be more influential in shaping buying behavior.

2.4.3. Strategic Importance of a Salesperson

Store image is made up of a set of store attributes which signify what the store is about to the customer (Diallo, 2012; Hsu et al., 2010) and it is an important clue of what the store can deliver at its best that benefits consumers and helps minimize the perceived risk to facilitate the choice decision (Bigne & Blesa, 2003; Mitchell & Harris, 2005; Semeijin et al., 2004). The emergence of relationship marketing as a paradigm to explain buying behavior has drawn the research attention from non human attributes such as brand mix, price levels, etc. to service‐ based and human based attributes like the quality of service and the behavior of salespersons (Semeijin et al., 2004; Thorbjornsen & Supphellen, 2011), to provide for a more comprehensive and accurate measurement for store image. For instance, what became known as the Consumer Image of Retail Stores (Bearden & Netemeyer, 1999) includes items directly related to the frontline salesperson, like appearance and service behavior.

To most consumers, shopping is an activity that produces satisfaction from more than simply the utility of the merchandise bought. The social or personalizing shopper enjoys conversation with the frontline sales staff and will seek personal relationships with store personnel. The store environment including helpful personnel may thus positively influence feelings (Lloyd et al., 2011; Machleit & Mantel, 2001; Sharma et al., 2011). Schneider and Bowen (1995) have reported that the service attitudes and behavior of frontline service personnel create a lasting impression that determines customers' perception of the firm and customer satisfaction.

Other studies have demonstrated that a positive impression and feelings towards the frontline sales personnel are crucial for consumers' perception of store image and approaching behavior towards the store (Brown & Lam, 2008) and the perceived value of retail service and future purchase behavior (Sweeney & Soutar, 2001). In particular, this relationship would be stronger in people‐ dominant services like retail service (Brown & Lam, 2008).

2.5. Effects of Social Media Affects Consumer Purchase Behavior

Consumers once had a limited number of media channels from which to obtain product information and were forced to rely on word of mouth (WOM) and print media (e.g. newspapers, magazines) to learn about products in which they were interested. This changed radically in the twentieth century, as the number of media channels increased with the advent of radio and television (TV), revolutionizing the ways in which consumers could access information. Over the past two decades, the advent of the internet has again fundamentally altered the quantity and quality of information available to consumers. As a type of “new media,” the internet contains all of the information that was available from older media and, when used in conjunction with personal media devices such as smartphones and Tablets, allows consumers to obtain information anywhere, at any time (Woo, 2015).

While print media advertising expenditures decreased by 3 percent per year from 2009 to 2012, spending on internet media grew by 18 percent annually over the same time period (Zenith Optimedia, 2013). In tandem with this evolution in information and communications technology (ICT), consumer purchasing behavior and corporate advertising strategies have also changed. Consumers are now able to gather information through various media channels including the internet at each stage of the purchase decision making process (need recognition, information search, alternative evaluation, purchase decision, and post purchase behavior). (Chen & Hsieh, 2012). The internet has also facilitated social networking which enables connections with a network of people who share common interests or goals (Hsu, 2012).

Social media has become an imperative conduit for global marketing communications and is commanding a larger share of advertising budgets, 25

especially to reach the younger generation. Therefore, the value of advertising on social media such as Facebook, YouTube, LinkedIn, Twitter and others is of great interest to organizations, managers and academics (Saxena & Khanna, 2013). marketers are increasing their social media budgets with digital interactive advertising forecasted to reach $138 billion in 2014, a growth rate of nearly 15 per cent in comparison to 2013 (eMarketer, 2014a). Furthermore, the Middle East and Africa are predicted to have the highest social media advertising spend growth (64 per cent) in 2014 (eMarketer, 2014c). Social media, such as Facebook and Twitter, have played huge roles in the ways we work, study, travel, eat, entertain and make purchases (Bilgihan et al., 2013). With the emergence of social media and mobile technology, customers have begun to share their thoughts about, and assessments of, satisfactory and unsatisfactory service experiences without temporal or spatial constraints (Al Jabri & Sohail, 2012; Wilcox & Stephen, 2013). Research has demonstrated that social media and mobile technology have become crucial channels of information exchange prior to purchases (Lee et al., 2013; Kwon et al., 2011).

2.5.1. Consumer Engagement on Social Media and Its Effect on Purchase

Recent large scale commercial studies provide evidence that consumer engagement continues to be a problem for social media users. For example, IBM’s CMO Insights Global C Suite Study found that few companies engage with customers via social media and most have failed to “exploit the opportunities arising from the data explosion and advanced analytics” (IBM, 2014). Moreover, a TrackMaven’s (2016) study found that while social media content per brand rose by 35 per cent across varied platforms from 2014 to 2015, content engagement actually decreased by 17 per cent over that same time period. Even social media vendors who purportedly measure engagement have been unable to prove whether, or the degree to which, engagement correlates to metrics like loyalty or sales (Elliott, 2014). So, while almost nine in ten US companies with at least 100 employees have a social media presence for marketing purposes, how this translates into customer value remains a mystery (eMarketer, 2015).

A number of factors contribute to this engagement gap. First, social media is a relatively new marketing phenomenon, and there is thus a naiveté for how engagement should be created, tracked and measured (Barger & Labrecque; 2013; Schivinski et al., 2016). Second, with its many platforms and varied formats, social media has become a fragmented medium, making it difficult for companies to track and coordinate their efforts (King et al., 2014; Straker et al., 2015). This fragmentation, along with content saturation across channels, has placed greater cognitive demands on consumers, forcing them to either ignore content or become more selective in what they view and process (IBM, 2014). Lastly, in their search for short term sales gains, marketers over rely on social media to deliver sales promotions to consumers, and this continues to have a negative effect on brand equity (Schultz & Block, 2014).

Baker et al. (2016) studied the valence, channel and social tie strength of consumer engagement. Negative word of mouth had the strongest effect on purchase intention, although purchase intention was also influenced by the strength of the social tie between the communicator and the recipient. The format of the online word of mouth plays a role as well; specifically, the presence of photos in posts results in higher product interest and higher purchase intention for both search products and experience hedonic products (Lin et al., 2012).

2.5.2. Consumer Reviews on Social Media and its Effect on Purchase

Generation Y consumers in those countries that provide sound information infrastructure visit Facebook and Twitter to read reviews of services and products written by fellow customers. They are also emerging as micro bloggers who showcase retail products on online platforms and get insider information about brands through tweets (Bilgihan et al., 2013). He and Bond (2013) investigated the effect on forecasts of consumption enjoyment. They found that engagement in the form of reviews was most likely to result in potential purchasers adjusting their forecasts. Perhaps not surprisingly, credible reviews lead to higher purchase intentions (Jiménez & Mendoza, 2013).

What makes a review credible differs for search products versus experience products; however, reviews for search products are more credible if they provide 27

detailed information about the product, whereas reviews for experience products are considered more credible if the reviewer agrees with the review (Jiménez & Mendoza, 2013). The language of the review also affects product choice (Kronrod & Danziger, 2013). Illustrating the importance of reviews in general, de Langhe et al. (2016) showed that consumers rely heavily on average ratings of products to arrive at purchase decisions, despite a “substantial disconnect between the objective quality information that online user ratings actually convey and the extent to which consumers trust them as indicators of objective quality”.

With respect to brand attitude, Huang et al. (2013) demonstrated that the likelihood of a consumer sharing a viral video was linked to not only the consumer’s attitude toward the video but also the consumer’s attitude toward the brand. Moreover, the impact of attitude toward the brand had a significant impact on sharing. On the opposite end of the spectrum, Anderson and Simester (2014) showed that brand attitude may affect the likelihood of consumers posting negative product reviews without ever having purchased the product they are reviewing.

User generated content (UGC) in the form of reviews can affect consumers’ willingness to pay. In their study of dis preferred markers (discussed previously under “Brand Effects”), Hamilton et al., (2014) found that the presence of dis preferred markers in UGC increased willingness to pay for a product. Wu and Wu (2016) argue that willingness to pay varies across individuals and even within an individual depending on preferences for uncertainty. They offer a framework for quantifying willingness to pay based on consumers’ preferences for different review statistics. Positive eWOM often results in more positive opinions and purchasing behaviors by other consumers, while negative eWOM often results in negative opinions and purchasing behaviors (Cantallops & Salvi, 2014).

2.5.3. Facebook as the Most Effective Social Media Tool to Drive Purchase

Current Figures reveal that the largest online social medium in the world is Facebook, with 1.32 billion active members, and it is also the largest social commerce site that accounts for 85 per cent of all orders from social media (Shopify, 2014). Duffett (2015)’s results confirm that advertising on Facebook has a positive influence on the behavioral attitudes (intention to purchase and purchase) of Millennials who reside in 28

SA. According to Hsu (2012), the Facebook community has the following characteristics: shares company, product, or service information; communicates and shares marketing messages; expands networks; and receives feedback updates, which provide members with as many opportunities as possible to become involved and participate in the community. Furthermore, Facebook revenue from advertising has grown by 59 per cent during the past year to over $5.4 billion in 2014 (Facebook, 2014a), which is testament to the shift from traditional media advertising to digital interactive media advertising by organizations. It is estimated that Millennials will have a combined purchasing power of $2.45 trillion world wide by 2015. It can be assumed that social communications in the form on online reviews, posts and word of mouth (WOM) will play a large part in driving purchase decisions (Priyanka, 2013).

A review of Facebook’s global advertising performance indicated that click through rates had improved by 20 per cent from 2011 to 2012 (AYTM, 2012). Furthermore, the cost per click had risen by over a quarter and the cost per thousand increased by more than half. However, Greenlight (2012) found that 44 per cent of consumers did not ever click on Facebook advertisements, 31 per cent rarely did, 10 per cent often did and 3 per cent clicked regularly. While Associated Press and CNBC (2012) reported that over eight out of ten Facebook users never or seldom viewed Facebook advertisements or their sponsored content. However, Reuters and Ipsos (2012) revealed that one in five Facebook users had purchased products as a result of advertisements and/or comments that they viewed on Facebook. This rate increased to nearly 30 per cent who were aged 18-34.

Facebook and ComScore (2012) disclosed that 4 per cent of consumers bought something within a month after being exposed to earned brand impressions from a retailer. The exposure also increased consumers’ intention to purchase. Rich Relevance revealed that consumers who made purchases, owing to Facebook advertising, were double in comparison to Pinterest and Twitter. Facebook also had the greatest income per session. Bannister et al., (2013) found that the attitudes of US college students towards Facebook advertising were largely negative or indifferent. Respondents disclosed that Facebook advertisements were predominantly uninformative, irrelevant, uninteresting, and would, therefore, not generally click on them. Moreover, a majority of college students stated that they would not make a 29

purchase owing to Facebook advertising. Persuad (2013) used a controlled experiment among 96 young adults to explore the impact of interactivity and product involvement on respondents’ attitudes towards brands on Facebook and their intention to purchase. No significant results were found for interactivity, product involvement or intention to purchase. However, the study revealed that high levels of interactivity on Facebook were positively correlated to intention to purchase and favorable attitudes towards the brand.

2.6. Chapter Summary

This chapter has analyzed the literature available in books and journals on how the four marketing communication strategies in this study affect consumer purchase behavior on FMCG goods. The marketing communication strategies are the independent variables under study and they include: advertising, sales promotions, personal selling and social media. Chapter 3 covers the research methodology that was used to address this survey and this includes: the overall design, sample and sampling procedure, instrumentation, data collection, data analysis and the Ethics in research.

CHAPTER THREE

3.0. RESEARCH METHODOLOGY

3.1. Introduction

This chapter outlines the methodology that was used in this study to achieve the overall objective on investigating the effectiveness of marketing communications on consumer purchase behavior of FMCG goods in Kenya, and the four objectives outlined in chapter 1. The research design was descriptive in nature and took a quantitative approach. This chapter also covers the population and sampling design, data collection method, and research procedures and data analysis methods that were covered.

3.2. Research Design

Research design is a plan and structure of investigation that is conceived to obtain answers to the research questions and it can either be exploratory or descriptive (Coopers & Schindler, 2008). Explorative studies are undertaken when a new area is being investigated or when little is known about an area of interest (Polit et al, 2001). Based on the undertaken literature review, this study did not cover a new area and there was fair knowledge on how marketing communication strategies affect consumer purchase intention. In light of this, descriptive research was the most preferred research design for this study. Descriptive research is designed to provide a picture of a situation as it naturally happens. It may be used to justify current practice and make judgment and also to develop theories (Burns & Grove, 2003). For the purposes of this study, descriptive research was used to obtain shopping decision makers opinions on how marketing communication strategies i.e. advertising, sales promotions, personal selling and social media, affect their purchase behavior.

There are three main techniques used under descriptive research: observational methods, case-study methods and survey methods. This study used a survey method. Using this quantitative approach, we established the relationship between the dependent variable; ‘consumer purchase behavior’ and the independent variable; ‘marketing communication’. Quantitative research refers to an investigation of a phenomenon by testing a theory that can be measured numerically and analyzed statistically (Shafeek, 2009). Rather, it involves the collection of raw data from a large sample size with the 31

intention of generalizing the results to a wider population as well as future courses of action. Therefore, it allows researchers to provide statistical facts and estimates about relationships between constructs of the research interest as well as generalizing inferences about the defined target population (purchase decision makers of FMCG goods).

Despite the inadequacy of quantitative research design in generating theory and providing strong in-depth explanations of qualitative enquiry, it will still be useful in this study’s hypotheses verification, reliability and validity tests (Shammout, 2007). The major strengths of quantitative research are that measurement is reliable, valid, and can be generalized in its clear prediction of cause and effect (Neill, 2007). Leedy and Ormrod (2001) alleged that quantitative research is specific in its surveying and experimentation, as it builds upon existing theories. Thus, quantitative research is appropriate for this study since the issues in this particular research have been studied by other researchers, hence a substantial body of literature on the subject exists (Shafeek, 2009).

