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Proceedings of the Twelfth Annual SIG IS-CORE 2016 Pre-ICIS
Cognitive Research Workshop
Dublin, Ireland December 11, 2016
ORGANIZING COMMITTEE
Teresa Shaft (Chair) University of Oklahoma
Robert F. Otondo Mississippi State University
Emre Yetgin Rider University
Jim Buckley University of Limerick
The Association for Information Systems
Special Interest Group on Cognitive Research
Table of Contents
Workshop Agenda 1
Sangseok You & Lionel Robert, Do You Trust Me? Examining the Impacts of Trust in Robots and Trust in Humans on Performance and Satisfaction of Teams Working with Robots.
2
Hadi Karimikia & Harminder Singh, Being Useful: How Information Systems Professionals Influence the Use of Information Systems in Enterprises.
4
Michael P. O’Brien & Jim Buckley, Building a Model of the Information Search & Retrieval Processes of Industrial Software Practitioners.
6
Andreas Hoegen, Dennis M. Steininger & Daniel Veit, An Interdisciplinary Review of Crowdfunding Investment Decisions.
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Emre Yetgin, The Measurement and Implications of Cognitive Fit in Visualizing Big Data.
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Time Event 8:30 – 9:15 Breakfast Buffet & Registration 9:15 – 9:30 Welcome Presentations
9:30 – 10:30 Sangseok You & Lionel Robert - Research Paper Do You Trust Me? Examining the Impacts of Trust in Robots and Trust in Humans on Performance and Satisfaction of Teams Working with Robots
10:30 – 11:30 Hadi Karimikia & Harminder Singh – Research Paper Being Useful: How Information Systems Professionals Influence the Use of Information Systems in Enterprises
11:30 - 1:00 Lunch
1:00 – 1:45 Michael P. O’Brien & Jim Buckley - Invited Presentation Building a Model of the Information Search & Retrieval Processes of Industrial Software Practitioners
1:45 – 2:45 Andreas Hoegen, Dennis M. Steininger & Daniel Veit - Research Paper An Interdisciplinary Review of Crowdfunding Investment Decisions
2:45 - 3:30 Coffee Break and Business Meeting
3:30-4:15 Emre Yetgin – Invited Presentation The Measurement and Implications of Cognitive Fit in Visualizing Big Data
4:15-4:30 Closing comments
The Association for Information Systems
Special Interest Group on Cognitive Research
Twelfth Annual SIG IS-CORE 2016 Cognitive Research Workshop
Dublin, Ireland December 11, 2016 Gresham Hotel, O’Connell 2
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Do You Trust Me? Examining the Impacts of Trust in Robots and Trust in Humans on Performance and Satisfaction of Teams Working with Robots
Sangseok You, School of Information, University of Michigan, [email protected] Lionel Robert, School of Information, University of Michigan, [email protected]
Introduction
Teams are increasingly employing robots to accomplish various collaborative tasks. Construction teams use robots to bring down concrete walls. Robots used in first-responder teams help team members coordinate their actions and save human lives. In these teams, multiple team members use their own robots to accomplish a team task. Despite the increasing use robots in teams, however, the topic of promoting effectiveness of teamwork with robots has not been paid much attention.
Understanding collaboration through robots can be a challenge to IS scholars. While robots can be viewed as a technology like any other collaboration technologies that teams have adopted to accomplish their goals, robots can also be understood differently because of physical embodiment. Research shows that robot’s physical embodiment often manifests unique interactions and relationships with users, such as ascribing personality, intentions, and actions (Robert & You, 2015; Takayama, Groom, & Nass, 2009). These unique characteristics of robots warrant an investigation of interaction and outcomes within teams working with robots.
Trust, often defined as one’s belief to follow through on other’s behalf, has been an important construct in literature on teamwork and technology use (Mcknight, Carter, Thatcher, & Clay, 2011). Although previous research examined trust between team members and toward technologies, it is understudied the impact of trust in teams working with robots. Given that trust can exist both between team members and toward robots in teams working with robots, it is important to study whether team trust in robots and in humans have similar or different impacts on team outcomes.
