Visual Representation Technologies and Consensus Seeking in Virtual Teams

INFORMATION SYSTEMS RESEARCH Vol. 30, No. 3, September 2019, pp. 948–962 http://pubsonline.informs.org/journal/isre ISSN 1047-7047 (print), ISSN 1526-5536 (online)

Using Technology to Persuade: Visual Representation Technologies and Consensus Seeking in Virtual Teams

Chih-Hung Peng,a Nicholas H. Lurie,b Sandra A. Slaughterc,* a Department of Management Information Systems, National Chengchi University, Taipei 11605, Taiwan; b School of Business, University of Connecticut, Storrs, Connecticut 06269; c Scheller College of Business, Georgia Institute of Technology, Atlanta, Georgia 30332 *Deceased Contact: [email protected], https://orcid.org/0000-0002-7101-7999 (C-HP); [email protected], https://orcid.org/0000-0001-8505-9033 (NHL)

Received: July 8, 2011 Abstract. Although Fogg’s ideas of persuasive technologies are widely accepted, few at- Revised: October 3, 2012; April 26, 2014; tempts have been made to test his ideas, particularly in a team context. In this article, we February 18, 2016; January 18, 2017; October 5, (1) theoretically extend Fogg’s ideas by identifying contexts in which virtual teams are more 2017 likely to use persuasive technologies; (2) empirically measure technology visualness, a factor Accepted: January 11, 2018 that likely makes technologies more or less persuasive; and (3) assess the association between Published Online in Articles in Advance: August 29, 2019 the use of persuasive technologies, judgment shifts, and forecast performance in a real-world virtual team context. We identify visual representation technologies (VRTs) as a class of https://doi.org/10.1287/isre.2019.0843 technologies used by virtual teams to select, transform, and present data in a rich visual format. We propose that such technologies play a persuasive, as well as diagnostic, role in Copyright: © 2019 INFORMS virtual team decisions. Over a three-year period, we examine the daily chat room discussions and decisions of a virtual team that makes smog forecasts with large economic and health consequences. We supplement regression models of ﬁeld data with an experiment, interviews with team members, and analyses of imagery processing and group cohesion in team language use. Experiment results show that, relative to non-VRTs, the use of a VRT in a forecasting task increases imagery processing. Field data results show that team members increase their use of VRTs during chat room discussions when initial

team consensus is low and the environment is more exacting. Greater use of VRTs in team discussions relates to greater shifts in the initial to ﬁnal consensus forecasts of the team and greater odds of the team shifting its forecast policy to issue a smog alert. Increased useofVRTsisassociatedwithlowerforecastbiasbutisnotsigniﬁcantly associated with forecast accuracy. VRT use is also associated with greater imagery processing and increased group cohesion, as shown through language use.

History: Viswanath Venkatesh, Senior Editor; Kai-Lung Hui, Associate Editor. Funding: This article was partially supported by grants from the Research Grants Council, Hong Kong Special Administrative Region, China [CityU 21500614, CityU 11501315, CityU 11505818]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/isre.2019.0843.

Keywords: virtual teams • consensus • persuasion • exactingness • information visualization • decision making • visual representation technologies • forecasting

Introduction the persuasive role of technology in changing team Organizations use teams, rather than individuals, to member attitudes and seeking consensus in virtual team offer multiple perspectives on important decisions settings. Instead, research on virtual teams has tended (Hackman and Morris 1975). Virtual teams enhance to focus on team communication and information pro- the range of perspectives by bringing together in- cessing (i.e., computer-mediated communication (Kock dividuals from different organizations and locations 2004) and group support systems (Dennis et al. 2001)). (Martins et al. 2004). However, reaching consensus Beyond their diagnostic role, we propose that certain can be difﬁcult because team members cannot le- types of information technologies play a persuasive role verage the richness of face-to-face interactions to in virtual team settings. We identify a class of tech- overcome differences in opinions (Sproull and Kiesler nologies called visual representation technologies (VRTs), 1986,Tanetal.1998, Majchrzak et al. 2005). Although which select, transform, and present data in a rich there is substantial research on how virtual teams use visual format (Card et al. 1999, Thomas and Cook technology to exchange information (Powell et al. 2005), and suggest they are associated with greater 2004), and an interest in how technologies persuade informational inﬂuence in virtual team settings, per- individuals (Fogg 1999, 2003), little is known about haps by enhancing imagery processing (MacInnis and