3.3. Population and Sampling Design

3.3.1 Population

A population is a group of people whom the outcomes of the research findings are generated from. It is generally the persons who possess certain characteristics or a set of features that make them fall within a certain group that is being studied or examined and analyzed so as to attain a certain result (Frankel & Wallen, 2000). It can also be referred to as the sum of all the number of units of a certain event that a research is being carried on and also which will represent observation of the same 24 kind (Kumekpor, 2002).

The population of this study was the main decision makers on the FMCG goods to be purchased in a household. Nairobi has 1 million households with an average of 4.4 people in a house hold (Kabintie, 2015). According to the Kenya National Bureau of Statistics (KNBS), Kenya’s middle class includes anybody spending between KES 23, 670 to Sh199,999 monthly – which is less than 25% of the population. This study targeted decision makers with a net income of KES 40, 000 after all deductions, making

them fall under middle class. This therefore made the population about 250,000 household decision makers.

3.3.2 Sampling Design

Sampling design is the process of selecting a representative sub section of the population to be studied for the findings to be generalized to the entire population (Cooper & Schindler, 2014).

3.3.2.1 Sampling Frame

According to Cooper and Schindler (2014) sampling frame is a series of each and every one of the clusters from where the sample range was collected from and it is suspiciously connected to the total population. From the sample frame, the researcher can be able to get the number of subjects, respondents, elements and firms to select from in order to make a sample. This makes it very important so ensure that the sample frame is unbiased, current and accurate (Saunders, Lewis & Thornhill, 2012). In this study, the sample consisted of both males and females purchase decision makers who were 18-45 years old as this age group is most likely to be influenced by communication to change brands. This frame also included a fair purchasing power by selecting those with a household net income of KES 40,000 and above, after all deductions.

3.3.2.2 Sampling Technique

Probability sampling techniques are primarily used in quantitatively oriented studies and involve ‘‘selecting a relatively large number of units from a population, or from specific subgroups (strata) of a population, in a random manner where the probability of inclusion for every member of the population is determinable’’ (Tashakkori & Teddlie, 2003a, p. 713). Probability samples aim to achieve representativeness, which is the degree to which the sample accurately represents the entire population. This study used systematic sampling. This is a method of sampling which involves the selection of elements from an ordered sampling frame. The most common form of systematic sampling is an equal probability method. In an equal probability method, progression through the list in a sampling frame is treated circularly, with a return to the top once the end of the list is passed (Almale, 2014). The sampling starts by selecting an element from the list at random and then every kth element in the frame is selected, where k, the

sampling interval (sometimes known as the skip): This is calculated as: k=N/n. Where, n is the sample size, and N is the population size. Using this procedure, each element in the study population has a known and equal probability of selection (Almale, 2014). Respondents were sampled from supermarkets and other shopping areas to enable us freshly capture the influences on their purchases through exit interviews. In order to implement systematic sampling, every 5th respondent exiting the retail outlet was targeted for an interview.

3.3.2 Sample Size

A sample size is a representative subset of the population to be studied and the findings generalized to the entire population (Schindler & Cooper, 2014). The degree of confidence related to the data has to be estimated and associated with the sample data size (Pervez & Kjell, 2002). The larger the population size, the smaller the percentage of the population required to get a representative sample, however Schindler and Cooper (2014) advise that the greater the desired precision of the estimate, the larger the sample should be.

Our population was estimated at 250,000 household shopping decision makers in Nairobi. The following sample size formula for infinite population (more than 50,000) is used to arrive at a representative number of respondents when population estimate is known (Godden, 2004):

Where: n = Sample Size for infinite population Z = Z value (e.g. 1.96 for 95% confidence level) P = population proportion (expressed as decimal) (assumed to be 0.5 (50%) M = Margin of Error at 5% (0.05)

To simplify the process of determining the sample size for a finite population, Krejcie & Morgan (1970), came up with a Table using sample size formula for finite population. There is no need of using sample size determination formula for ‘known’ 34

population since the Table has all the provisions one requires to arrive at the required sample size. For a population which is equal to or greater than 100,000, the required sample size is 383. In view of this, the sample size for this study was 385 because the population was above 1000,000 and the focus in this population was specific to 250,000 middle income household shopping decision makers in Nairobi.

Table 3.1: Krejcie & Morgan Table Providing Sample Sizes

Source: Determining Sample Size for Research Activities. Educational and Psychological Measurement. (1970), 30, 607-610

3.4. Data Collection Methods

The primary data was collected through administering the questionnaire face to face using research assistants. The questionnaire was programmed on a mobile platform which research assistants downloaded on their phones, logged in and collected data on the programed questionnaire. This method was fast and accurate because the submitted data was automatically aggregated and downloaded. This method was also economical because it reduces printing costs, shipping costs, risk on damage of questionnaires during shipping. The questionnaires were largely closed ended and pre-coded but also included one open ended question in section 2, 3 and 4. The data collection process also endorsed anonymous to protect the respondent’s identity and allow them to answer freely knowing that the information collected would not be traced back to them but would be aggregated with other interviews. The researcher was also careful to avoid constructing a complex and lengthy questionnaire to encourage participation.

The questionnaire structure was in line with the study’s objectives and had 5 section sections that covered the key areas which included: general information, influence of advertising, influence of sales promotion and influence of personal selling on their purchase behavior. The main research variables were measured using interval scales of a 5-point Likert scale where 1 represented ‘completely disagree’ and 5 represented ‘completely agree’

3.5. Research Procedure

Before undertaking the actual study, a pilot test was done to test how respondents reacted to the questionnaire, if it had a flow, if the questions were well understood or needed to be adjusted, and if the length of the questionnaire was friendly to the consumer. The data collection was done through administering the questionnaire face to face using field research assistants. The questionnaire was programmed on a mobile platform which research assistants downloaded on their phones, logged in and collected data on the programed questionnaire. These questionnaires were administered to respondents exiting shopping centers. Some of the ways we addressed the response rate was by explaining to respondents the benefits of their participation in the study, and emphasizing on respondent confidentiality through anonymity and outlining that their responses would be aggregated with data from other respondents.

3.6. Data Analysis Methods

According to Kothari (2004), data analysis procedure includes the process of packaging the collected information, putting it in order and structuring its main components in a way that the findings can be easily and affectively communicated. Data analysis is not an end in itself; its purpose is to produce information that aids to address the problem at hand. Once fieldwork was completed, a sample of the data collected was quality checked for reliability and validity prior to data analysis. All incomplete surveys were discarded from the analysis. During data analysis, editing, coding and tabulation was carried out. Statistical Package for Social Sciences (SPSS) software was used to carry out the analysis of the collected data. The verbatim recorded in the open-ended questions was coded in short phrases to make it easier to enter into the analyzing software, for more analysis. The open-end data was analyzed using a two-step

procedure, as suggested by Anderson and Gerbing (2001). It was combined with the rest of the close ended data and processed using the Dimensions software to give the output in SPSS which was converted to excel Tables to enable charting and reporting.

3.7. Chapter Summary

This chapter has presented the research methodology that was used to actualize the study objectives by establishing the research design which took a quantitative approach, the population and sampling design, data collection method, research procedures and data analysis methods. The following chapter presents the study’s findings.

CHAPTER FOUR

4.0. RESULTS AND FINDINGS

4.1. Introduction

The chapter provides results and findings of the data collected according to the research objectives and analysed as per the research design. The results and findings are presented in tables and figures. The first part gives the response rate and the second part provides background information on the demographic representation of the respondents. The third part evaluates how purchase behaviour is influenced by advertising, the fourth part on how it is influenced by sales promotions, the fifth part on how it is influenced by personal selling, and the sixth part on how it is influenced by social media. The seventh part gives a general overview on consumer purchase behaviour. The eighth part does a correlation on the four marketing strategies versus purchase decisions. The ninth and final section is the summary of the whole chapter.

4.2. Response Rate

The response rate is utilized to find out the statistical authority of a test and the higher the response rate the higher the statistical power. In this study, the researcher administered 395 questionnaires and all were completed. This represents a response rate of 100% as shown in Table 4.1.

Table 4.1: Response Rate

Questionnaires Number Percentage Filled and collected 395 100 Non-Responded 0 0 Total 395 100

4.3. Background Information The research analysed data with regard to the demographic factors and the results were presented as follows:

4.3.1. Gender Figure 4.1 shows the gender of the people who participated in the study. 55% were male and 45% were female. This means that majority of the respondents in the study we male.

Figure 4.1: Gender

4.3.2. Age Figure 4.2. portrays the age of the respondents who participated in the study. The figure establishes that the youngest age group 18 – 24 year olds were 37%, the next age group 25 - 30 year olds were 38%, the next age group 31 – 40 year olds were 17%, the 40 – 45 year olds were 8%, and only one person was above 45 years as shown in Figure 4.2 below. This means that majority of the respondents in this study were between 18 – 30 years old.

Figure 4.2: Age

4.3.3. Decision Making Capacity The study’s focus was on decision makers of brands purchased in households. Table 4.2. demonstrates the decision-making capacity of the respondents. The results revealed that 257 respondents were responsible on deciding on or purchasing the products that they used and this represents 70% of the sample, whereas 119 respondents were partially responsible on deciding on or purchasing the products that they used and this represents 30% of the sample as shown Table 4.2 below. This means that majority of the respondents have full capacity in deciding on the products purchased.

Table 4.2: Decision Making Capacity

Variable Distribution Frequency Percent I am fully responsible in deciding on / purchasing the 275 70 products that I use I am partially responsible in deciding on / purchasing the 119 30. products that I use Total 394 100

4.3.4. Nationality Table 4.3 provides the nationalities that were involved in this survey. Results show that 392 respondents are Kenyan representing 99% of the total sample, and 2 respondents came from other countries representing 1% as shown in Table 4.3. this shows that respondents were mainly Kenyan citizens.

Table 4.3: Nationality

Variable Distribution Frequency Percent Kenyan 392 99 Other 2 1 Total 394 100

4.3.5. Net House Hold Income Table 4.4. shows the net household income distribution for the sample. The results reveal that those earning a net household income below Kshs 40,000 are 7 respondents representing 2% of the sample. Those earning a net household income of Kshs. 40,000 to 60,000 are 247 respondents representing 63% of the sample, Kshs. 60,001 to 80,000 are 91 respondents representing 23% of the sample, Kshs. 80,001 to 100,000 are 35 respondents representing 9% of the sample, Kshs. 100,001 to 120,000 are 7 respondents representing 2% of the sample, above Kshs. 120,000 were 5 representing another 2% of the sample.

This shows us that a large proportion of this study was done amongst people who earn a net household income of between Kshs. 40,000 to 80,000. This is aligned to the project which had a high focus on middle income earners classified as those making Kshs. 25,000 and above by KNBS.

Table 4.4: House Hold Income

Variable Distribution Frequency Percent Below Ksh. 40,000 7 2 40,000 to 60,000 247 63 60,001 to 80,000 91 23 80,001 to 100,000 35 9 100,001 to 120,000 7 2 Above 120,000 5 2 Total 392 100

4.3.6. Occupation

Figure 4.3. provides the occupation of the respondents in the sample. Results show that almost half of the sample is employed 45%, a significant proportion are businessmen 31%, followed by students at 18%, unemployed at 4% and 2% identified themselves with other occupation categories as shown in Figure 4.3 below. This means that over three quarters of the respondents have a steady income; are employed or self-employed.

Figure 4.3: Occupation Status

4.3.7. Religion Figure 4.4 shows the religious faiths of the sample. Results show that 44 % of the respondents are protestant, 23% are catholic, 3% are Muslim, 2% do not have a religion and 1% are Hindu and Buddhist, and 24% belong to other are other unspecified religions as shown in Figure 4.4 below. This shows that majority of our sample is Christian as 67% are either protestant or catholic.

Figure 4.4: Religion

4.3.8. Marital Status Figure 4.5 portrays the marital status of the respondents in the sample. Results show that 55% are single- never married before, 38% are married/living together, 3% are divorced, 2% are widowed and 2% never answered. This shows that majority of the respondents were unmarried.

Figure 4.5: Marital Status

4.3.9. Level of Education The researcher sought to investigate respondent’s level of education. Findings revealed that 6 of the respondents have no formal education and this represent 2% of the total sample, 9 have studied up to primary school this represents 2% of the sample, 46 have studied up to secondary school this represents 12% of the sample, 324 respondents have studied up to college/university this represents 82% of the total sample, and 10 of the respondents never answered this represents 2% of the total sample as shown in Table 4.5 below. This means that the sample largely consists of well-educated people.

Table 4.5: Level of Education

Variable Distribution Frequency Percent None 6 2 Primary school 9 2 Secondary school 46 12 College / university 324 82 Missing 10 1 Total 395 100.0

4.4. Advertising’s Effects on Purchase Intention

This section of the study covers the first objective which is to establish the effects of advertising on consumer purchase decisions on FMCG products. The study section addresses aspects such as: factors that influence trial purchase, advertisement channels used and their influence on purchase, elements of an advertisement and their influence on purchase decisions, whether poor advertising influences non-purchase, the times of day they interact with advertisements.

This section analysed the significance of some of the aspects in this section using descriptive statistics such as: mean, standard deviation, correlation, and regression. In order to measure the central tendency, statistical mean is used whereas standard deviation depicts how the result is dispersed from the mean.