In light of this, the goal of this study is two-fold. First, this paper seeks to investigate the impacts of team’s trust in robots and in humans on performance and satisfaction in teams working with robots. Also, this paper examines ways to promote team’s trust in robots and in humans in teams working with robots.
In this paper, we propose a research model (figure 1-a), in which team trust in robots and in humans are promoted by robot-building activity and team identification in teams working with robots. The research model includes interaction effects of robot-building and team identification on team trust in robots and in humans, respectively. We posit that team trust in robots and in humans will be the highest in teams with both robot-building and team identification. In addition, we propose that team trust in robots and in humans will result in increases in team performance and satisfaction with teamwork.
Figure 1 a) Research Model and b) experimental setting
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Method
To test the research model, we conducted a 2 (robot-building: building vs. no-building) × 2 (team identification: team identification vs. no team identification) between-subjects experiment in a controlled lab environment with 110 individual participants in 55 teams working with robots. Each team working with robots consisted of 2 humans who operated 2 robots. In the experiment, each team was asked to complete a team task using robots (see Figure 1-b for the experimental setting). The objective of the experimental task was to deliver five water bottles from one point to another point by controlling their robots as quickly as possible. Participants were told that additional incentives would be given to three best performing teams in the entire experiment.
Robot-building had two levels: building and no-building. In building condition, participants were asked to assemble a part of their robot using Lego bricks, whereas participants in no-building condition did not. Team identification had two levels, team identification and none, and was manipulated by creating a team name and making participants in the team identification condition wear a team uniform and put the small uniform in the same design to their robot.
Results
Results indicated that robot-building increased team trust in robot, but not team trust in humans. However, team identification increased team trust in humans, whereas had no impact on team trust in robots. We also found an interaction effect between robot-building and team identification: team trust in robots was the highest in teams with both robot-building and team identification. In addition, results also showed that team trust in robots and in humans yielded different impacts on team outcomes. Specifically, team trust in robots enhanced performance of teams working with robots, while team trust in humans did not. On the other hand, team trust in humans promoted team satisfaction, while team trust in robots did not have effects on team satisfaction.
Conclusion
This study examined ways to promote trust in robots and in human teammates and their impacts on performance and satisfaction in teams working with robots. Results of this study indicated that trust in robots and in humans were promoted by different mechanisms and yielded increases in performance and satisfaction separately. Taken together, findings of this study suggest that both relationships between humans and relationships between humans and their robots should be considered to better understand teamwork with robots.
References
Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems (TMIS), 2(2), 12.
Robert, L. P., & You, S. (2015). Subgroup Formation in Teams Working with Robots. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2097–2102). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2732791
Takayama, L., Groom, V., & Nass, C. (2009). I’m sorry, Dave: i’m afraid i won’t do that: social aspects of human-agent conflict. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2099–2108). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=1519021
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Being Useful: How Information Systems Professionals Influence the Use of Information Systems in Enterprises
Hadi Karimikia ([email protected]) Auckland University of Technology
Harminder Singh ([email protected]) Auckland University of Technology The interaction between IS and non-IS staff often expands beyond formal communication into informal interaction, with IS professionals engaging in discretionary, prosocial behaviors toward business, influencing users’ engagement with new systems. This interaction between business and IS professionals is not clearly addressed in prior post-adoption research. Little attention has been paid to explain how the voluntary behaviors or informal activities that IS professionals carry out affect the perceptions of users and encourage them to use information systems as fully as possible and in novel, improvised ways. Thus, this paper’s research question is: how does the informal interaction between IS and non-IS employees influence the inclination of non-IS employees to infuse information systems into their work practices? Figure 1 depicts the research framework tested in this study. Positive, discretionary behaviors carried out by IS professionals are hypothesized to positively affect perceptions among users about the usefulness (H1) and ease of use (H2) of systems.
H1: IS-Specific OCBs displayed by IS professionals are positively related to the perceived usefulness of information systems among employees, and H2: IS-Specific OCBs displayed by IS professionals are positively related to the perceived ease of use of information systems among employees.