Price 1987, Petrova and Cialdini 2008). We examine associated with greater imagery processing and in- the conditions in which the use of persuasive technol- creased group cohesion. ogies, such as VRTs, is likely to increase; how team use of persuasive technologies affects shifts in judgments Relevant Literature and and changes in policy; and how persuasive technology Hypothesis Development use affects bias and accuracy in team judgments. VRTs as Diagnostic and Persuasive Technologies We identify lack of initial team consensus and en- Technologies play diagnostic and persuasive roles in vironmental exactingness as important contextual team settings (see Appendix A in the online supple- factors affecting the relative need of virtual team ment for a summary of information technology roles members to use persuasive technologies when mak- in team settings). For example, information technol- “ ” ing decisions. By use, we mean using a particu- ogies help team members exchange and diagnose in- lar information technology, or type of technology, to formation that can reduce uncertainty and minimize gather information, and referring to this technology ambiguity in decision making (Dennis et al. 2001). in team discussions. Lack of consensus is a critical VRTs have several aspects that make them useful for challenge in teams of diverse individuals (Alavi and diagnosis, such as enhancing users’ ability to detect Tiwana 2002, Zhang et al. 2007). Exactingness (de- patterns in data because they engage the more effi- fined as greater consequences of small errors of judg- cient visual system (Lurie and Mason 2007). In fore- ment; Hogarth et al. 1991)isalsoimportant,because casting contexts, VRTs can help detect extreme and team members need to be convinced that they are consequential environmental events, such as forest fires, making the right decision (Eisenhardt 1989,Knight better than traditional tools (Al-Saadi et al. 2005). et al. 1999). The combination of low consensus and Information technologies, in addition to their di- high exactingness is particularly challenging because agnostic role, can play a persuasive role in changing team members must persuade others to change their team member attitudes and reaching consensus (Sia fi minds in environments where the consequences of et al. 2002). Fogg (1998, 1999, 2003)identi es aspects errors are high. of persuasive technologies that help them change ’ We hypothesize that virtual team members will in- user opinions or attitudes. Consistent with Fogg s crease their use of VRTs when initial team consensus is framework, and in contrast to nonvisual technologies low and consequences of making small errors of judg- and simple graphics examined in previous studies ment are high; that greater use of VRTs will be associated (Benbasat and Dexter 1985,BenbasatandDexter1986, with larger shifts in the initial to final consensus judg- Jarvenpaa 1989), VRTs have a number of persuasive ments of the team; and that the use of VRTs will be aspects. For example, VRTs use multiple modalities associated with less-biased and more-accurate deci- to display information, an aspect of persuasive tech- sions. We test our hypotheses in a novel real-world nologies (Fogg 2003). This includes the use of visu- setting: air quality forecasting performed by a virtual alization, which is important to persuasion (Fogg team responsible for the five-million-person Atlanta 1998), and involves encoding data or information region in the United States. The result provides a rich as visual objects. For example, in the contour weather context for examining the persuasive role of technology map shown in Figure 1, wind speed is presented by in which team membership and task are relatively wind barbs. Supporting Fogg’s(1998) conjectures, constant but the decision context varies. We supple- prior research shows that visualization can enhance ment regression models of fielddatawithanexperi- attitude change (Pandey et al. 2014). Furthermore, ment, interviews with team members, and analyses many VRTs use simulations—an additional afford- of imagery processing and group cohesion in team ance of persuasive technologies (Fogg 2003). Simu- discussions. lations allow users to see movement and change over Experiment results show that, relative to non-VRTs, time, leading to cause–effect attributions (Fogg 1998, the use of VRTs in a forecasting task increases imagery Oinas-Kukkonen and Harjumaa 2009). Simulations processing. Field data results show that team members allow users to answer “what if” questions by changing increase VRT use when initial team consensus is low simulation options (Fogg 1999). In our context, VRTs and the environment is more exacting. Providing ev- can simulate ozone formation using meteorological idence for VRTs as persuasive technologies, greater use and emission data. of VRTs in team discussions relates to greater shifts in Fogg (1998, 2003) posits that technologies that are the initial to final consensus forecasts of the team and visually attractive and that include images are more greater odds of the team shifting its forecast to issue a persuasive. Visual information is more accessible, has smog alert. Increased use of VRTs is associated with greater credibility, is evaluated more favorably, and is lower forecast bias but not forecast accuracy. Language perceived to be more likely to occur (Sherman et al. analysis of team discussions shows that VRT use is 1985,Lee2004, Lee and Labroo 2004,Schwarz2004). Peng, Lurie, and Slaughter: Visual Representation Technology and Virtual Teams 950 Information Systems Research, 2019, vol. 30, no. 3, pp. 948–962, © 2019 INFORMS

Figure 1. (Color online) VRT Example: 850-Millibar (mb) Weather Contour Map

Moreover, visual information is more vivid and re- images activate brain regions that overlap with those quires fewer cognitive resources to process than non- active when the object is actually viewed (Chao et al. vivid information (Keller and Block 1997). Thus, VRTs 1999,ChaoandMartin2000,Grezes` and Decety 2002, may engage gestalt and automatic processes that are Mellet et al. 2002,Justetal.2004, Magnussen and simple for human decision makers, such as the ability to Helstrup 2007). In addition, brain regions involved in recognize patterns (Larkin and Simon 1987, Kosslyn the processing of images are activated when people 1994, Sloman 1996), enhancing their persuasiveness. read high-imagery sentences (Just et al. 2004). The VRTs may further enhance persuasion by engaging cognitive processes associated with seeing, imagin- imagery processing (MacInnis and Price 1987,Petrova ing, and referencing images, and seeing and using and Cialdini 2008). Imagery processing, as compared imagery words, are the same. In the context of our with discursive processing, is shown by greater ease study, this suggests that using a (visual) map of a of imagination, perceived vividness, emotional re- storm in team discussions should make visualizing sponse, and use of sensory-arousing words in com- the storm easier for team members and increase team munication (Friendly et al. 1982, Bone and Ellen 1992, estimates of an incoming storm. Emrich et al. 2001,Holmesetal.2008). References to images can be particularly important Research suggests that differences in information in team contexts. For example, references to mental processing also occur when one is talking about (but images can help resolve conﬂict in organizational not actually viewing) a physical image. Substantial settings (Von Glinow et al. 2004). Other studies show research shows a strong link between the cognitive that the picture-superiority effect, in which recall is and neurological processes involved in talking about higher for pictures than words (Paivio and Csapo and actually seeing physical images (Kosslyn et al. 1973), is stronger for teams than individuals, even 1999,O’Craven and Kanwisher 2000). References to for private information known only by one person Peng, Lurie, and Slaughter: Visual Representation Technology and Virtual Teams Information Systems Research, 2019, vol. 30, no. 3, pp. 948–962, © 2019 INFORMS 951