4.4.1. Factors That Influence Trial Purchase The researcher sought to investigate factors that influence customers to try a new product or service. 101 respondents stated that they try a new product or service due to recommendation from friends or family this represents 27% of the sample, 90 respondents were because of personal experience representing 21% of the sample, 28 respondents were because of promotion representing 7% of the sample and 13 respondents was because of celebrity influence representing 3% of the sample.

Table 4.6: Factors That Influence Trial Purchase

Variable Distribution Frequency Percent Recommendation from friends or family 101 27 Personal experience 90 23 Recommendation from shop attendants or experts 42 11 Promotions 28 7 Seeing famous people use the product 13 3 Missing 121 31 Total 395 100

4.4.2. Advertisement Channels of Interaction Table 4.7 shows the extent to which respondents interact with different advertisement channels. Answers were based on a scale of 1 – 5 where I meant ‘Not Likely at All’ and 5 meant ‘Very Likely’. This means that when the mean is closer to 5 the higher the interaction with that channel.

The results reveal that television has a mean of 4.07 and a standard deviation of 1.122, internet has a mean of 4.06 and a standard deviation of 1.187, radio has a mean of 3.12 and a standard deviation of 1.29, billboards have a mean of 2.99 and a standard deviation of 1.393, newspapers have a mean of 2.82 and a standard deviation of 1.371.

Table 4.7: Advertisement Channels of Interaction

Variable N Mean Std. Deviation Television 385 4.07 1.122 Internet 385 4.06 1.187 Radio 385 3.12 1.29 On Billboards 385 2.99 1.393 Newspapers 385 2.82 1.304 Magazines 385 2.54 1.371 Valid N (list wise) 385

4.4.3. Advertisement Channel Influence on Purchase Table 4.8 shows the extent to which the different advertisement channels influence purchase. The respondents answered based on a scale of 1 – 5 where 1 meant ‘No Influence at All’ and 5 meant ‘Very High Influence’. This means that the closer the mean is to 5 the higher the influence of that channel.

The results show that internet advertisement have a mean of 3.75 and a standard deviation of 1.311, TV advertisements have a mean of 3.62 and a standard deviation of 1.318, radio advertisements have a mean of 2.79 and a standard deviation of 1.304, billboard advertisements have a mean of 2.77 and a standard deviation of 1.433, newspaper advertisements have a mean of 2.65 and a standard deviation of 1.363, magazine advertisements have a mean of 2.52 and a standard deviation of 1.325.

Table 4.8: Advertisement Channel Influence on Purchase

Variable N Mean Std. Deviation Internet Advertisements 385 3.75 1.311 TV Advertisements 385 3.62 1.318 Radio Advertisements 385 2.79 1.304 Billboards Advertisements 385 2.77 1.433 Newspaper Advertisements 385 2.65 1.363 Magazine Advertisements 385 2.52 1.325 Valid N (list wise) 385

4.4.4. Correlation on Advertisement Channel and Purchase Decision A correlation analysis was run between the advertisement channels and purchase decision. Correlation coefficient denoted by r ranges between -1 and +1 and quantifies the direction and strength of the linear association between 2 variables. Pearson’s r value for the relationship between TV advertisements and purchase decision is 0.309, between radio advertisements and purchase decision is 0.282, between newspaper advertisements and purchase decision is 0.362, between magazine advertisements and purchase decision is 0.317, between billboard advertisements and purchase decision is 0.106, between internet advertisements and purchase decision is 0.341. Since all correlation r

Table 4.9: Correlation on Advertisement Channel and Purchase Decision

Variable Purchase Decision Pearson Correlation .309** TV Advertisements Sig. (2-tailed) .000 Pearson Correlation .282** Radio Advertisements Sig. (2-tailed) .000 Pearson Correlation .362** Newspaper Advertisements Sig. (2-tailed) .000 Pearson Correlation .317** Magazine Advertisements Sig. (2-tailed) .000 Pearson Correlation .106* Billboards Advertisements Sig. (2-tailed) .037 Pearson Correlation .341** Internet Advertisements Sig. (2-tailed) .000 N 385

4.4.5. Regression on Advertisement Channels and Purchase Decision A model summary is utilized when predicting the value of a variable based on the value of another variable. In this case, dependent variable (purchase) is the variable being predicted. The independent variable (advertisement channel) is the variable being used

to predict the value of other variables. The model summary is demonstrated in Table 4.10 below. It provides information about the regression line’s ability to offer a description for the total divergence in the dependent variable. From the table, the value of R is 0.484, R2 is 0.235, which means that 23.5 percent of the total variation in purchase has been explained by variability in advertisement channels. The value of F critical, F (79,258) =19.324 and the P value is (0.000) therefore significant. These results therefore show that there was a statistically significant difference in the mean between the various variables of what constitute advertisement channels.

Table 4.10: Regression on Advertisement Channels and Purchase Decision

Model Summary Mode R R Adjust Std. Change Statistics l Square ed R Error of R F df1 df2 Sig. F Square the Square Chang Change Estimate Change e 1 .484a .235 .223 .82653 .235 19.324 6 378 .000 a. Predictors: (Constant), Internet Advertisements, TV Advertisements, Radio Advertisements, Billboards Advertisements, Newspaper Advertisements, Magazine Advertisements ANOVAa Model Sum of df Mean Square F Sig. Squares Regression 79.207 6 13.201 19.324 .000b 1 Residual 258.229 378 .683 Total 337.436 384 a. Dependent Variable: Purchase Decision b. Predictors: (Constant), Internet Advertisements, TV Advertisements, Radio Advertisements, Billboards Advertisements, Newspaper Advertisements, Magazine Advertisements *p<.05

4.4.6. Advertisement Elements of Interest The researcher sought to investigate what customers look for in an advertisement. Findings revealed 56% of the respondents look for that brand they are familiar with and trust, 28 % product information, 8% price information, 3% never responded, 2% look for discount and deals. Whereas 1% celebrity and famous people, humour, and consumer level of interaction with the advertisement as shown in Figure 4.6 below

Figure 4.6: Advertisement Elements of Interest

4.4.7. Most Important Advertisement Element The researcher sought to investigate what customers look out for most in an advertisement. Findings revealed 33% of the respondents look for product information, 28% brand they are familiar with and trust, 18% price information, 11% discount and deals, 3% never responded, 3% customers level of interaction and 2% humour and as shown in Figure 4.7

Figure 4.7: Most Important Advertisement Element

4.4.8. Advertisement Elements to Improve The researcher sought to investigate what advertisers should improve on to make customers purchase the product. Findings revealed that respondents want advertisers to improve on product information 41%, price information 23%, brand familiarity and trust 16%, discounts and deals 9%, consumer interaction 4%, celebrity and famous people 3%, and humour 1% as shown in Figure 4.8 below. Those who never responded were 3%.

Figure 4.8: Advertisement Elements to Improve

4.4.9. Advertisement Elements’ Influence to Purchase Table 4.9 shows the extent to which the different advertisement elements influence purchase. The respondents answered based on a scale of 1 – 5 where 1 meant ‘No Influence at All’ and 5 meant ‘Very High Influence’. This means that the closer the mean is to 5 the higher the influence of that element.

The findings revealed that ‘A brand that I am familiar with and trust’ has a mean of 4.34 and a standard deviation of 0.822, ‘Product information’ has a mean of 4.21 and a standard deviation of 0.848, ‘Discounts and deals’ have a mean of 4.17 and a standard deviation of 0.99, ‘A level of consumer interaction’ has a mean of 4.15 and a standard deviation of 0.821, ‘Price information’ has a mean of 4.14 and a standard deviation of 0.821, ‘humour’ has a mean of 4.03 and a standard deviation of 1.149, ‘Celebrities and famous people’ had a mean of 3.84 and a standard deviation of 1.037.

Table 4.11: Advertisement Elements’ Influence to Purchase

N Mean Std. Deviation A brand that I am familiar with and 224 4.34 .822 trust Product information 251 4.21 .848

Discounts and deals 125 4.17 .99

A level of consumer interaction 67 4.15 .821

Price information 231 4.14 .879

Humour 29 4.03 1.149

Celebrities and famous people 50 3.84 1.037

Valid N (list wise) 0

4.4.10. Correlation on Advertisement Elements and Purchase A correlation analysis was run between the advertisement elements and purchase decision. Correlation coefficient denoted by r ranges between -1 and +1 and quantifies the direction and strength of the linear association between 2 variables. Pearson’s r value for the relationship between ‘A brand that I am familiar with and trust’ and purchase decision is -0.30 which denotes a weak, negative relationship. Pearson’s r

value for the relationship between ‘Celebrities and famous people’ and purchase decision is 0.522 which denotes a strong, positive association.

The other elements have a weak, positive association as their Pearson’s r values are above zero but below 0.5: results show that Pearson’s r value for the relationship between ‘Product information’ and purchase decision is 0.185, ‘Price information’ and purchase decision is 0.052, ‘Discounts and deals’ and purchase decision is 0.411, ‘Humour’ and purchase decision is 0.203, ‘A level of consumer interaction’ is 0.179.

Table 4.12: Correlation between Advertisement Elements and Purchase Decisions

Variable Purchase Decision Pearson Correlation -.030 A brand that I am familiar with and trust Sig. (2-tailed) .655 Pearson Correlation .185** Product information Sig. (2-tailed) .003 Pearson Correlation .052 Price information Sig. (2-tailed) .429 Pearson Correlation .522** Celebrities and famous people Sig. (2-tailed) .000 Pearson Correlation .411** Discounts and deals Sig. (2-tailed) .000 Pearson Correlation .244 Humour Sig. (2-tailed) .203 Pearson Correlation .179 A level of consumer interaction Sig. (2-tailed) .148

4.4.11. Poor Advertisements’ Influence on Purchase The researcher sought to investigate if customers will purchase a product if they do not like the advertisement. Findings revealed that 251 respondents said they will not buy the brand if there is something they disliked this represents 64% of the sample and 134 of the respondents said they will still buy the brand even if there is something they disliked this represents 34% of the respondents as shown in Table 4.10 below

Table 4.13: Poor Advertisements’ Influence on Purchase

Variable Distribution Frequency Percent Yes, I will not buy the brand if there is something I disliked 251 64 No, I will still buy the brand even if there is something I 134 34 disliked Missing 10 3 Total 395 100

4.4.12. Time of Interaction with Advertisements This study sought to investigate what time customers interact with advertisements. The respondents answered based on a set of pre-coded options which were: Early morning (6am- 10am), Mid- morning (10am-12pm), Afternoon (12pm-5p), Evening (5pm-7pm), Night (7pm-10pm), Late at night (10pm-6am).

The findings revealed the times in which the advertisement channels are interacted with the most; 57% respondents stated that they interact with TV Advertisements at night from 7pm-10pm, 32% said that they interact with radio advertisements early morning from 6am- 10am, 24% of the respondents said they interact with newspaper advertisement mid-morning from 10am-12pm, 18% said they interact with magazine advertisements in the evening from 5pm-7pm, 23% said they interact with billboards in the evening from 5pm-7pm, 25% said they interact with internet at night 7pm-10pm and 28% late at night from 10pm-6am as shown in Table 4.11.

Table 4.14: Time of Interaction with Advertisements

5pm)

12pm)

10am)

7pm) 10pm)

morning

6am)

- -

fternoon ight

arly morning arly vening

E (6am M (10am A (12pm E (5pm N (7pm Missing Variables night ate TV L (10pm 6 8 5 12 57 7 3 Advertisements Radio 32 19 11 10 12 5 3 Advertisements Newspaper 21 24 14 10 6 3 3 Advertisements Magazine 9 10 12 18 11 6 3 Advertisements Billboards 15 11 11 23 8 4 3 Advertisements Internet 5 8 13 11 25 28 3 Advertisements

4.5. Sales Promotions’ Effects on Purchase Intention This section of the study covers the second objective which is to establish the effects of sales promotions on consumer purchase decisions on FMCG products. The findings in this section addresses aspects such as: frequency of encountering sales persons, awareness of sales promotions, usage of sales promotions, source of knowledge, influence on purchase behaviour, future usage, preferred price discounts, and impulse buying.

This section also analysed the significance of some of the aspects in this section using descriptive statistics such as: mean, standard deviation, correlation, and regression. In order to measure the central tendency, statistical mean is used whereas standard deviation depicts how the result is dispersed from the mean.

4.5.1. Shopping Frequency The researcher sought to investigate how frequently customers go household shopping in supermarkets. Findings revealed that 26% of respondents shop once a month, 23%

shop 2-3 times a month, 22% once a week, 16% more than once a week, 7% daily, 4% less than once a month and 2% never responded as shown in Figure 4.9 below

Figure 4.9: Shopping Frequency

4.5.2. Frequency of Sales Promotions The researcher sought to investigate how often customers encounter sales promotions when they go shopping in supermarkets. Findings revealed that 31% of respondents encounter sales promotion once a month, 26% encounter 2-3 times a month, 16% once a week, 11% less than once a month, 10% more than once a month, 4% daily, and 2% never responded as shown in Figure 4.10 below

Figure 4.10: Frequency of Sales Promotions

4.5.3. Awareness of Sales Promotion Types The researcher sought to investigate types of sales promotion customers are aware of. Findings revealed that 40% of respondents are aware competitions (e.g. prize draws), 24% free gifts, 16% price discounts, 9% extra amount, 4% free samples, 3% buy1 get 1 free, 1% loyalty points and 3% never responded Figure 4.11 below

Figure 4.11: Awareness of Sales Promotion Types

4.5.4. Usage of Sales Promotions The researcher sought to investigate types of sales promotion customers have used. Findings revealed that 53% have used price discount, 20% buy1 get 1 free, 14% free gifts, 4% loyalty points, 3% free samples, 2% flash sales and 1% extra amount.