The perceived level of usefulness and ease of use of a system influence the extent to which improvisation occurs (H3 & H4). Thus,
H3: The perceived usefulness of information systems is positively related to the extent of employees’ improvisational behaviors, and H4: The perceived ease of use of information systems is positively related to the extent of employees’ improvisational behaviors.
In such behavior models, users enhance their level of knowledge and skills on how to improvise with a difficult-to-use technology and in turn, the level of improvisation influences the likelihood of the new system being infused into an organization (H5). So,
H5: Employees’ improvisational behaviors are positively related to the extent to which new information systems are infused into work practices.
The fit statistics for the model show that /df is 1.535, which falls between 1 and 2, SRMR yields a value of 0.04, which is less than 0.08; the values of IFI, TLI and CFI are 0.945,0.940, and 0.945, respectively, which are close to 0.95, and RMSEA is 0.049, which is less than 0.05. These results reveal that the data is a reasonable fit with the model. In this study, improvisation is proposed as a determinant of IS infusion. The link between these two variables was extremely significant (p < 0.01) and highly correlated (β = 0.70). 55 percent of the variance in IS infusion can be explained by improvisation, supporting H5 and the proposition that accomplishing tasks more innovatively leads to deeper use of an information system among employees. The significant role of improvisation in supporting IS infusion could be because improvisation provides employees with opportunities to explore the various features of an information system in a range of conditions. For instance, while routine use will be suitable during normal operations, the ability to improvise is valuable when individuals have to use a system in an emergency situation or when they face a tight deadline. Both perceived usefulness and ease of use were found to be positively (β = 0.48 and 0.36 respectively) and significantly related (p < 0.01) to improvisation, supporting H3 and H4. Both perceived usefulness and ease of use contribute to 64 percent of the explained variance in improvisation, with perceived usefulness having a stronger than perceived ease of use. This study asserted that employees’ perception of the usefulness and ease of use of systems
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depends on the extent to which IS professionals assist them through informal interactions. Such support enables employees to display improvisational behaviors by utilizing their IT-related knowledge and better deploy their IS resources, and thus infuse a system into their work practices. Perceived usefulness and ease of use are the cognitive factors in our model. Importantly, the perception of an IS system’s usefulness and ease of use are shaped by the IS-specific behaviors exhibited by IS professionals when they interact with other users. This is because users observe how other users benefit from interacting with IS professionals, which motivate them to engage in such interaction to receive the same benefits. These cognitive processes enhance the likelihood of improvisation in two ways. First, individuals become more familiar with an IS, and find out other users they can ask if they face any problems. Access to a network of users makes it easier for individuals to decide to engage in improvisation because they can rely on alternative sources of knowledge to find out about different ways of carrying out their work tasks. Second, when individuals observe others using a system adeptly to achieve their goals, any uncertainty they may have over the system’s ability to be useful for their purposes is reduced. The relationship between IS-specific OCB and employees’ perceptions of system usefulness was highly significant (p < 0.01) and strongly positive (β = 0.70). The relationship between IS-specific OCB and perceived ease of use was also positive and significant relationship (β = 0.68, p < 0.01). IS-specific OCB explains approximately 55 percent of the variance in perceived usefulness and almost 45 percent for ease of use. These results support H1 and H2. Interacting with IS professionals makes employees more technically knowledgeable about their information system so that they become more aware of its usefulness and ease of use. This knowledge enables them to learn how to respond to unplanned-for situations, using knowledge and experiences obtained from past situations.