(Weldon and Bellinger 1997), and that using images making an error are large. In these conditions, the need to illustrate ideas helps persuade other group mem- to resolve differences in team member opinions increases bers (Stigliani and Ravasi 2012). In sum, multiple because team members share the cost of making a modalities of VRTs, including the use of visualization, wrong decision. More exacting situations also increase simulation, and visual imagery, as well as greater vi- the need for decision makers to believe that they have sual attractiveness, should make VRTs more persuasive reached a correct decision. Therefore, the use of per- (Fogg 2003, Oinas-Kukkonen and Harjumaa 2009). suasive technologies should be even more beneficial. These characteristics should enhance the use of VRTs We predict that lack of consensus interacts with in team settings when the need to persuade is higher. environmental exactingness to affect VRT use. That is, To explore the idea that the use of VRTs enhances the extent to which team members need to influence imagery processing, we conducted a preliminary ex- other members depends on the interaction of team periment in which undergraduate participants who consensus and environmental exactingness. When had taken a meteorology course were randomly as- consensus is high, there is little need for team mem- signedtouseeitheraVRTornon-VRTforecasting bers to use VRTs, regardless of the level of environ- technology, which had identical weather informa- mental exactingness. Similarly, under low consensus tion, to predict air quality for the next day. The VRT and low exactingness, whereby making the right was a Google Motion Chart, and the non-VRT was a decision is less critical and small changes in the team Microsoft Excel sheet (see Appendix B in the online consensus recommendation have a limited impact on supplement). Participants forecast the air quality for outcomes, persuading others should be less necessary. the following day and then read the following state- However, when consensus is low and exactingness is ment: “The weather technology forecasts a decrease high, team members will need to be persuaded to alter in moisture and instability recently. The weather tech- their decisions, which should increase the use of per- nology suggests that the chance of precipitation is re- suasive technologies. In other words, exactingness ducing significantly. Low chances of rainfall and low should moderate the effects of low consensus on the moisture. Therefore, the air quality should be good to- use of persuasive technologies. morrow.” Imagery processing was measured using Hypothesis 1. The association between lower initial con- the mean of two items (Bone and Ellen 1992, r =0.74): sensus and increased use of VRTs in team discussions in- “As you read this statement, to what extent did any creases with environmental exactingness. images come to mind” (1 = to a very small extent; 7 = to a very large extent); “While reading this statement, VRTs and Persuasion I experienced” (1 = few or no images; 7 lots of images). In addition to hypothesizing an interaction between results from analysis of variance showed that imagery initial team consensus and environmental exactingness processing was greater among participants in the VRT on the use of VRTs, we posit that the use of VRTs will be versus non-VRT condition [M =3.92vs.M = VRT NON-VRT associated with enhanced team persuasion. Following 3.08; F(1, 23) = 4.86, p < 0.05], supporting the idea prior research (Simons 1976,O’Keefe 1990), we define that VRT use may be associated with greater imagery persuasion as a communication process in which processing. people affect opinions or attitudes of others (Cialdini 2009). Successful persuasion is evidenced by shifts in Lack of Consensus, Exactingness, and VRT Use beliefs, opinions, attitudes, or choices (El-Shinnawy Reaching consensus is often challenging for team and Vinze 1998,Siaetal.2002)andcanbemeasured members, given the diversity of and geographic dis- through the magnitude and likelihood of judgment tance between virtual team members, time pressure, and policy shifts (El-Shinnawy and Vinze 1998, Sia and difficulty in communicating ideas in online set- et al. 2002). If greater VRT use enhances team per- tings. Difficulties in reaching consensus may not be suasion, it should lead to greater differences between overcome even through technologies with greater so- the initial and final consensus judgments of the team. cial presence and media richness (George et al. 1990, Straub and Karahanna 1998,Dennisetal.2001, Hypothesis 2. Greater use of VRTs is associated with (a) Montoya-Weiss et al. 2001, Miranda and Saunders greater shifts in a team’s initial to final consensus judgments 2003,Kock2004,Dennisetal.2008), suggesting that and (b) greater likelihood of a shift in recommended policies. virtual teams will seek other ways to overcome differences. One way to reach consensus may be by VRTs and Team Performance using technologies with greater persuasive power. We have argued that the use of VRTs is positively re- Another important variable affecting virtual team lated to persuasion, as indicated by shifts in team decisions is the exactingness or the consequences of judgments and policies. However, whether the use of making small errors of judgment (Hogarth et al. 1991). VRTs is associated with higher team performance is When exactingness is high, the consequences of not clear. Indeed, prior research has found mixed effects Peng, Lurie, and Slaughter: Visual Representation Technology and Virtual Teams 952 Information Systems Research, 2019, vol. 30, no. 3, pp. 948–962, © 2019 INFORMS of information technology use on team performance “moderate,” 76–95 (orange) “unhealthy for sensitive (Driskell et al. 2003, Martins et al. 2004). For example, groups,” 96–115 (red) “unhealthy,” 116–374 (purple) some studies have found no difference (Cappel and “very unhealthy,” and over 374 (brown) “danger- Windsor 2000), whereas others have found that ous,” butconditionsinthisrangehaveneveroccurred computer-mediated teams sometimes perform worse in Georgia. In Atlanta, smog alerts are issued in the (Andres 2002), or better (Schmidt et al. 2001), than orange, red, and purple zones. face-to-face teams. Air quality forecasting is difficult, given the un- In forecasting tasks, bias and accuracy are the primary predictability of weather and the health and economic measures of performance (Durand 2003). Bias is the costs of making an incorrect forecast. Excessive ozone difference between predicted and observed forecast exposure has significant health consequences, particu- levels. Negative values for bias reflect a tendency to larly for individuals with respiratory problems, who underpredict, whereas positive values suggest a tendency should stay indoors when ozone levels are high. to overpredict. By contrast, accuracy is the absolute value Air pollution accounts for 10,000 premature deaths of the difference between predicted and observed values. per year in the United States (Caiazzo et al. 2013). More accurate predictions are shown by values closer to However, declaring a smog alert is costly, with an zero. Conversely, higher values reflect greater inaccuracy. economic loss of $58.63 million annually due to school Although most teams seek to minimize bias, some absences alone on smog alert days in Los Angeles teams tend to bias their decisions. The tendency to be (Hall et al. 2008). As smog alert frequency increases, positively or negatively biased depends on the con- individuals reduce ozone avoidance behavior, putting sequences of underprediction or overprediction. For themselves at greater risk (Neidell 2010). example, weather forecasting teams are more likely to The team uses a custom website to access fore- overpredict than underpredict severe weather events, casting technologies. These technologies include text- because their primary responsibility is public safety. based weather forecasts, interactive graphical contour Given this, we propose that greater use of VRTs will plots and maps, current readings from air sensing devices reduce decision bias. In particular, to the extent that in the area, satellite imagery, and regression and other VRTs are more persuasive, their use should increase statistical models. Nonvisual technologies, such as the decision confidence (Fogg 2003, Oinas-Kukkonen and weather diagnostics tool in Figure 2,providetextual Harjumaa 2009). If teams are more confident in their and numeric information, including temperature forecasts, they should be less likely to bias their forecasts readings, air speed, and dew point, and contain few to mitigate the consequences of being wrong. Because visual images. VRT technologies are found at the other accuracy is the absolute value of bias, greater use of end of the spectrum, such as the 850-mb contour satellite VRTs should also be associated with greater accuracy. weather map in Figure 1, which presents visual images and uses color to convey differences in informationbut Hypothesis 3. Greater use of VRTs is associated with (a) also shows numeric information such as air pressure, decreased forecasting bias and (b) increased forecasting wind speed, and temperature. Appendix C in the online accuracy. supplement provides details on the portfolio of information technologies available to the team. Method Every day at 1:30 p.m., during the ground-level Research Setting ozone season, the smog forecasting team meets in an To evaluate our hypotheses, we conducted a field internet chat room to discuss and reach consensus on study using daily chat room discussion data as well as the team’s ozone forecast for the following day. Be- predicted and actual air quality levels from a virtual fore joining the chat room, each team member inputs smog forecasting team in Atlanta, Georgia, from 2006 his or her initial forecast. Individuals are not allowed to 2008. The team in our study is composed of 10 to view other forecasts before submitting their own research scientists from a major research university in forecasts. Before the discussion, the website computes Georgia and the state’s Environmental Protection the average of individual forecasts, which serves as Division (EPD). Their primary task is to forecast air the team’s initial forecast of the ozone concentration quality (i.e., the level of ozone pollutant concentration for the next day. During the discussion, individual in parts per billion [PPB]) and make a forecast recom- team members often defend or clarify their pre- mendation (whether to call a smog alert) for the sub- dictions using particular information technologies. sequent day in the Atlanta metropolitan area and The team must reach a consensus forecast. The fore- surrounding cities during the ground-level ozone cast includes the predicted ozone concentration level season (from May 1 to September 30). During the time for the next day and the recommendation of whether of our study, U.S. Environmental Protection Agency to call a smog alert. Team forecasts are posted on the (EPA) standards specified that PPB values of 0–59 State of Georgia’s EPD website by 2 p.m. and sent to (green) were considered “good,” 60–75 (yellow) the news media by email. Peng, Lurie, and Slaughter: Visual Representation Technology and Virtual Teams Information Systems Research, 2019, vol. 30, no. 3, pp. 948–962, © 2019 INFORMS 953