Figure 4.12: Usage of Sales Promotions

4.5.5. Sales Promotions Source of Awareness The researcher sought to investigate how customers learnt about the sales promotion being offered. Findings revealed that 36% from sales representatives, 28% never answered, 24% from TV, 5% from social media, 4% radio and 1% from word of mouth billboards and newspapers as shown in Figure 4.13

Figure 4.13: Sales Promotions Source of Awareness

4.5.6. Sales Promotions Influence on Purchase Behavior Table 4.12 shows the extent to which monetary sales promotions influence purchase behavior. Respondents answered on a scale of 1 – 5 where 1 meant ‘Strongly Disagree’ and 5 meant ‘Strongly Agree’. This means that when the mean scores closer to 5 the higher the level of agreement.

The findings revealed that ‘The promotion makes me temporarily change brands’ has a mean of 3.74 and a standard deviation of 0.93, ‘The promotion helps me make a faster purchase decision’ has a mean of 3.61 and a standard deviation of 1.07, ‘The promotion makes me permanently change brands’ has a mean of 3.61 and a standard deviation of 0.973, ‘The promotion gives me new ideas of things to buy’ has a mean of 3.43 and a standard deviation of 1.083, ‘The promotion makes me buy more because I feel I have saved money’ has a mean of 2.54 and a standard deviation of 1.305.

Table 4.13: Sales Promotion Influence on Purchase Behavior

Variable N Mean Std. Deviation The promotion makes me temporarily 385 3.74 0.93 change brands The promotion helps me make a faster 385 3.61 1.07 purchase decision The promotion makes me permanently 385 3.61 0.973 change brands The promotion gives me new ideas of 385 3.43 1.083 things to buy The promotion makes me buy more 385 2.54 1.305 because I feel I have saved money

4.5.7. Regression on Sales Promotion and Purchase

A model summary is utilized when predicting the value of a variable based on the value of another variable. In this case, dependent variable (purchase) is the variable being predicted. The independent variable (sales promotion) is the variable being used to predict the value of other variables. The model summary is demonstrated in Table 4.10 below. It provides information about the regression line’s ability to offer a description for the total divergence in the dependent variable.

From the table, the value of R is 0.352, R2 is 0.124, which means that 12.4 percent of the total variation in purchase decision has been explained by variability in sales promotion. The value of F critical, F (41,295) =10.694 and the P value is (0.000) therefore significant. These results therefore show that there was a statistically significant difference in the mean between the various variables of what constitute sales promotions.

Table 4.14: Regression of Sales Promotion and Purchase Decision

Model Summary Mode R R Adjust Std. Change Statistics l Square ed R Error of R F df1 df2 Sig. F Square the Square Chang Change Estimate Change e 1 .352a .124 .112 .88332 .124 10.694 5 379 .000 a. Predictors: (Constant), the promotion makes me temporarily change brands, the promotion helps me make a faster purchase decision, the promotion makes me permanently change brands, the promotion gives me new ideas of things to buy, the promotion makes me buy more because I feel I have saved money

ANOVAa Model Sum of df Mean Square F Sig. Squares Regression 41.720 5 8.344 10.694 .000b 1 Residual 295.715 379 .780 Total 337.436 384 a. Dependent Variable: Purchase Decision b. Predictors: (Constant), the promotion makes me temporarily change brands, the promotion helps me make a faster purchase decision, the promotion makes me permanently change brands, the promotion gives me new ideas of things to buy, the promotion makes me buy more because I feel I have saved money

4.5.8. Correlation of Sales Promotion and Purchase A correlation analysis was run between the sales promotion and purchase decision. Correlation coefficient denoted by r ranges between -1 and +1 and quantifies the direction and strength of the linear association between 2 variables. Pearson’s r value for all relationships are above zero but below 0.5 denoting a weak, positive associations. Results show that Pearson’s r value for relationships between ‘Price

discounts’ and purchase decision is 0.244, ‘Flash sales’ and purchase decision is 0.103, ‘Free gifts’ and purchase decision is 0.135, ‘Buy 1 get 1 free’ and purchase decision is 0.115, ‘Extra amounts’ and purchase decision is 0.302, ‘Loyalty points’ and purchase decision is 0.233, ‘Competitions’ and purchase decision is 0.224, ‘Joint promotions’ and purchase decision is 0.066, ‘Free samples’ and purchase decision is 0.155.

Table 4.17: Correlation of Sales Promotion and Purchase

Variable Purchase Decision Pearson Correlation .244** Price discounts Sig. (2-tailed) .000 Pearson Correlation .103 Flash sales e.g. Black Tuesday) Sig. (2-tailed) .381 Pearson Correlation .135 Free gifts Sig. (2-tailed) .054 Pearson Correlation .115* Buy 1 get 1 free Sig. (2-tailed) .048 Pearson Correlation .302* Extra amount Sig. (2-tailed) .019 Pearson Correlation .233* Loyalty points Sig. (2-tailed) .019 Pearson Correlation .224 Competitions / prize draws Sig. (2-tailed) .087 Joint promotions (2 brands being Pearson Correlation .066 sold together) Sig. (2-tailed) .623 Pearson Correlation .155 Free samples Sig. (2-tailed) .098

4.5.9. Future Use of Sales Promotion Table 4.13 shows the extent to which the customers intend to use sales promotions in the next 3 months. Respondents answered based on a scale of 1 – 5 where 1 means ‘Very Unlikely’ and 5 means ‘Very Likely’

Results show that ‘loyalty points’ have a mean of 4.12 and a standard deviation of 1.18, ‘Buy 1 get 1 free’ has a mean of 3.93 and a standard deviation of 1.123, ‘Free samples’ has a mean of 3.92 and a standard deviation of 1.312, ‘Joint promotions (2 brands sold together)’ has a mean of 3.7 and a standard deviation of 1.281, ‘Price discounts’ has a mean of 3.65 and a standard deviation of 1.212, ‘Free gifts’ has a mean of 3.65 and a standard deviation of 1.19, ‘Extra amount’ has a mean of 3.5 and a standard deviation of 1.172, ‘Flash sales’ has a mean of 3.19 and a standard deviation of 1.3, ‘Competitions’ have a mean of 3.17 and a standard deviation of 1.51

Table 4.15: Future Use of Sales Promotion N Mean Std. Deviation Loyalty points 102 4.12 1.18 Buy 1 get 1 free 297 3.93 1.123 Free samples 115 3.92 1.312 Joint promotions (2 brands sold together) 57 3.70 1.281 Price discounts 269 3.65 1.212 Free gifts 205 3.65 1.19 Extra amount 60 3.5 1.172 Flash sales e.g. Black Tuesday 74 3.19 1.3 Competitions / prize draws 59 3.17 1.51 Valid N (list wise) 0

4.5.10. Most Attractive Discount Figure 4.14 portrays the most preferred discount. The findings revealed that respondents most prefer buy 2 get 1 free 46%, buy 2 at 50pc off 30%, buy sh. 100 get 50pc off 21%, and 3 never responded as shown in Figure 4.14 below

Figure 4.14: Most Attractive Discount

4.5.11. Impulse Purchase Items The researcher sought to investigate types of products customers buy on impulse. Findings revealed that on impulse, 23% buy personal care items, 16% household items, 11% luxury items, 6% clothes, 2% other; stationery, toys, utensils and 42% never responded as shown in Figure 4.15 below

Figure 4.15: Impulse Purchase

4.5.12. Impulse Purchase Share The researcher sought to investigate share of impulse purchase items which was 34% household items, 26% clothes, 15% personal care items, 10% stationery, toys, utensils 9% luxury items, and 6% never responded as shown in Figure 4.16 below

Figure 4.16: Types of Products Customer Buy the Most

4.6. Personal Selling’s Effects on Purchase Intention This section of the study covers the third objective which is to establish the effects of personal selling on consumer purchase decisions on FMCG products. The findings in this section addresses aspects such as: interaction with sales persons, frequency of interaction, product isles with sales persons, satisfaction with information provided, influence on purchase, action taken, personal selling as a tool for building trust and brand image.

This section has analysed the significance of some of the aspects in this section using descriptive statistics such as: mean, standard deviation, correlation, and regression. In order to measure the central tendency, statistical mean is used whereas standard deviation depicts how the result is dispersed from the mean.

4.6.1. Interaction Incidence with Sales person The researcher sought to investigate if customers have ever interacted with sales persons in the supermarket. Findings revealed 88% of respondents said they have interacted with sales persons in the supermarket, 9% said they have never interacted with sales persons in the supermarket and 3% did not respond as shown in Figure 4.17 below

Figure 4.17: Interaction Incidence with Sales person

4.6.2. Frequency of Sales Person Encounter The researcher sought to investigate number of times customers have encountered sales persons in the supermarket. Findings revealed that they encounter sales men / women: every time 13%, often 25%, sometimes 35%, rarely 16%, almost never 8 and 3% never responded as shown in Figure 4.18 below

Figure 4.18: Frequency of Sales Person Encounter 64

4.6.3. Product Isles with Sales Persons The researcher sought to investigate types of product isle customers will find sales persons. Findings revealed that isles with salespersons include: household items 41%, personal care items 27%, clothes 18%, luxuries 6%, stationaries, toys, utensils 4%, and those who never responded were 4% as shown in Figure 4.19 below

Figure 4.19: Product Isles with Sales Persons

4.6.4. Satisfaction with Product Information Provided Table 4.14 shows how satisfied respondents were with the information the sales man / woman provided during their interaction. Responses were based on a scale of 1 – 5 where 1 means ‘Very Dissatisfied’ and 5 means ‘Very Satisfied’. This means that when the mean scores closer to 5 it shows a higher level of satisfaction.

The results show that ‘Information on what the promotion is about’ had a mean of 3.85 and a standard deviation of 0.946, ‘Information on the benefits of the promotion’ had a mean of 3.78 and a standard deviation of 0.981, ‘Information on how long the promotion lasts’ had a mean of 3.75 and a standard deviation of 1.209.

Table 4.16: Satisfaction with Product Information Provided

Variable N Mean Std. Deviation Information on what the promotion is about 385 3.85 .946 Information on the benefits of the promotion e.g. 385 3.78 .981 how much money you will save Information on how long the promotion lasts 385 3.75 1.209 Valid N (list wise) 385

4.6.5. Sales Information Influence on Purchase The researcher sought to investigate action taken by customers as a result of interacting with this sales person. Findings revealed that 197 respondents purchased the product this represents 50% of the sample, 166 respondents did not purchase the product but it increased their consideration to buy it in future this represents 41% of the sample, and 22 respondents did not purchase the product and will not buy it in future this represents 6% of the sample as shown in Table 4.15 below

Table 4.17: Sales Information Influence on Purchase

Variable Distribution Frequency Percent I purchased the product 197 50 I did not purchase the product but it increased my 166 41 consideration to buy it in future I did not purchase the product and will not buy it in 22 6 future Missing 10 3 Total 395 100

4.6.6. Sales Person Influence on Purchase Table 4.16 shows the extent to which personal selling influences purchase. Answers are based on a scale of 1–5 where 1 means ‘No influenced at all’ and 5 means ‘Large Influence’. This means that when the mean scores closer to 5 the higher the influence of personal selling. 66

The results show that ‘sales women in the supermarket’ had a mean of 3.8 and a standard deviation of 1.123, ‘sales men in the supermarket’ had a mean of 3.29 and a standard deviation of 1.190, ‘shop attendant’ had a mean of 3.11 and a standard deviation of 1.316.

Table 4.21: Sales Person Influence on Purchase

Descriptive Statistics N Mean Std. Deviation Sales women in the supermarket 385 3.80 1.123 Sales men in the supermarket 385 3.29 1.190 Shop attendant 385 3.11 1.316 Valid N (list wise) 385

4.6.7. Correlation on Sales Persons and Purchase Decision A correlation analysis was run between the sales persons and purchase decision. Correlation coefficient denoted by r ranges between -1 and +1 and quantifies the direction and strength of the linear association between 2 variables. Pearson’s r value for all relationships are above zero but below 0.5 denoting weak, positive associations. Pearson’s r relationship between ‘sales women in the supermarket’ and purchase decision is 0.289, ‘sales men in the supermarket’ and purchase decision is 0.282, ‘shop attendant’ and purchase decision is 0.259.

Table 4.18: Correlation of Sales Person Influence on Purchase Decision

Purchase Decision Pearson Correlation .289** Sales women in the supermarket Sig. (2-tailed) .000 Pearson Correlation .282** Sales men in the supermarket Sig. (2-tailed) .000 Pearson Correlation .259** Shop attendant Sig. (2-tailed) .000 N 385

4.6.8. Regression on Sales Person and Purchase Decision

A model summary is utilized when predicting the value of a variable based on the value of another variable. In this case, dependent variable (purchase) is the variable being predicted. The independent variable (sales person) is the variable being used to predict the value of other variables. The model summary is demonstrated in Table 4.20 below. It provides information about the regression line’s ability to offer a description for the total divergence in the dependent variable.

From the table, the value of R is 0.357, R2 is 0.127, which means that 12.7 percent of the total variation in purchase decision has been explained by variability in sales persons. The value of F critical, F (42,294) =18.534 and the P value is (0.000) therefore significant. These results therefore show that there was a statistically significant difference in the mean between the various variables of what constitute sales persons.