Figure 2. (Color online) Non-VRT Example: Weather Diagnostics Tool

Figure 3 shows an online chat on the day the team and 2008 from the Ambient Monitoring Program issued a smog alert. The team’s initial forecast is 93 database of the Georgia EPD. The team makes ozone PPB—equivalent to an orange-level smog alert, in forecasts for May, June, July, August, and September which conditions are considered unhealthy for sen- each year. From the smog forecasting website, we sitive groups. During the discussion, proponents of a extracted (1) initial individual forecasts of the ozone higher forecast level use VRTs to convince the team to concentration for the next day, (2) the team’s final raise the forecast to 96 PPB—equivalent to a red-level consensus forecast, and (3) the text of the team’s alert, in which ozone levels are considered unhealthy online chat. We also interviewed key informants from for all. For instance, forecaster A uses wind infor- the virtual team—one research scientist from a ma- mation from the North American Mesoscale Forecast jor university and two scientists from the EPA in System (NAM) model to support the argument that Atlanta—and observed a meeting in which the team the forecast ozone level should be higher. This leads made a smog prediction, after which we asked team others (e.g., forecaster C) to agree to a higher-level members questions about the forecast process. alert. The actual ozone reading for the next day is captured by sensors in the field. Dependent Variables Technology Visualness Corrected for Chance. To as- Data Collection sesstheextentofVRTuse,wefirst examined the full We obtained observed ozone values for each day range of the 22 technologies available to the team.1 We during the ground-level ozone season in 2006, 2007, compute a technology’s visualness by dividing the Peng, Lurie, and Slaughter: Visual Representation Technology and Virtual Teams 954 Information Systems Research, 2019, vol. 30, no. 3, pp. 948–962, © 2019 INFORMS

Figure 3. (Color online) Example of Daily Chat Room Discussion

Note. In this example, the smog forecasting team refers to three VRTs (NAM, Nested Grid Model (NGM), and NAM bufkit–a visualization and analysis tool that uses NAM data) to help them make a prediction for July 19, 2008. number of information attributes presented in a visual choices of individual team members before discussion, format (e.g., graphic images rather than numbers) by to the group’sconsensusjudgment—or choice after the total number of different attributes. For instance, discussion as well as categorical shifts in policy (El- the 850-mb plot shown in Figure 1 includes seven Shinnawy and Vinze 1998, Sia et al. 2002). Follow- information attributes: four are presented in a visual ing prior research (e.g., Crott et al. 1991, El-Shinnawy format (contour height, contour temperature, wind and Vinze 1998), Forecast Shift is the difference be- speed, and wind direction), and three are presented in a tween the final and the initial team forecasts in PPB nonvisual format (temperature, dew point, and pres- for each day. We measure policy shifts in ozone sever- sure). Thus, the visualness of the 850-mb plot tech- ity levels using a categorical variable, Up-Down Shift, nology is 4/7 = 57%. coded as “0” if the team’sinitialandfinal ozone se- We calculate VRT use as the average visualness of verity level decisions are the same, “−1” if the team technologies used by the team on a particular day shifts to a lower (less serious) ozone severity level Technology Visualness (TV). TV controls for potential (e.g., from yellow to green), and “+1” if the team shifts difference in the number of technologies used. For from an initial severity level to a higher (more serious) example, in the chat room discussion shown in severity level (e.g., from yellow to orange). Figure 3, the team used three technologies (NGM, NAM, and Bufkit), whose visualness measures are all Team Performance. We compute forecast Bias as the 100%, so TV on that day is equal to 100%. To correct difference between predicted and observed ozone thepercentageofinformation(76%)thatisvisual concentration, divided by the observed ozone concen- across all available technologies, following Lurie tration level. Negative values indicate underprediction, (2004), we compute Corrected Visualness (CV)on whereas positive values indicate overprediction. To − day i as CV TVi Pi,whereCV = corrected technol- ease interpretation, we compute the Inaccuracy of the i 1−Pi i forecast as the absolute value of forecast bias. Larger ogy visualness, TVi = unadjusted technology visu- numbers indicate greater inaccuracy. To facilitate alness, and Pi = the percentage of information that is visual across all available technologies. For example, interpretation, bias and inaccuracy are multiplied in the chat room discussion shown in Figure 3, Cor- by 100. rected Visualness is (100%–76%)/(1%–76%) = 1. We use the corrected measure of technology visualness (CV) Independent Variables in our analysis. Following prior research (Lahiri and Teigland 1987), initial team consensus (Lack of Consensus)ismeasured Persuasion. Persuasion in team settings can be mea- as the variance in initial individual forecasts on a sured as change from the average judgments—or given day. We use two binary variables to represent Peng, Lurie, and Slaughter: Visual Representation Technology and Virtual Teams Information Systems Research, 2019, vol. 30, no. 3, pp. 948–962, © 2019 INFORMS 955 exactingness and code YO as “1” if the initial team with greater use of such technologies. Ordinary Least perception is within 5 PPB of the yellow–orange Squares (OLS) results, shown in model 2 of Table 2, border and OR as “1” if the initial team perception Lack of Consensus × YO (β =0.012,p < 0.01) and Lack of is within 5 PPB of the orange–red border and “0” Consensus × OR (β =0.007,p < 0.10) are positively otherwise.2 associated with Corrected Visualness. A joint test of these two interactions yields an F value of 4.63, p = Control Variables 0.01, showing that overall, exactingness moderates To control differences in ground-level ozone con- the relationship between the lack of consensus and the centrations on weekdays versus weekends, as well as use of VRTs. For robustness, given the time-series holiday, month, and year effects (Dye et al. 2003), we nature of the data, we re-estimated the technology code Weekday as “1” for Monday to Friday and “0” visualness model using the Cochrane-Orcutt pro- otherwise. We code Holiday as “1” for holiday and “0” cedure and found consistent results, leading us to otherwise. We also use 14 binary variables, Jun06 conclude that serial correlation is not an issue in through Sep08, to distinguish month–year pairs from our data (see Appendix D in the online supplement). thebasemonthofMayin2006(i.e.,May06). As an additional robustness check, we coded each For each day, we include Team Size as the number of statement in each team chat as either (1) initial av- forecasters who posted an initial forecast. On the basis erage, (2) agreement statement, (3) persuasion attempt of academic training, we classify each team mem- using VRTs, (4) persuasion attempt not using VRTs, ber as “meteorologist,”“environmental geochemist,” or (5) other statement. Re-estimating the model, “statistician,” or “other.” We also identify whether substituting the proportion of VRT persuasion at- the team member is located at a major research uni- tempts for the dependent variable, led to similar versity in Georgia or at the state’sEPD,aswellastheir results (see Appendix E in the online supplement). gender. Following past research (Knight et al. 1999), Therefore, Hypothesis 1 is supported. the daily expertise diversity, location diversity, and gender diversity of the team are computed using Impact of VRTs on Persuasion ’ ( − 2) Blau s heterogeneity index, 1 pi ,wherepi is the Tobit model results in Table 3 show that VRT use is proportion of the team in the ith category partici- positively related to shifts in initial to final forecasts pating in the team discussion for that day. We control (β =0.026,p < 0.05).3 A multinomial logistic model for for team participation by dividing the number of Up-Down Shift, testing whether the use of VRTs re- forecasters who participate in a team discussion lates to upward or downward shifts in the initial to by the number of forecasters who post their initial final team forecast policy, indicates that, although an forecast on a particular day. We include forecaster increase in the use of VRTs increases the relative log dummies to account for individual differences in odds of shifting to a more serious ozone level (β =0.359, forecasting behavior and team influence. To control p < 0.01), an increase in the use of VRTs does not affect for the amount of information available to the team, we the relative log odds of shifting to a less serious level compute the product of the number of information at- (β = −0.056, p > 0.10). That is, the team is more likely tributes and the number of observations shown at a time to shift to a more serious ozone level as VRT use in the default setting for a technology and calculate the increases. In summary, these results provide support average amount of information for technologies used on for Hypotheses 2a and 2b. a particular day (Information Amount). VRT Use and Performance Analysis and Results Table 4 displays the results of regression analyses for The data on technology use, environmental exact- Bias and Inaccuracy. Supporting Hypothesis 3a,but ingness, and initial team consensus for each day in the not 3b,wefind a significant effect of Corrected Visual- ozone season in each year yielded 457 daily obser- ness on Bias (β = −1.400, p < 0.05) but, although the vations from the period 2006–2008. Table 1 reports the coefficient is in the expected direction, the results for means, standard deviations, and correlations of the Inaccuracy are not significant (β = −0.567, p > 0.10). variables in the analysis. Pairwise correlations be- Thus an increase in VRT use is associated with a re- tween the independent and control variables in our duction in team forecast bias but not significantly analysis are modest, with values below 0.40. improved forecast accuracy.

Use of VRTs Use of VRTs and Imagery Processing Hypothesis 1 posits a moderating relationship be- To gain further insight into why VRT use may enhance tween lack of consensus and environmental exact- persuasion in virtual teams, following Just et al. (2004), ingness on the use of VRTs, such that at lower levels of we explore whether mentions of VRTs in group dis- consensus, exacting environments will be associated cussions are associated with language reﬂecting imagery Peng, Lurie, and Slaughter: Visual Representation Technology and Virtual Teams 956 Information Systems Research, 2019, vol. 30, no. 3, pp. 948–962, © 2019 INFORMS

processing (Martindale 1975,Friendlyetal.1982, — 0.04

− Emrich et al. 2001, Seyranian and Bligh 2008). For each daily team discussion, we count the number of —— 0.07 0.02 − − image- and concept-based words in the chat using Martindale’s(1975) regressive imagery dictionary of 4 ——— 0.03 2,900 image words (e.g., imagine, see, rock, journey). − We regress the number of image-based words on VRT use, controlling the total number of words. We ﬁnd a — ——— 0.04 0.07 0.06 ﬁ Corrected Visualness − − signi cant effect of on the use of image-based words (β =0.68,p < 0.00). By contrast,

— — ——— regressing the number of concept-based words on VRT use does not show a signiﬁcant effect of Corrected Visualness (β =0.28,p > 0.10). Consistent with the — — — ——— 0.01

− experiment, these ﬁndings show that the use of VRTs is associated with image (but not concept) words and