Table 4.19: Regression of Sales Person Influence on Purchase Decision

Model Summary Mode R R Adjust Std. Change Statistics l Square ed R Error of R F df1 df2 Sig. F Square the Square Chang Change Estimate Change e 1 .357a .127 .120 .87913 .127 18.534 3 381 .000 a. Predictors: (Constant), sales women in the supermarket, sales men in the supermarket, shop attendant ANOVAa Model Sum of df Mean Square F Sig. Squares Regression 42.974 3 14.325 18.534 .000b 1 Residual 294.462 381 .773 Total 337.436 384 a. Dependent Variable: Purchase Decision b. Predictors: (Constant), sales women in the supermarket, sales men in the supermarket, shop attendant

4.6.9. Products Inclined to Personal Selling The researcher sought to investigate types of products that sales persons will convince customers to purchase. Findings revealed 47% agreed that sales persons would 68

convince them to purchase food items, 21% house hold items, 17% personal care items, 8% clothes, 4% luxury items, 1% other; stationery, toys, utensils and 2% never responded as shown in Figure 4.20 below

Figure 4.20: Products Inclined to Personal Selling

4.6.10. Other Personal Selling Platforms The researcher sought to investigate if customers have ever interacted with sales person elsewhere apart from supermarket. Findings revealed 69% said they have never interacted with sales person via phone, 28% said yes, 51% they have never interacted with sales person via social media, 47% said yes, 77% have never interacted with sales person via email and links, 21% said they have, 86% have never interacted with sales person via web chat, 12 % said they have, 49% said they have never interacted with sales person in other retail outlets as shown in Table 4.17 below

Table 4.24: Other Personal Selling Platforms

Variable Distribution (%) Yes No Missing Telephone 28 69 3 Social media 47 51 2 Emails and links 21 77 2 Web chats 12 86 2 Other retail outlets 49 49 2 69

4.6.11. Personal Selling Perceptions Table 4.18 below shows respondents agreement level to sales persons increasing their trust and brand image. Responses were based on a scale of 1 – 5 where 1 meant ‘Disagree Completely’ and 5 meant ‘Agree Completely’.

The results show that ‘sales persons increase my trust in a brand’ had a mean of 3.76 and a standard deviation of 1.034, ‘sales persons improve image of the brand’ had a mean of 3.89 and a standard deviation of 1.023.

Table 4:25 Personal Selling Perceptions

Variable N Mean Standard Deviation

Sales persons increase my trust in a brand 385 3.76 1.034 Sales persons improve image of the brand 385 3.89 1.023

4.6.12. Correlation on Personal Selling Perceptions and Purchase Decision A correlation analysis was run between the personal selling attributes and purchase decision. Correlation coefficient denoted by r ranges between -1 and +1 and quantifies the direction and strength of the linear association between 2 variables. Pearson’s r value for all relationships are above zero but below 0.5 denoting weak, positive associations. The results show that the Pearson r relationship between ‘sales persons increase my trust in a brand’ and purchase decision is 0.390, ‘Sales persons improve the image of the brand’ and purchase decision is 0.364.

Table 4.20: Correlation on Personal Selling Perceptions and Purchase Decision

Variable Purchase Decision Pearson Correlation .390** Sales persons increase my trust in a brand Sig. (2-tailed) .000 Pearson Correlation .364** Sales persons improve the image of the brand Sig. (2-tailed) .000 N 385

4.6.13. Regression on Personal Selling Perceptions and Purchase Decision

From the table, the value of R is 0.412, R2 is 0.170, which means that 17 percent of the total variation in purchase decision has been explained by variability in personal selling perception (image and trust). The value of F critical, F (57,280) =39.077 and the P value is (0.000) therefore significant. These results therefore show that there was a statistically significant difference in the mean between the various variables of what constitute personal selling perceptions.

Table 4.21: Regression on Personal Selling Perceptions and Purchase Decision

Model Summary Model R R Adjusted Std. Change Statistics Square R Error of R F df1 df2 Sig. F Square the Square Change Change Estimate Change .412a .170 .165 .85634 .170 39.077 2 382 .000 1 a. Predictors: (Constant), sales persons increase my trust in a brand, sales persons improve the image of the brand ANOVAa Model Sum of Squares df Mean Square F Sig. Regression 57.311 2 28.655 39.077 .000b 1 Residual 280.125 382 .733 Total 337.436 384 a. Dependent Variable: Purchase Decision b. Predictors: (Constant), sales persons increase my trust in a brand, sales persons improve the image of the brand

4.7. Social Media’s Effects on Purchase Intention This section of the study covers the fourth objective which is to establish the effects of personal selling on consumer purchase decisions on FMCG products. The findings in this section addresses aspects such as: social networks used, satisfaction with product information provided, types of product information used, action taken, influence of social media on purchase, types of products purchased from social media.

This section has also analysed the significance of some of the aspects in this section using descriptive statistics such as: mean, standard deviation, correlation, and 71

regression. In order to measure the central tendency, statistical mean is used whereas standard deviation depicts how the result is dispersed from the mean.

4.7.1. Frequent Social Media The researcher sought to investigate social media networks customers us at least weekly. Findings revealed that 39% use Facebook, 22% use WhatsApp, 14% use Instagram, 2% use twitter, 2% use Jumia, OLX, 1% other, and 20% never responded as shown in Figure 4.21 below

Figure 4.21: Frequent Social Media

4.7.2. Social Media Share The researcher sought to investigate social media share based on what respondents use the most. Findings revealed that 43% use Facebook the most, 35% WhatsApp, 6% Instagram, 4% Twitter, 3% Jumia, OLX, 8% use other social media, and 1% never responded as shown in Figure 4. 22 below

Figure 4.22: Social Media Share

4.7.3. Social Media with Product Information The researcher sought to investigate types of social media customers get product and brand information from. Findings revealed that 55% get information from Facebook, 13% WhatsApp, 13% Twitter, 11% Jumia, OLX, 1% other, and 7% never responded as shown in Figure 4.23 below

Figure 4.23: Social Media with Product Information

4.7.4. Satisfaction with Product Information on Social Media Table 4.19 shows the satisfaction levels with the product information on social media platforms. Responses were based on a scale of 1 – 10 where 1 meant ‘Very Dissatisfied’ and 10 meant ‘Very satisfied’.

The results show that Facebook had a mean of 6.77 and a standard deviation of 2.537, Twitter had a mean of 6.07 and a standard deviation of 2.664, Instagram had a mean of 5.63 and a standard deviation of 2.717, WhatsApp had a mean of 5.45 and a standard deviation of 2.781, Pinterest had a mean of 5.39 and a standard deviation of- 2.792.

Table 4.28: Satisfaction with Product Information on Social Media

Variable N Mean Std. Deviation Facebook 346 6.77 2.537 Twitter 161 6.07 2.664 Instagram 158 5.63 2.717 WhatsApp 276 5.45 2.781 Pinterest 23 5.39 2.792 Snapchat 37 4.73 2.317 LinkedIn 42 4.67 1.79

4.7.5. Correlation on Social Media Information and Purchase Decision

A correlation analysis was run between the satisfaction with social media platforms’ information and purchase decision. Correlation coefficient denoted by r ranges between -1 and +1 and quantifies the direction and strength of the linear association between 2 variables. Pearson’s r relationship between satisfaction with Pinterest information and purchase decision is 0.589, WhatsApp information and purchase decision is 0.494 denoting a strong, positive correlation for Pinterest and WhatsApp. LinkedIn information and purchase decision is -0.132 denoting a weak, negative association.

Pearson’s r values for the other platforms are below 0.5 denoting weak, positive associations: the r relationship between Facebook information and purchase decision is 0.320, Twitter information and purchase decision is 0.262, Instagram information and purchase decision is 0.396, Snapchat information and purchase decision is 0.217.

Table 4.22: Correlation Social Media Information and Purchase Decision

Variable Purchase Decision Pearson Correlation .320** Facebook Sig. (2-tailed) .000 Pearson Correlation .262** Twitter Sig. (2-tailed) .001 Pearson Correlation -.132 LinkedIn Sig. (2-tailed) .406 Pearson Correlation .396** Instagram Sig. (2-tailed) .000 Pearson Correlation .494** WhatsApp Sig. (2-tailed) .000 Pearson Correlation .217 Snapchat Sig. (2-tailed) .197 Pearson Correlation .589** Pinterest Sig. (2-tailed) .003

4.7.6. Types of Product Information from Social Media The researcher sought to investigate product information customers get from social media. Findings revealed that 39% get information on promotions, 30% never responded, 25% get information on functions of the products and 6% from events being hosted by the company as shown in Figure 4.24 below

Figure 4.24: Types of Product Information from Social Media

4.7.7. Purchase Influence from Social Media Types of Information Table 4.30 portrays the importance of different information types in making purchase decisions. Responses are based on a scale of 1 – 5where 1 means ‘Very Unimportant’ and 5 means ‘Very important’.

The results show that information on ‘reviews e.g. people’s experiences, comments’ had a mean of 4.13 and a standard deviation of 1.01, ‘functions of the product’ had a mean of 4.34 and a standard deviation of 1.856, ‘promotions’ had a mean of 3.99 and a standard deviation of 1.012, ‘events being hosted by the company’ had a mean of 3.39 and a standard deviation of 1.174.

Table 4.30: Purchase Influence from Social Media Types of Information

Variable N Mean Std. Deviation Reviews e.g. people’s experiences, comments 297 4.13 1.01 Functions of the product 210 4.34 0.856 Promotions 203 3.99 1.012 Events being hosted by the company 103 3.39 1.174 Valid N (list wise) 3

4.7.8. Correlation on Social Media Types of Information and Purchase

A correlation analysis was run between the satisfaction with social media platforms’ information and purchase decision. Correlation coefficient denoted by r ranges between -1 and +1 and quantifies the direction and strength of the linear association between 2 variables. Pearson’s r values for all variables are below 0.5 denoting weak, positive associations. The r relationship between information on ‘reviews’ and purchase decision is 0.258, ‘promotion’ and purchase decision is 0.326, ‘Functions of the product’ and purchase decision is 0.259, ‘events being hosted by the company’ and purchase decision is 0.295.

Table 4.23: Correlation on Social Media Types of Information and Purchase

Variable Purchase Decision Reviews e.g. people’s experiences, Pearson Correlation .258** opinions, comments Sig. (2-tailed) .000 Pearson Correlation .326** Promotions Sig. (2-tailed) .000 Pearson Correlation .259** Functions of the product Sig. (2-tailed) .000 Events being hosted by the Pearson Correlation .295** company Sig. (2-tailed) .002

4.7.9. Actions Resulting from Social Media Information The researcher sought to investigate action customers have taken a result of interacting with social media product information. Findings revealed that 196 respondents have purchased the product this represents 50% of the respondents, 168 respondents did not purchase the product but it increased their consideration to buy it in future this represents 43% of the sample, and 21 respondents did not purchase the product and will not buy it in future this represents 5% of the sample as shown in Table 4.21 below.

Table 4.32: Actions resulting from Social Media Information

Variable Distribution Frequency Percent I purchased the product 196 50 I did not purchase the product but it increased my 168 43 consideration to buy it in future I did not purchase the product and will not buy it in future 21 5 Missing 10 3 Total 395 100

4.7.10. Purchase Influence from Social Media Platforms Table 4.20 shows how the different social media platforms influence purchase. Responses are based on a scale of 1 – 5 where 1 means 1 ‘No influence at all’ and ‘5’ means ‘Large influence’.

The results show that Facebook had a mean of 3.62 and a Standard deviation of 1.336, Twitter had a mean of 3.40 and a Standard deviation of 1.261, Pinterest had a mean of 3.35 and a Standard deviation of 1.229, Instagram had a mean of 3.2 and a Standard deviation of 1.305, WhatsApp had a mean of 3.02 and a Standard deviation of 1.393, LinkedIn had a mean of 2.64 and a Standard deviation of 0.958, Snapchat had a mean of 2.59 and a Standard deviation of 1.279, Online selling platforms did not have any responses.

Table 4.33: Purchase Influence from Social Media Platforms

Variable N Mean Standard Deviation Facebook 346 3.62 1.336 Twitter 161 3.40 1.261 Pinterest 23 3.35 1.229 Instagram 158 3.20 1.305 WhatsApp 276 3.02 1.393 LinkedIn 42 2.64 0.958 Snapchat 37 2.59 1.279 Online selling platforms e.g. 0 Jumia, OLX)

4.7.11. Products purchased from Social Media The researcher sought to investigate types of products customers have purchased via social media. Findings revealed that 24% of the respondents have never bought anything from social media, 21% have bought clothes and shoes, 17% personal care items, 15% food items, 13% household items, 3% luxury, perfume and cosmetics and 3% never answered as shown in Figure 4.25

Figure 4.25: Products Purchased from Social Media

4.7.12. Perceptions Towards Social Media Purchase Table 4.21 below shows the different perceptions customers have towards social media purchase. Responses are based on a scale of 1 – 5 where 1 means ‘Disagree Completely’ and 5 means ‘Agree Completely’.

Results show that ‘Social media marketing is more creative and attractive compared to others’ has a mean of 3.79 and a standard deviation of 1.174, ‘Products I buy on social media are delivered to me’ has a mean of 3.7 and a standard deviation of 1.213, ‘I physically buy the products I see on social media’ has a mean of 3.33 and a standard deviation of 1.249, ‘Products I buy on social media are better priced’ has a mean of 3.29 and a standard deviation of 1.344

Table 4:34: Perceptions Towards Social Media Purchase

Variable N Mean Std. Deviation Social media marketing is more creative and 385 3.79 1.174 attractive compared to others Products I buy on social media are delivered to me 385 3.70 1.213 I physically buy the products I see on social media 385 3.33 1.249 Products I buy on social media are better priced 385 3.29 1.344 Valid N (list wise) 385 79

4.8. Consumer Buying Behavior The study was set to establish consumer buying behavior. Respondents were asked a set of questions and were asked to respond as indicated.

4.8.1. Most Impactful Marketing Strategy The researcher sought to investigate form of marketing used and their impact on customer. Findings revealed that 37% of the respondents said sales promotion has an impact on them, 28% personal selling, 25% advertising, 9% social media and 3% never responded as shown in Figure 4.30 below

Figure 4.26: Most Impactful Marketing Strategy

4.8.2. Correlation on the 4 Marketing Strategies and Purchase A correlation was done between advertising, sales promotions, personal selling and social media on consumer purchase decisions. The findings revealed that there was a positive correlation between purchase decision and advertising (Rho=0.438, p<0.05); Sales promotion (Rho=0.326, p<0.05); Personal selling (Rho=0.435, p<0.05); and Social media (Rho=0.499, p<0.05); This implied that with every increase in advertising, sales promotions, personal selling and social media, there was appositive influence on purchase decision as indicated in Table 4.22

Table 4.35: Correlation on the Marketing Strategies and Purchase

Purchase Sales Personal Social Variable Decision Advertising Promotion Selling Media 1 Purchase Decision

.438** 1 Advertising .000 .326** .372** 1 Sales Promotion .000 .000 .435** .338** .418** 1 Personal Selling .000 .000 .000 .499** .539** .414** .394** 1 Social Media .000 .000 .000 .000 385 385 385 385 385 **. Correlation Is Significant at the 0.01 Level (2-Tailed).

4.8.3. Shopping Frequency The researcher sought to investigate action customers have taken a result of interacting with information. Findings revealed that 51% of respondents go shopping monthly, 41% weekly, 5% daily and 3% was missing as shown in Figure 4.26 below

Figure 4.27: Shopping Frequency

4.8.4. Shopping Outlets The researcher sought to investigate where customers mainly shop. Findings revealed that 49% of respondents usually shop in large supermarkets, 24% malls, 21% of

respondents shop in small supermarkets, 4% in other supermarkets and 2% never responded as shown in Figure 4.27 below

Figure 4.28: Shopping Outlets

4.8.5. Shopping Allies The researcher sought to investigate who accompanies the customer shopping. Findings revealed that 35% of the respondents usually go alone, 30% usually go with their family, 28% are accompanied with friends/peer, 5% colleagues and 2% never answered as shown in Figure 4.28 below

Figure 4.29: Shopping Allies

4.8.6. Selection Criteria for Purchase The researcher sought to investigate what customers considers when purchasing a product. Findings revealed that 54% of the respondents consider quality, 35%consider price, 26% communication used, 3% never responded and 2% consider brand as shown in Figure 4.29 below

Figure 4.30: Shopping Criteria for Purchase

4.9. Chapter Summary This chapter focused on results and findings achieved from this study. The first section looked at the background information of the sample. The four sections that followed analyzed the findings from the research objectives that established the: effects of advertising on consumer purchase decisions, effects of personal selling on consumer purchase decisions, effects of sales promotions on consumer purchase decisions, and effects of social media on consumer purchase decisions. The last section looks at the general consumer buyer behavior. The next chapter provides the discussion on these results, the conclusions from the findings, and the key recommendations for each objective.

CHAPTER FIVE

5.0. DISCUSSIONS, CONCLUSIONS AND RECOMMENDATIONS

5.1. Introduction This chapter provides the discussion on the findings from the previous chapter and avails the conclusions and recommendations. It begins with a summary of the study which focuses on the problem statement, objectives, findings, and general implications of the study. The discussion follows and concomitantly, the next part articulates the conclusions, the last part gives recommendations for each objective and for further studies.

5.2. Summary

The objective of this study was to determine the effect of marketing communication on consumer purchase behavior in the FMCG sector in Kenya. This study was guided by four research objectives that established: the effects of advertising on consumer purchase decisions, the effects of personal selling on consumer purchase decisions, the effects of social media on consumer purchase decisions, and the effects of sales promotions on consumer purchase decisions.

A descriptive research was used to obtain information from customers on how marketing communication affects their purchase behavior. The target was purchase decision makers from middle income households in Nairobi making a household net income of Kshs. 40,000 and above. According to KNBS statistics this population amounted to 250,000 household decision makers. Using a sampling formula, a sample of 395 was valid and was covered. The probability sampling technique used was systematic sampling since the research was done using exit interviews; hence every 5th person was approached. The questionnaire was programmed onto a mobile based platform which the research assistants used to conduct and submit the face to face exit interviews – this helped researcher achieve a response rate of 100%. Data was then analyzed using both descriptive and inferential statistics using SPSS and the results were presented in Figures and Tables.

The first objective was set to determine the effects of advertising on consumer purchase decisions on FMCG products. The findings revealed that recommendation from friends 84

or family, and personal experiences are the key factors that determine trial purchase. TV and internet are the advertisement channels that consumers interact with the most. TV is most viewed at night (7pm to 10pm), and internet is most used at night to late night (7pm to 6am) The correlation analysis showed that newspaper and internet advertisement are most likely to influence purchase. The most important advertisement elements are ‘brand familiarity / trustworthiness’ and ‘product information’. The advertising elements that need the most improvements are ‘product information’ and ‘price information’. The advertising elements that drive purchase the most are ‘celebrities and famous people’ and ‘discounts and deals’. Majority of the respondents would not buy a brand if they disliked its advertisement.

The second objective was set to determine the effects of sales promotions on consumer purchase decisions on FMCG products. The findings revealed that customers mainly go shopping 2-4 times a month, and mainly encounter sales promotions at the same frequency. The sales promotions consumers are most aware of are competitions, free gifts and price discounts in that order. Majority participate in price discounts. Nonetheless, the most effective driver of purchase is extra amounts e.g. 20 percent more for the same price as it had the strongest positive correlation. Sales promotions are most known from sales representatives followed by TV. The main influence sales promotions have on purchase behavior is a temporary switch of brands. The sales promotion they anticipate to participate in is loyalty points. A test was done on monetary versus quantity discounts to find out the most preferred; ‘Buy 2 get 1 free’ was preferred by almost half, ‘Buy 2 at 50% off’ was preferred by a third implying quantity would be prioritized to price discounts. Impulse purchase share leads in household item followed by clothes.

The third objective was set to determine the effects of personal selling on consumer purchase decisions on FMCG products. The findings revealed that almost all consumers have interacted with a sales person in a supermarket. Sales person are mostly found on isles with household items and personal care items. Consumers are satisfied with the information the sales person gives them on the promotion, the benefits of the product and the duration of the promotion. As a result of interacting with sales persons, half bought the product while 2 out of 5 considered to buy it in future. Sales women have a higher influence on purchase. More effort is placed to convince purchase when it is a 85

food item. Social media is the most alternative platform for personal selling activities. There is agreement that sales person increase the trustworthiness and image of a brand.

The fourth objective was set to determine the effects of social media marketing on consumer purchase decisions on FMCG products. The findings revealed that Facebook and WhatsApp are the most frequently and widely used social media networks. Consumers are mostly satisfied with the adequate product information received from Facebook and Twitter platforms. However, the content on Pinterest, WhatsApp, Instagram is more effective in driving purchase. Product information on social media is mainly on promotions which also had the strongest, positive association with purchase. There is also high agreement that information on function of the product has a higher likelihood to drive purchase. Half of the respondents purchased as a result of social media information while a significant proportion increased their consideration for future purchase. The most purchased products on social media are clothes, personal care, food items and household items. Social media marketing is perceived to be more creative and attractive compared to others.

5.3. Discussion

5.3.1. Advertising’s Effects on Purchase Intention The findings established that TV advertisement and internet advertisement highly influence brand purchase and customers agreed that they will not buy the brand if there is something they disliked in the advertisement. According to Storme (2015) a positive attitude toward an advertisement predicts a positive attitude toward the brand and also increases the likelihood that the consumer will want to purchase products from the brand in the future. According to a study done by Saadeghvaziri (2013), the results show that attitudes towards online advertising is a statistically significant and positive predictor of web users’ purchase intention.

Massey et al., (2013) research findings show that if respondents like an advertisement, this will improve their attitude towards the advertiser, and this in turn will improve their attitude towards the brand. This is important because one's attitude towards the brand strongly influences purchase intent across all four cultural groups, for both the ethical and unethical advertisements.

On the other hand, in a study done by Fam Kim Shyan (2013) in five Asian cities (Hong Kong, Shanghai, Jakarta, Bangkok and Mumbai) more than two thirds of the respondents in each city claimed they would not purchase the advertised product/service if it consists disliked executions (Fam, et al., 2013). The dislike attributes in this case were: style, meaningless, character, exaggeration, irresponsive, violent and hard sell (Fam, et al., 2013).

The findings revealed that respondents were most likely to interact with advertisement on broadcast media, that being TV and Radio. There was a lower likelihood to interact with advertisements from newspapers, magazines and billboards. According to Woo (2015), results show that consumers’ preferences for media channels in each product category when socio demographics and lifestyle variables are held constant gives relatively larger estimates for broadcast TV and WOM (word of mouth), while those for newspapers and magazines are relatively smaller; this suggests that consumers’ product purchase decisions are affected most by broadcast TV and WOM, and least by newspapers and magazines

Findings also revealed that product information, brand trust and familiarity, price information, use of celebrity in advertisement, used of discount and deals in advertisement, humor in advertisement and advertising that capture their attention, all had a high influence on the purchase of the product. According to Izquierdo Yusta et al., (2015) research demonstrates that such consumers tend to accept the advertising positively if they have trust in the advertisers.

An advertising spokesperson can have a significant effect on the attraction and retention of viewers’ attention. Attention enables the development of brand awareness, which forms attitude, influencing purchase intentions (Chang & Chang, 2014). Pilelienė and Grigaliūnaitė, (2017) research suggests that a celebrity spokesperson in an advertisement elicits more positive attitudes than a non-celebrity spokesperson, however the level of purchase intentions does not differ for the brand advertised by a celebrity compared to the brand advertised by a non-celebrity spokesperson. The study found that celebrities’ likeability and their attractiveness have the greatest impact on both consumers Attitude and their purchase behavior (Mansour, 2016).

5.3.2. Sales Promotions’ Effects on Purchase Intention

The findings established that respondents prefer to buy products that have buy 2 get one free offer, buy 2 at 50% off, buy at sh.100 and get 50% off in that order. In line to this statement Huang and Yang (2015) present two cases where as in their first experiment, the results show that offering quantity discounts e.g. “4 for 30% off” can result in greater willingness to buy a single product at the full price than offering promotions with a low quantity discount e.g. “2 for 30% off”. In their second experiment, the results show that when the missed quantity discount is based on dollars rather than on the number of pieces “Buy $100, get 30% off” versus “Buy 4, get 30% off”, the effect of purchase quantity on willingness to buy is enhanced (Huang & Yang, 2015).

Finding revealed that respondents are aware of competition as a promotional strategy, free gifts, price discounts, free samples, loyalty points and buy1 get 1 free. This is in line with Schultz and Block (2014) sales promotions affect consumer purchase behavior. One of the questions they asked in their study in the U.S. was “What sales promotional tool most influenced your purchase behavior toward ______brand?” 55.8 per cent of the respondents reported it was “coupons in newspapers or inserts” that influenced or greatly influenced them. This was followed by “product samples delivered to the home”, the third one was “product samples in the store” at 48.2 per cent, and the fourth most impactful promotional activity, store loyalty cards, with 47.7 per cent of all respondents. The most surprising promotional tool found in this analysis was the rapid and continuing growth of consumers reporting

that retail store shopper cards had a major influence on their purchase behaviors; an approximately 70 per cent growth rate

Findings also established that customers have used price discounts, buy1 get 1 free, free gifts, free samples and loyalty points. Sales promotion consists of a diverse collection of mostly short term incentives designed to motivate consumers or the trade to purchase a product immediately and/or in larger quantities by lowering the price or adding value (Lamb et al., 1996). These include coupons, samples, premiums, contests, point of purchase displays, and frequent‐ buyer programs, etc.

In his journal, Huang et al (2014) finds that price promotion activities at Starbucks in Taiwan had a favorable effect on customer quality evaluations and positively influenced repeat purchase intentions. Prior to Huang’s paper, Tong et al (2012) examined ladies' buying behavior during shoes sales promotions in Malaysia, using the highly validated Belk's Model. Despite the multiracial society in the country, ethnic group interaction on the model did not indicate impact on consumer differences affecting the sales promotion purchase during a specific festive season sale; all ethnic groups take full advantages of the sale (Tong, 2012).

Findings also established that respondents buy other products e.g. stationery, toys, utensils, household items, luxury items, clothes and personal care items products in impulse. In support to this statement, numerous studies confirm the global prevalence of impulse buying behavior and that over half of consumers visiting a shopping mall will make impulse purchases (Hultén & Vanyushyn, 2014). Clothes have been reported to be among the most frequently impulse purchased items (Pornpitakpan &

Han, 2013). Consequently, it is the norm for retailers to design stores and display products in ways which encourage impulse buying i.e. through sales promotions (Hultén & Vanyushyn, 2014). Further to this, Tifferet and Herstein (2012) found that female shoppers make more impulse purchases than men. Which can be explained by females being more susceptible to sensory cues from touching an item than men.