— — — — ——— imagery language and offer a possible mechanism 0.08* 0.07 0.35**

− for the observed results. In addition, forecaster interviews indicate that team chats convey images of ———————— 0.03 0.01 0.03 0.02 0.00 0.03 0.03 0.04 0.03 0.01 0.06 − − − forecasting technology outputs. For example, one said “we can imagine what the forecaster is saying ” ————————— ————————— ————————— ————————— ————————— without looking at the model. As with the prelimi- 0.08 0.00 0.13*** 0.02 0.33** 0.23*** 0.03 0.07 0.00 0.14*** 0.06 0.30*** 0.12*** 0.06 − − − − − nary experiment, language analysis and interviews support the proposal that VRT mentions in team chats

— ————————— invoke imagery processing by team members. 0.02 0.01 0.01 0.22*** 0.04 0.15*** 0.01 0.04 0.03 0.03 0.06 0.07 0.04 0.09* − − − − − − Use of VRTs and Group Cohesion

— — ————————— We have argued that VRTs help teams reach con- 0.13*** 0.01 0.05 0.03 0.00 0.11**

− − − sensus and enhance team persuasion. That is, such technologies lead to shifts in team decisions and

— — — ————————— help teams coalesce around these decisions. Previous 0.13** 0.01 0.03 − − − studies (e.g., Dennis et al. 2008,Sarkeretal.2010, Zhang et al. 2011) investigate the relationship be- 45678910111213141516 ———— tween technology usage and team interaction, using 0.02 0.12** 0.12** 0.07 0.10** 0.07 0.04 0.04 0.04 0.15** 0.15*** − − − − communication content to measure group social dynamics (Gonzales et al. 2010, Chung and Pennebaker ————— 0.09* 0.00 0.13*** 0.01 0.00 0.12** 0.12** 0.01 0.01 0.26** 0.26*** 0.10** 0.05 2014). Building on this work, we examine how VRT − − − − − − use is associated with group cohesion, as measured through language use. We focus on group cohesion —————— 0.04 0.54*** 0.06 0.12** 0.12** 0.01 0.01 0.02 0.06 0.12** 0.12** 1.00*** 0.01 0.030.02 0.13***0.01 0.07 0.04 0.22*** 0.13*** 0.26*** 0.11** 0.01 0.14*** 0.11** 0.14*** 0.01 0.05 0.01 0.06 0.06 0.03 0.06 because it is related to team consensus and persua- − − − − − − − − − − − − sion (Yoo and Alavi 2001). Following Gonzales et al. (2010), we take two approaches to examine group ——————— 0.04 0.09* 0.09* 0.06 0.06 0.06 0.06 ﬁ − − − − − − − cohesion: (1) the use of rst-person plural pronouns and (2) linguistic style matching (LSM) in daily team chats. We use the Linguistic Inquiry and Word Count program (Pennebaker et al. 2007) to assess the use of ﬁrst-person plural pronouns (e.g., “we,”“us”)and 0.10. 7.03 22.05 0.02 0.26 0.12 0.77 0.03 0.01 0.580.45 0.14 0.08 0.03 0.03 1.78 1.81 0.27 0.16 0.230.05 0.42 0.21 0.06 0.04 0.93 0.12 0.03 0.72 0.45 0.02 0.14 0.11** 0.10** 4.91 1.29 nine function words (auxiliary verbs, common adverbs, 16.09 16.62 0.75*** 33.26 43.43 31.90 34.65 < − p

Mean SD 1 2 3 personal pronouns, impersonal pronouns, articles, 11,220.76 65,702.20 0.02 0.03 prepositions, conjunctions, negations, and quantiﬁers).

0.05; * Following previous studies (Ireland and Pennebaker <

p 2010, Ludwig et al. 2014), we measure LSM as the extent to which the intensity of function words in a Means, Standard Deviations, and Correlations

0.01; ** given team chat is similar to the average intensity = 457. Forecaster and month dummies are included. SD, standard deviation.

< in all team chats. We average the nine LSM scores p Expertise diversity Location diversity Gender diversity Participation Weekday Information amount Holiday Team size

*** to generate a composite LSM score. A greater com- Inaccuracy Bias Up-down shift Technology visualness Forecast shift Corrected visualness Lack of consensus YO OR Table 1. 2. 1. 4. 5. 3. 13. 14. 6. 15. 7. 16. 8. 9. 10. 17. 11. Notes. N 12. posite LSM score indicates greater linguistic style Peng, Lurie, and Slaughter: Visual Representation Technology and Virtual Teams Information Systems Research, 2019, vol. 30, no. 3, pp. 948–962, © 2019 INFORMS 957

Table 2. Antecedents of VRT Use (Technology Visualness technologies in team discussions is associated with Corrected for Chance) enhanced persuasion, as shown through greater shifts in team policies, and that VRT use is associated with Model 1 Model 2 improved decision performance by virtual teams. We (base model) (expanded model) test our ideas using an experiment, archival data, Independent variable Coefficient t-value Coefficient t-value team observation, and interviews. Intercept −3.802*** −3.96 −3.667*** −3.78 Weekday 0.103 0.55 0.167 0.89 Discussion of Findings Holiday 0.973* 1.96 0.963* 1.88 Results from our study of the virtual team responsible Team size −0.104 −0.36 −0.081 −0.28 − − − − for forecasting ozone levels and issuing smog alerts Expertise diversity 0.032 0.03 0.150 0.16 fi Location diversity 1.975** 2.32 1.579* 1.80 for the 5-million-person Atlanta region con rm many Gender diversity −2.210 −1.56 −2.525* −1.77 of our hypotheses. In particular, the team increases its Participation −0.383 −0.59 −0.361 −0.55 use of more visual technologies when initial team Lack of consensus ——0.002 0.69 consensus is low and the consequences of small errors ——− − YO 0.405* 1.71 of judgment are large. These findings add to prior OR ——0.345 0.84 Lack of consensus × YO ——0.012*** 2.90 research showing how task characteristics determine Lack of consensus × OR ——0.007* 1.78 technology choice (Dennis et al. 1988,Georgeetal. R2 0.20 — 0.22 — 1990, Straub and Karahanna 1998). F 5.75 — 8.56 — Our prediction of a relationship between the use Notes. N = 457. Forecaster and month dummies are included. of VRTs and persuasion is supported. Greater use of ***p < 0.01; **p < 0.05; *p < 0.10. VRTs in team discussions is associated with a larger shift between initial and final consensus forecasts matching. A regression showed a significant effect of and greater likelihood that the team shifts its fore- Corrected Visualness on the use of first-person plural casts to a more serious ozone severity level in which words (β = 0.038, p < 0.05) as well as composite LSM smog alerts are issued. These results are materially (β =0.026,p < 0.01). These results support the idea significant, given the economic and health conse- fi