5.3.3. Personal Selling’s Effects on Purchase Intention Findings revealed that almost all respondents have ever encountered a sales person when shopping in a supermarket. A significant proportion agreed that they encounter them every time they go shopping. According to Hsu et al., (2010) for retail services, consumers always look for tangible cues like store image and behavior of sales staff that may help them reduce perceived risk (Jayawardhena & Farell, 2011). The appearance of frontline salespersons represents the first impression of the company in the customer's mind. Their service behavior and relationship with the customer also add value to the product/service and provide psychological and social utilities (Lloyd & Luk, 2011). In this connection, frontline sales personnel serve as risk reliever. This risk reduction role will be more significant if the sales staff is perceived as trustworthy (Sirdeshmukh et al., 2002; Wong & Sohal, 2003)

In store, it is these salespersons’ skills that allow connections with the consumer through verbal and nonverbal communication that adapts to consumer’s needs, thereby increasing trust (Orth et al., 2013), with trust leading to retail store patronage. Retail salespeople often have the most interaction with a retail firm’s customers, yet compensation and training are minimal

Findings also revealed that respondents were very satisfied with information on how long the promotion lasts, what the promotion is about, and information on the benefits of the promotion e.g. how much money you will save. Findings from a study done in Nigeria revealed clearly that personal selling is the most persuasive in the marketing communication mix elements. It aims at consolidating customers and maintaining the buyer seller exchange relationship. Personal selling as a business strategy helps representatives of a company to explain to their clients/customers how well the products/services can satisfy their needs. Therefore, Organizations should focus more

on the customer by adopting personal selling as a marketing strategy (zoltanpolla.com, 2017).

Findings also revealed that salespersons have increased respondents trust and brand image. In line to this statement according to Liu et al., (2013) reported that the store trust was a stronger influencing factor for the in-store purchases than the online trust was for internet purchases. His overall findings sustained the notion that salespeople with higher levels of active empathetic listening (AEL) will have higher quality relationships, and be regarded as more trustworthy. Further, when levels of trustworthiness are high the level of relationship quality is higher which results in higher sales performance (Drollinger, 2012).

Grayson et al., (2008) a consumer's trust in the firm / company / brand could be affected by the perceived trust of the business context in which it operates. Grayson et al., (2008) refer interpersonal trust and organization‐ specific trust as narrow scope trust and the level of trust of an industry, or a country, as broad scope trust. Interpersonal trust is more influential in shaping exchange activities in a business context where broad‐ scope trust is low because it will perform a safeguarding function to reduce the perceived risk inherent in the purchase (Grayson et al., 2008).

Arnott (2007), trust has been conceptualized as existing when one party has confidence in an exchange partner's reliability and integrity. The importance of trust in retailing overall has not received much attention, although a few studies have reported a significant role of trust in the retail context in company‐ customer relationships (Too et al., 2001), in salesperson‐ customer relationships (Ball et al., 2004), and in online retailers (Nassir et al., 2008). Risk emerges when the consumer feels uncertain of the outcome associated with the purchase from a retail

outlet. Previous empirical studies have proved that this is a common phenomenon in the retail sector (e.g. Diallo, 2012) and the types of risks frequently experienced by consumers include financial, physical, time, and psychosocial (Mitchell & Harris, 2005).

However, it was revealed that respondents rarely interact with sales persons via phone, social media, other retail outlets, web, email and links. In contrast, according to Ahearne and Rapp, (2010), with advances in technology, personal selling also takes place over the telephone, through video conferencing and interactive computer links between buyer and seller. Despite this, it still remains a highly human intensive activity despite the use of technology and scholars argue that technology cannot replace the unique functions of the salesperson. Evolving Internet, social media and other technology enabled tools and the interaction patterns being created by such tools are transforming how salespeople interact with prospects and customers, and how organizations manage their sales force (Dixon & Tanner, 2012).

5.3.4. Social Media’s Effects on Purchase Intention Findings revealed respondents get product information from Facebook followed by WhatsApp, Instagram, Twitter and Jumia, or OLX. In support to this statement according to Woo (2015) over the past two decades, the advent of the internet has again fundamentally altered the quantity and quality of information available to consumers. As a type of “new media,” the internet contains all of the information that was available from older media and, when used in conjunction with personal media devices such as smartphones and tablets, allows consumers to obtain information anywhere, at any time.

Chen and Hsieh (2012), consumers are now able to gather information through various media channels including the internet at each stage of the purchase decision

making process (need recognition, information search, alternative evaluation, purchase decision, and post purchase behavior.

It was also established that reviews and information regarding promotion, product function and event hosted by the company in social media is very important because it has influenced their purchase decision. According to Priyanka (2013), it can be assumed that social communications in the form on online reviews, posts and word of mouth (WOM) will play a large part in driving purchase decisions.

It was also revealed that use of Facebook has largely influenced customers purchase; LinkedIn and Instagram have moderately influence purchase intention, and WhatsApp and Snap chat has slightly influenced customers purchase. Social media has become an imperative conduit for global marketing communications and is commanding a larger share of advertising budgets, especially to reach the younger generation. Therefore, the value of advertising on social media such as Facebook, YouTube, LinkedIn, Twitter and others is of great interest to organizations, managers and academics (Saxena & Khanna, 2013). marketers are increasing their social media budgets with digital interactive advertising forecasted to reach $138 billion in 2014, a growth rate of nearly 15 per cent in comparison to 2013 (eMarketer, 2014a).

Facebook is the most popular social medium in the world. Duffett (2015)’s results confirm that advertising on Facebook has a positive influence on the behavioral attitudes (intention to purchase and purchase) of Millennials who reside in SA. However, Reuters and Ipsos (2012) revealed that one in five Facebook users had purchased products as a result of advertisements and/or comments that they viewed on Facebook. This rate increased to nearly 30 per cent who were aged 18 34. Facebook and ComScore (2012) disclosed that 4 per cent of consumers bought something within a month after being exposed to earned brand impressions from a retailer also increased consumers’ intention to purchase. Rich Relevance revealed that consumers who made purchases, owing to Facebook advertising, were double in comparison to Pinterest and Twitter. Facebook also had the greatest income per session

It was also revealed that customers agreed that products they buy on social media are better priced and delivered to them. A promotion tactic that is commonly used by retailers is temporary price reductions. Somervuori and Ravaja (2013) measured 93

psychophysiological responses to different price levels and found that low prices induced positive emotions. Further to this, Luong and Slegh (2014) report that consumers perceive an attractive difference between certain percentage level discounts (e.g. 10 and 50 per cent) but not at other levels (e.g. 50 and 75 per cent). Price discounts are interesting because they are more complicated for consumers to interpret and because they signal meanings unique from price. By their very nature, price discounts are harder for shoppers to evaluate than price because they require use of a more demanding cognitive process (Biswas et al., 2013).

5.4. Conclusions

5.4.1. Advertising’s Effects on Purchase Intention The key conclusions derived from findings on the first objective ‘effects of advertising on purchase intention’, are explained. Trial purchase is a result of recommendation from friends or family, and personal experiences consumers have had. The main sources of advertisement information are TV and internet which were the most used advertisement channels. However, newspaper and internet advertisement are most effective in influencing purchase as they had the strongest and most positive correlation outcomes. 7pm onwards is the prime time for receiving advertisement information because TV is most viewed at night (7pm to 10pm), and internet is most used at night to late night (7pm to 6am).

Advertisements are more valued when they are from a familiar brand they trust, and has adequate product information as these were the most important advertisement elements to consumers. There is an opportunity to upgrade advertisements by increasing product information and price information since consumers felt these two elements needed the

most improvement. Notwithstanding, ‘celebrities or famous people’ and ‘discounts or deals’ drive purchase the most as they are the elements that had the strongest and most positive correlation. Disliked advertising retains or promotes non-purchase of a brand.

5.4.2. Sales Promotion’s Effects on Purchase Intention The key conclusion resulting from the findings on the second objective; effects of sales promotions on consumer purchase decisions, are described. Shopping mainly happens from bimonthly to monthly. Competitions, free gifts and price discounts in that order are the most known sales promotions however price discounts are the most widely used which could be a result of its higher frequency. Despite this, ‘extra amounts’ are more effective in influencing purchase, parallel to this, ‘Buy 2 get 1 free’ (quantity) was more preferred to ‘Buy 2 at 50% off’ (price discount). Sales representatives and TV communication are the main sources of knowledge. Sales promotions’ main influence on purchase behavior is a temporary switch of brands implying that it boosts short term sales but other marketing strategies should support it to promote permanent switching. Loyalty points have potential demand as they are the most intended for use in the near future; next 3 months. Household item followed by clothes are most likely to gain from sales promotions as they had the leading share on impulse purchase.

5.4.3. Personal Selling’s Effects on Purchase Intention The key conclusions arrived at from the findings for the third objective; the effects of personal selling on consumer purchase decisions, are articulated. There is high interaction incidence as about 90 percent have had interface with a sales person. Household items and personal care items leverage on personal selling the most however more persuasion activity is done for food items probably because of their perishability. There is contentment on the information provided by sales person on the promotion, the benefits of the product and the duration of the promotion. As a result, half buy the product while and purchase consideration is increased for 2 out of 5 people. They also increase the trustworthiness and image of a brand. Nevertheless, sales women are preferred to sales men hence the higher influence on purchase. Technology alternatives for personal selling would be social media.

5.4.4. Social Media’s Effects on Purchase Intention The key conclusions derived from the findings of the fourth objective; effects of social media marketing on consumer purchase decisions on FMCG products, are as follows. Facebook and WhatsApp are the best social media networks for achieving wide reach since they are most frequent and widely used. Facebook and Twitter are the best social media platforms for providing adequate product information, however Pinterest, WhatsApp, and Instagram are the best platform for providing effective product information. Product information on promotions and functions of the product influence purchase as half will take action and buy and a significant proportion increase their consideration. Sales for clothes, personal care, food items and household items can be driven through social media as they are most popular product categories on these platforms. Social media marketing is more engaging because it is perceived to be more creative and attractive compared to others.

5.5. Recommendations

5.5.1. Recommendations for Improvements

5.5.1.1. Advertising’s Effects on Purchase Intention The key recommendations derived from the conclusions on the first objective ‘effects of advertising on purchase intention’, are described. Advertisement initiatives to increase trial usage should incite word of mouth and optimize on great experience for great results. Advertising budgets should have priorities on TV advertising and online marketing since they are the main channels of information. Advertisements should be more concentrated in the evening from 7pm onwards because this is when consumers interact with the main channels’ TV and internet. Nonetheless target newspapers readers because newspaper have the strongest positive association for influencing purchase probably because readers go through the information provided more keenly. Internet follows in influencing purchase probably for the same reason as newspaper readers. Brands should build familiarity and trustworthiness because this is most important.

When advertising build familiarity, trustworthiness and provide adequate product information to connect with consumers since these are the most important elements to them. Quality of advertisements can currently be improved by increasing product

information and price information since these are the most unmet gaps in the advertisement content. Use ‘celebrities or famous people’ and ‘discounts or deals’ to drive purchase since they had the strongest, positive correlation. Eliminate factors that cause potential disliking to avoid promoting non-purchase of a brand.

5.5.1.2. Sales Promotion’s Effects on Purchase Intention The key recommendations resulting from the conclusions on the second objective; effects of sales promotions on consumer purchase decisions, are described. Sales promotions should be frequent enough to leverage on the bimonthly to monthly shopping frequency. Competitions and free gifts are underutilized compared to their leading awareness hence increase frequency of these promotions. Price discounts are the most widely used however there is potential to get more effective results through ‘extra amounts’ e.g. buy 2 get 1 free. Leverage on training sales representatives and effective TV communication since they are the main sources of knowledge on sales promotions. Sales promotions should be used to boost immediate sales since they promote temporary switch of brands. Support marketing strategies should be used to promote permanent switching. Marketers should look into loyalty points as consumers anticipate using them the most over the next 3 months. Marketers should bias their sales promotions to household item followed by clothes which drive impulse purchase

5.5.1.3. Personal Selling’s Effects on Purchase Intention The key recommendations derived from the conclusions for the third objective; the effects of personal selling on consumer purchase decisions, are described. Marketers should maintain current performance on personal selling as most customers are happy across the various factors which include the types of products it is done for; household and personal care. Consumers also appreciate the extra convincing efforts implemented for food items. Marketers should also maintain the dissemination of information given as it results in majority taking action and purchasing the product. And it increases the trustworthiness and image of a brand. Marketers should bias recruitment of sales persons to women as they drive purchase more than men. Personal selling initiatives should also be done on social media where consumers have had higher incidence of interacting with the same

5.5.1.4. Social Media’s Effects on Purchase Intention The key recommendations derived from the conclusion for the fourth objective; effects of social media marketing on consumer purchase decisions on FMCG products, are as follows. Marketer should use Facebook and WhatsApp for achieving wide reach since they are most frequent and widely used. Marketers should use Facebook and Twitter for providing adequate product information. Marketers should use Pinterest, WhatsApp, and Instagram for effective results since they drive purchase better. Product information should focus on promotions and the functions of the product since these information types influence purchase on half of the consumers. Marketers should sell clothes, personal care, food items and household items on social media as they are most popular product categories on these platforms. Social media marketing should be leveraged on because it is perceived to be more creative and attractive compared to other marketing platforms.

5.5.2 Recommendation for Further Studies The study was carried out within the Nairobi with a focus on middle income earners. Further studies could be carried out in other counties as the findings from Nairobi residents cannot be generalized to the whole country. Purchase behavior across the regions could vary based on their exposure to marketing strategies and peculiar buyer behaviors across the counties. Further studies could also be carried out across other social economic classes whose purchase patterns vary from middle income earners. There is also an opportunity to carry out the study across other demographic factors besides income, these include buyer behavior across age groups, religion, education levels, media interaction, or occupation.

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APPENDICES

APPENDIX 1: INTRODUCTION LETTER

July, 2017

Dear Respondent,

I am a student in the Chandaria School of Business at United States International University (USIU) and I am conducting a study on consumer behavior in Kenya. The objective of this research project is to attempt to understand the effects of marketing communication on consumer purchase behavior in the FMCG sector in Kenya. Through your participation, I eventually hope to understand how best to satisfy the needs of consumers when it comes to marketing communication.