that greater VRT use is associated with greater group quences of issuing smog alerts. These ndings add cohesion. to research showing that technology choice can affect persuasion (El-Shinnawy and Vinze 1998, Sia et al. Discussion and Conclusions 2002). Importantly, the use of VRTs is associated with Drawing on the pioneering work by Fogg (1998, 1999, greater persuasion even in the absence of increased 2003), we propose that VRTs play important per- communication channels or social presence. suasive and diagnostic roles in virtual teams. We Our analyses provide partial support for a positive identify low-consensus and high-exacting contexts relationship between VRT use and decision quality, as those in which the use of persuasive technologies showing that the use of VRTs in team discussions re- should increase. We posit that increased use of these duces decision bias; in the context we study, a tendency

Table 3. Technology Visualness (Corrected for Chance) and Persuasion

Model 3 (tobit model) Model 4 (multinomial logistic model)

ForecastShift UpDownShift = −1 UpDownShift =+1

Independent variable Coefficient t-value Coefficient z-value Coefficient z-value

Intercept −0.036 −0.15 −37.264 0.00 −9.572** −2.00 Weekday 0.003 0.07 −2.439*** −2.81 −1.022* −1.81 Holiday −0.159 −1.08 5.137** 2.51 −19.358 0.00 Team size −0.039 −0.58 −1.002 −0.65 −0.818 −0.86 Expertise diversity 0.246 1.13 −9.622* −1.76 6.007 1.22 Location diversity 0.153 0.61 1.992 0.25 −4.351 −1.19 Gender diversity −0.384 −1.16 −3.917 −0.51 1.313 0.25 Participation 0.152 1.13 1.615 0.42 6.108* 1.76 Information amount 0.000 −1.34 0.000 −0.45 0.000 −0.25 Corrected visualness 0.026** 2.45 −0.056 −0.21 0.359** 2.24 Log likelihood −242.82 — −94.47 χ2 35.15 — 76.71

Notes. N = 457. Reference category is UpDownShift = 0. Forecaster and month dummies are included. ***p < 0.01; **p < 0.05; *p < 0.10. Peng, Lurie, and Slaughter: Visual Representation Technology and Virtual Teams 958 Information Systems Research, 2019, vol. 30, no. 3, pp. 948–962, © 2019 INFORMS

Table 4. Technology Visualness (Corrected for Chance) and Theoretical Implications Team Performance Although the ideas presented by Fogg (1999, 2003)on persuasive technologies are widely accepted, few at- Model 5 Model 6 tempts have been made to test them. This article tests (inaccuracy) (bias) some of these ideas in a real-world virtual team setting. Independent variable Coefficient t-value Coefficient t-value Our findings are important because they identify an − − overlooked role for technologies that are traditionally Intercept 18.583 1.54 11.191 0.68 “ Weekday −1.160 −0.66 −2.946 −1.26 thought of as diagnostic tools and highlight the per- Holiday 11.089 1.5 18.513** 2.49 suasive” role of these technologies. Beyond the ca- Team size −4.643* −1.93 −7.312** −2.16 pacity to draw on and display different types of − − − − Expertise diversity 4.037 0.46 4.531 0.33 information and aid diagnosis, information technol- Location diversity 1.799 0.20 10.792* 0.72 Gender diversity 16.172 1.22 11.050 0.59 ogies vary in the extent to which they are persuasive Participation −4.055 −0.44 4.972 0.44 (Fogg 1999, 2003). By focusing on the persuasive role Information amount 0.000 0.95 0.000 1.57 of technologies in virtual team settings, we contribute Corrected visualness −0.567 −1.25 −1.400** −2.23 to research focused on IT as a tool for communication 2 — — R 0.09 0.10 (e.g., Daft et al. 1987) and decision support (e.g., F 1.80 — 1.91 — Zigurs et al. 1988, Todd and Benbasat 1999). Notes. N = 457. Forecaster and month dummies are included. Our research provides an approach to empirically < < < ***p 0.01; **p 0.05; *p 0.10. examine persuasive technologies in team settings. Fu- ture researchers can build on our research by (1) ac- to over-predict smog levels in the interest of public counting for virtual teams’ useofpersuasiveinformation safety is found. The use of VRTs may help to address technologies beyond those designed specifically to sup- some of the limitations of virtual team environments port communication and decision making; (2) measur- that lead to differences in virtual team members’ ing or manipulating technology visualness and other interpretation of information, lack of cues to infor- factors likely to affect persuasion and consensus build- mation importance, and difficulties in understanding in team settings; and (3) assessing the impact of ing the contexts in which other team members act persuasive technologies on virtual team dynamics, (Cramton 2001). However, VRT use is not signifi- persuasion, consensus, and performance. cantly associated with decision accuracy. One pos- Furthermore, we contribute to research on infor- sible explanation may be that, although VRTs mation visualization and its impact on virtual team facilitate the detection of patterns in data, the acti- decision making. Although prior research has argued vation of intuitive decision processes may also lead that visualization has beneficial effects on human virtual teams to draw false inferences from the data cognition (Card et al. 1999,ThomasandCook2005), (Kahneman 2011). and some conceptual work has focused on the impact The results of the preliminary experiment in which of visualization on decisions by individuals (Lurie and exposure to VRTs was manipulated and imagery Mason 2007), few attempts have been made to iden- processing measured through scales show that, rel- tify when visualization technologies are likely to be ative to using non-VRTs, the use of VRTs increases used and few attempts to measure the relationships imagery processing. Analysis of team language use among visualization technology use, dynamics, and/ provides additional supportive evidence, as shown or performance, particularly in virtual team settings. by a significant association between the use of VRTs Finally, our use of longitudinal data from the his- and use of image-based, but not concept-based, toricalrecordofavirtualteam’s chatroom discus- words in team discussions. To the extent imagery sions contributes to prior literature based on survey processing is associated with persuasion (Sherman or experimental data (e.g., El-Shinnawy and Vinze et al. 1985, Keller and McGill 1994, Keller and Block 1998,Siaetal.2002, Majchrzak et al. 2005). Our 1997, Green and Brock 2000, Green and Brock 2002, longitudinal field study design complements exper- Tormala et al. 2002,Lee2004,LeeandLabroo2004, imental studies on virtual teams and enables us to Petrova and Cialdini 2008), this may explain why examine the decision making of an expert team over visualization and imagery are important elements time with large health and economic consequences of persuasive technologies (Fogg 1999, 2003). Addi- and for which the consequences of making small tional analyses show that the use of VRTs is associated errors are significant. Such a setting would be difficult with greater group cohesion, indicated by the use of to mimic in a laboratory. first-person plural pronouns and LSM (Gonzales et al. 2010). These results provide insights into how VRTs Managerial Implications affect team interactions and support the idea that As virtual teams continue to play an important role in VRTs can help virtual teams reach consensus. many organizations, this study has the potential to Peng, Lurie, and Slaughter: Visual Representation Technology and Virtual Teams Information Systems Research, 2019, vol. 30, no. 3, pp. 948–962, © 2019 INFORMS 959 offer important managerial implications. When de- heuristic (vs. systematic) team decision processes, then ploying IT for use in virtual team settings, managers an optimal level of visualness, above which declines should consider their effects on individual decision in team performance are found, may be observed. makers and on team processes. That is, even tech- Similarly, although greater visualness in information nologies not explicitly designed for team communi- technologies may be associated with greater team co- cation or information processing (Daft et al. 1987, hesion, a downside of using such technologies in virtual Zigurs et al. 1988) may help teams reach decisions in team settings may be groupthink, in which a team fails exacting conditions. Our research also points to the to consider a wide range of alternative points of view need to consider the persuasive characteristics of and decision paths (Furst et al. 1999). technologies (Fogg 1999, 2003) in virtual team set- Our study presents initial evidence that VRT use tings, in addition to functionality and usability. The engages imagery processing and leads to greater group idea that team members can influence one another cohesion, as shown by language use. Future research by using technologies, and that some technologies could examine other cognitive processes engaged by are associated with more persuasion than others, is technologies in team settings and identify those with powerful and problematic. VRT use is associated with the largest impact on persuasion and team decision imagery processing, and imagery processing is asso- making. Studies could also examine how making VRTs ciated with heuristic (i.e., “fast”) decisions (Kahneman available to virtual teams compares to alternative ap- 2011). Therefore, overuse of persuasive technologies proaches such as using computer-mediated commu- may have negative consequences on the quality of nication technologies and group support systems. As team decisions (although our results suggest that, in virtual teams gain access to increasingly sophisticated some cases, increased use of VRTs can reduce decision and persuasive technologies, understanding how biases). Training teams on the advantages and dis- these technologies influence teams in unexpected advantages of persuasive technologies is important. and intended ways will be important.