If you choose to participate in this survey, please note that your name will not be collected. I do not need to know who you are and no one will know whether you participated in this study. Your responses will not be identified with you personally. Nothing you fill in this questionnaire will in any way affect you in future.

I hope you will take a few minutes to complete this questionnaire. Without the help of people like you, research on consumers could not be conducted. Your participation is voluntary and there is no penalty if you do not participate. If you have any questions or concerns about participating in this study, you may contact me at 0723774191 or at [email protected].

Sincerely,

Joy Masimane

MBA Student

Chandaria school of Business

Unites States International University (USIU)

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APPENDIX 2: QUESTIONNAIRE SECTION A: GENERAL QUESTIONS A.1 Tick your gender. SINGLE ANSWER A.1 Male  Female  A.2 Tick your age? SINGLE ANSWER A.2 Below 18 years Close Interview (Respondent does not qualify) 18 to 24 years  25 to 30 years  31 to 40years  41 to 45 years  Above 45 years Close Interview (Respondent does not qualify) A.3 Which of these statements best describes you? SINGLE ANSWER A.3 1. I am fully responsible in deciding on / purchasing the  products that I use 2. I am partially responsible in deciding on / purchasing the  products that I use 3. I am not at all responsible in deciding on / purchasing Close Interview (Does the products that I use not qualify) A4. What is your nationality? SINGLE ANSWER A.4 Kenyan  Other Close Interview (Respondent does not qualify) A.5 What is your NET household income range after deductions? SINGLE ANSWER A.5 Below Ksh. 40,000  Close Interview (Does not qualify) 40,000 to 60,000  60,001 to 80,000  80,001 to 100,000  100,001 to 120,000  Above 120,000  A.7 What is your occupation status? SINGLE ANSWER A.7 Student  Business man / Entrepreneur  Employed  Unemployed  Other 

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A.8 What is your religion? SINGLE ANSWER

Christian – Catholic  Christian – Protestant  Christian – Other  Muslim  Hindu  Buddhist  Other  No religion  A.9 What is your marital status? SINGLE ANSWER

Married / Living together  Single – Never married before  Divorced  Widowed  A.10 What is your level of formal education? SINGLE ANSWER

None  Primary school  Secondary school  College / university 

SECTION B: EFFECTS OF ADVERTISING ON PURCHASE INTENTION B.1 What factors influence you most to try a new product or service? MULTIPLE CODE B.1 Advertisements  Recommendation from friends or family  Recommendation from shop attendants or experts  Seeing famous people use the product  Personal experience  Promotions  Other, please specify:

B.2 Where are most likely to see/ hear/ read advertisements? Please use a scale of 1 to 5 where 1 means ‘Not Likely at all’ and 5 means ‘Very Likely’ TICK ONE PER ROW Not Likely at All Very Likely 1 2 3 4 5 TV      Radio      Newspaper      Magazine      Billboards      Internet     

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B.3 To what extent do these advertisements influence the brands you purchase when doing your shopping? Please use a scale of 1 to 5 where 1 means ‘No influence at all’ and 5 means ‘Very high influence’ TICK ONE PER ROW No Influence at All V. High Influence 1 2 3 4 5 TV Advertisements      Radio Advertisements      Newspaper Advertisements      Magazine Advertisements      Billboards Advertisements      Internet Advertisements      B.4 What do you look out for in an advertisement? PLEASE TICK ALL RIGHT ANSWERS B.5 What do you look out for most in an advertisement? PLEASE TICK ONE ONLY B.6 What do you feel advertisers need to improve most to make you purchase the product? PLEASE TICK ONE ONLY B.4 B.5 B.6 A brand that I am familiar with and trust    Product information    Price information    Celebrities and famous people    Discounts and deals    Humor    A level of consumer interaction    Other, please specify   Other, please specify   Other, please specify   B.7 To what extent do these factors in an advertisement influence your purchase of the product? Please use a scale of 1 to 5 where 1 means ‘Not at All’ and 5 means ‘Very Much’ TICK ONE PER ROW Not at Very All Influenced 1 2 3 4 5 A brand that I am familiar with and      trust Product information      Price information      Celebrities and famous people      Discounts and deals      Humor      Captures your attention      Other 1      Other 2      115

B.9 If you do not like how a brand was advertised, will it influence you not to buy the brand? SINGLE ANSWER

I agree, I will not buy the brand if there is something I disliked  I disagree, I will still buy the brand even if there is something I  disliked B.10 At what times of the day are you most likely to interact with these advertisements? TICK ONE PER ROW Early Mid- Late Morning Morning Afternoon Evening Night Night (6am- (10am- (12pm- (5pm- (7pm- (10pm- 10am) 12pm) 5pm) 7pm) 10pm) 6am TV Adverts       Radio Adverts       Newspaper Adverts       Magazine Adverts       Billboards Adverts       Internet Adverts      

SECTION C: EFFECTS OF SALES PROMOTIONS ON PURCHASE INTENTION C.1 How often do you go shopping in supermarkets? TICK ONE C.2 How often do you encounter sales promotions when you go shopping? TICK ONE C.1 (Shopping) C.2 (Sales promotions) Daily   More than once a week   Once a week   2 3 times a month   Once a month   Less than once a month   C.3 Which of these sales promotions are you aware of? MULTIPLE ANSWER C.4 Which of these sale promotions have you used? MULTIPLE ANSWER C3 (Aware) C4 (Used) Price discounts   Flash sales e.g. Black Tuesday   Free gifts   Buy 1 get 1 free   Extra amount   Loyalty points   Competitions / prize draws   Joint promotions (2 brands being sold together)   Free samples   Other, please specify  C.5 How did you learn about these sales promotions? MULTIPLE ANSWER 116

C.5 Supermarket display  Sales representative in the supermarket  TV  Radio  Newspaper  Magazine  Billboards  Social media e.g. Facebook, Twitter, etc.  Word of mouth e.g. family, friends, others  Other, please specify C.6 In the case of monetary sales promotions e.g. discounts, buy 1 get 1 free, etc. to what extent do you agree with the following statements? Please use a scale of 1 to 5 where 1 means ‘Strongly Disagree’ and 5 means ‘Strongly Agree’ TICK ONE ANSWER PER ROW

Disagree Uncerta

Strongly Strongly

Agree Strongly

- - - -

2 3 4 5 Agree

1 Disagree The promotion helps me make a faster purchase      decision The promotion makes me buy more because I feel      I have saved money The promotion gives me new ideas of things to      buy The promotion makes me temporarily change      brands The promotion makes me permanently change      brands C.7 How likely are you to use these sales promotions in the next 3 months? Please use a scale of 1 to 5 where is means ‘Very Unlikely’ and 5 means ‘Very Likely’ TICK ONE ANSWER PER ROW Very Very Unlikely Likely 1 2 3 4 5 Price discounts      Flash sales e.g. Black Tuesday      Free gifts      Buy 1 get 1 free      Extra amount      Loyalty points      Competitions / prize draws      Joint promotions (2 brands sold      together) Free samples      117

C.8 Please let me know which of the options below you would go for when buying a product in the super market. TICK ONE ONLY Discounts: Buy 2 at 50% off  Buy 2 get 1 free  Buy sh. 100 and get 50% off C.9 Which type of products do you buy on impulse during a promotion? MULTICODE C.10 Out of these, which one do you buy the most on impulse during a promotion? SINGLE ANSWER C9 C10 Food items e.g. beverages, pastries, grains, flour, oil, sugar,  junk food Household items e.g. detergents, pesticides, bleach, bar soap,  etc. Personal care items e.g. lotion, bathing soap, deodorant, hair  food, etc. Clothes  Luxuries; perfumes, cosmetics  Other; stationery, toys, utensils 

SECTION D: EFFECTS OF PERSONAL SELLING ON PURCHASE INTENTION D.1 Have you ever interacted with a sales person in a supermarket? TICK ONE Yes  No  D.2 How often do you encounter sales persons in supermarkets? TICK ONE

Every time I go shopping  Often  Sometimes  Rarely  Almost never  D.3 Which type of product isles will you usually find sales persons? MULTICODE

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D.4 Based on your interaction, how satisfied were you with the information this sales person provided? Please use a scale of 1 to 5 where 1 means ‘Very Dissatisfied’ and 5 means ‘Very Satisfied’ TICK ONE PER ROW Very Very Dissatisfied Satisfied 1 2 3 4 5 Information on what the promotion is      about Information on the benefits of the promotion e.g. how much money you will      save Information on how long the promotion      lasts D.5 What action did you take as a result of interacting with this sales person? TICK ONE

I purchased the product  I did not purchase the product but it increased my consideration to buy it in  future I did not purchase the product and will not buy it in future  D.6 Generally, do you feel that sales persons have an influence on the products you buy? Please use the scale of 1 to 5 where 1 means ‘No Influence at all’ and 5 means ‘Large Influence’ TICK ONE PER ROW No Influence Large at All Influence 1 2 3 4 5 Sales women in the      supermarket Sales men in the supermarket      Shop attendant      D.7 Which products are sales persons most likely to convince you to purchase? MULTICODE

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D.8 Have you interacted with a sales representative on other platforms besides supermarkets? TICK ONE PER ROW Yes No Telephone   Social media   Emails and links   Webchats   Other retail outlets   D.9 To what extent would you agree that sales persons increase the trust you have in a company or brand? Please use the scale of 1 to 5 where 1 means ‘Disagree Completely’ and 5 means ‘Agree Completely’ TICK ONE Disagree Agree Completely Completely 1 2 3 4 5 Sales persons increase my trust in a brand      D.10 To what extent would you agree that sales person improve the image of a brand? Please use the scale of 1 to 5 where 1 means ‘Disagree Completely’ and 5 means ‘Agree Completely’ TICK ONE Disagree Agree Completely Completely 1 2 3 4 5 Sales persons improve the image of the brand     

SECTION E: EFFECTS OF SOCIAL MEDIA ON PURCHASE INTENTION E.1 Which social media networks do you use at least weekly? MULTICODE E.2 Which social media network do you use the most? TICK ONE E.3 In which social media do you get any information on products and brands? MULTICODE E1 E2 E4 Facebook    Twitter    LinkedIn    Instagram    WhatsApp    Snapchat    Pinterest    Online selling platforms e.g.   Jumia, OLX Other (Please specify)  

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E.4 How satisfied are you with the information you receive from your social media networks about products and brands? Please use a scale of 1 to 10 where 1 means ‘Very Dissatisfied’ and 10 means ‘Very Satisfied’ TICK ONE PER ROW Very Very Dissatisfied Satisfied 1 2 3 4 5 6 7 8 9 10 Facebook           Twitter           LinkedIn           Instagram           WhatsApp           Snapchat           Pinterest           Other (Please           specify)

E.5 What type of product information do you get from social media? MULTICODE

Reviews e.g. people’s experiences, opinions, comments on the brand  Promotions  Functions of the product  Events being hosted by the company  Other (Specify)  E.6 To what extent is this product information important to you in making purchase decisions? Please use the scale of 1 to 5 where 1 means ‘Very Unimportant’ and 5 means ‘Very Important’ TICK ONE Very Very Unimportant Important 1 2 3 4 5 Reviews e.g. people’s experiences,      opinions, comments on the brand Promotions      Functions of the product      Events being hosted by the company      Other (Specify)      E.7 What action have you taken as a result of interacting with this information? SINGLE ANSWER

I purchased a product  I did not purchase a product but it increased my consideration to buy  one in future I did not purchase the product and will not buying it in future 

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E.8 Overall, what influence does social media have on your purchase habits? Please use the scale of 1 to 5 where 1 means ‘No Influence at all’ and 5 means ‘Large Influence’ TICK ONE ANSWER PER ROW No Influence Large at All Influence 1 2 3 4 5 Facebook      Twitter      LinkedIn      Instagram      WhatsApp      Snapchat      Pinterest      Other (Please      specify) E.9 What type of products have you purchased from social media? MULTICODE

Food items e.g. beverages, pastries, grains, flour, oil, sugar,  junk food Household items e.g. detergents, pesticides, bleach, bar soap,  etc. Personal care items e.g. lotion, bathing soap, deodorant, hair  food, etc. Clothes, shoes  Luxuries; perfumes, cosmetics  Other; stationery, toys, utensils  E.10 To what extent do you agree with the following statements? SINGLE ANSWER Disagree Agree Completely Completely 1 2 3 4 10 Products I buy on social media are better      priced Products I buy on social media are      delivered to me I physically buy the products I see on      social media Social media marketing is more creative      and attractive compared to others Other (Please specify)

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SECTION F: CONSUMER BUYER BEHAVIOUR (Dependent variable) F.1 How frequently do you go shopping? SINGLE ANSWER

Daily  Weekly  Monthly  Less than monthly F.2 Where do mainly you do your shopping? SINGLE ANSWER

Malls  Large supermarkets  Small supermarket  Other F.3 Who are you mainly with when shopping? SINGLE ANSWER

Alone  With friends/ peers  With family  With colleagues F.4. What is your main consideration when purchasing a product? SINGLE ANSWER

Price  Quality  Communication e.g. advertising, promotions, sales person talking to  you Brand name F.5. Which of these 4 forms of marketing has the highest impact to you when buying a product? SINGLE ANSWER

Advertising  Sales Promotions  Personal Selling  Social Media 

THANK YOU FOR PARTICIPATING IN THIS SURVEY

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APPENDIX 3: BUDGET

Resources Activities Timeframe (days) (sh.)

Data Collection 10,000 14

Printing Report 10,000 1

Total 20,000

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APPENDIX 4: TIMEFRAME

Activity Number of days

Programming of questionnaires 1

Data collection 14

Data processing 5

Report writing 10

Total 30

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