Limitations and Directions for Future Research Acknowledgments Like all types of research, our study has limitations The authors thank the senior editor, associate editor, and anonymous reviewers for valuable feedback and Michael and presents opportunities for future research. Our Chang and members of the Atlanta Air Quality Forecasting examination of a single-expert virtual team highly Team for sharing the data used in this paper and their in- versed in the available technologies controls for dif- sights on the smog forecasting process. The authors dedi- ferences across teams and technology familiarity. cate this paper to their late advisor, mentor, and coauthor, That said, verifying whether VRT use shows similar Sandra A. Slaughter. relationships for novice teams, for teams less familiar with technologies, or for face-to-face teams is im- Endnotes portant. Although our field data provide strong ex- 1 Appendix C in the online supplement shows the technology visu- ternal validity, the cross-sectional design of our study alness, corrected visualness, information amount, and reference frequency of each technology available to the team. makes direct examination of causal relationships 2 difficult. The causal relationships among VRT use, Interviews and discussion after observing the team forecasting process revealed that team members perceive forecasts near the judgment and policy shifts, bias and accuracy, im- yellow–orange and orange–red smog alert borders as the most ex- agery processing, and group cohesion are difficult to acting. The yellow–orange border potentially involves issuing a smog disentangle because many of the studied variables are alert for sensitive individuals, and the consequences of making an measured simultaneously. Although the strict tem- error in issuing or failing to issue a smog alert are even larger for the – poral sequencing of events, and the use of multiple orange red border. The team did not express similar concerns about the green–yellow border because it does not involve a smog alert. controls for alternative explanations, helps mitigate Team members indicated that initial forecasts within 5 PPB of a color these concerns, and our use of multiple methods in- category border require further consideration. Using 4 PPB and 6 PPB cluding experiments and interviews provides sup- shows consistent results. Using continuous rather than binary portive evidence, these causal concerns cannot be measures of exactingness, coding YO as “5” if the initial team per- – “ ” eliminated entirely. Our archival data may also suffer ception was within 1 ppb of the yellow orange border, 4 if the initial team perception was within 2 ppb, “3” if the initial team from selection bias because we are unable to capture perception was within 3 ppb, “2” if the initial team perception was the entirety of information technologies used by team within 4 ppb, “1” if the initial team perception was within 5 ppb, “0” members and rely on references to these technologies otherwise, and likewise for OR, leads to similar results. in the group chats. Other studies could design ex- 3 Because the team did not shift forecasts every day, there are days for periments to address these causality and selection which Forecast Shift equals 0 and OLS estimates may be biased and bias issues. inconsistent (Greene 2003). We are interested in the relationship between VRT use and policy change (i.e., Forecast Shift > 0orForecast Other studies could also examine the potential Shift < 0); thus, a two-limit Tobit regression is appropriate. The lower downsides of persuasive technologies in team settings. limit is −1 (a shift to a lower ozone level) and the upper limit is 1 (a For example, if elements such as visualness lead to shift to a higher level). Peng, Lurie, and Slaughter: Visual Representation Technology and Virtual Teams 960 Information Systems Research, 2019, vol. 30, no. 3, pp. 948–962, © 2019 INFORMS

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