• Control Variables (Gender, Age, and Tenure) were simultaneously added to the model
with other variables • Significance levels: **P<0.01; *P<0.05 • Only significant correlations are indicated Figure 1. Research Model with Results
Tenure Gender Age
0.48**
0.36**
Perceived Usefulness
(55%) Improvisation
(64%) Infusion (55%)
IS-Specific OCB
Perceived Ease of Use (45%)
0.70**
0.68**
-0.149**
0.102*
-0.18**
0.70**
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Building a Model of the Information Search & Retrieval Processes of Industrial Software Practitioners Dr. Michael P. O’Brien & Dr. Jim Buckley
University of Limerick [email protected] | [email protected]
Motivation
Several authors have proposed information-seeking as an appropriate perspective for studying software maintenance activities. However, there is little research in the literature describing holistic information-seeking models in this context. Instead, researchers have concentrated on the related fields of software comprehension and software tool development. The work in software comprehension, while providing a cognitive basis for describing software maintenance activities, is abstract in nature and cannot provide strong guidance to those that aim to support software maintenance engineers. Consequently, the work on software tool development, which largely depends on this work, also suffers. The research that has been directed at programmers’ information seeking has focused on the types of information programmers seek rather than on their overall process of information seeking.
Research Agenda
The core of this research is to model the information seeking behaviour of industrial software developers, involved in [real] software maintenance activities. Initially it aims to derive a provisional information seeking model for programmers involved in maintenance, from information-seeking literature in other domains. Subsequently it aims to evaluate and refine that model, using in-vivo empirical data from maintenance programmers, as they maintain real-world software systems.
Method
An initial literature review targeting information seeking models in other domains was carried out, resulting in a preliminary information seeking model for programmers (O’Brien & Buckley, 2005). The in-vivo, think-aloud data generated from eight programmers as they maintained real-world systems was then gathered. An immersive analysis was carried out, naming and categorizing statements or utterances made by the programmers in an ‘open-coding’ protocol, where the research coder effectively created the categories from scratch. Over iterations stable categories began to emerge, pointing to different stages of the programmers’ information-seeking behaviour during software maintenance.
Results
This research generated an empirically-evaluated, information seeking process for programmers during software maintenance, which is largely consistent, but expands, Marchionini’s model of information seeking and is consistent with the concept location work of Rajlich and Wilde (2002). The refined ISM is presented in Fig. 1.
Conclusion
This research proposes an empirically evaluated Information-Seeking Model (ISM) for programmers. But the empirical data-set is small and needs buttressing before the results can be taken as definitive. With this framework the ‘islands’ of information-seeking research carried-out to date in this area, can be positioned, evaluated and possibly combined. This refined ISM provides a framework that attempts to conceptualise and thus provide a basis for support for the process.
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Reflect/Progress/INTERRUPT
END
Awareness ofProblem
Focus Formulation
Choose a searchsystem
Formulate query
Execute searchInformationCollection
Examine Results
Problem Solution
PROBLEM ORIENTED
SOLUTION ORIENTED
Information PromptedAction
Extract
External Representationsand Knowledge
Fig. 1 – Refined Model of Programmers’ Information-Seeking Behaviour
References
Ellis, D., & Haugan, M. (1997). Modeling the Information Seeking Patterns of Engineers & Research Scientists in an Industrial Environment. Journal of Documentation, 53(4), 384-403.
Kuhlthau, C. (1988). Developing a Model of the Library Search Process: Investigation of Cognitive and Affective Aspects. Reference Quarterly, 28(2), 232-242.
Marchionini, G. (1995). Information Seeking in Electronic Environments. Cambridge, England: Cambridge University Press.
O’Brien, M. P., Buckley, J. (2005). Modelling the Information-Seeking Behaviour of Programmers - An Empirical Approach. Proceedings of the 13th International Workshop on Program Comprehension, St. Louis, Missouri, USA, 125-134.
Rajlich, V., & Wilde, N. (2002). The Role of Concepts in Program Comprehension. Proceedings of the 10th IWPC, Paris, France, IEEE Computer Society.
Wilson, T. D. (1999). Models of Information Behaviour Research. Journal of Documentation, 55(3), 249-270.
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An Interdisciplinary Review of Crowdfunding Investment Decisions
Extended Abstract
Andreas Hoegen1 [email protected]
Dennis M. Steininger1
Daniel Veit1 [email protected]
1University of Augsburg
Universitätsstraße 16, 86135 Augsburg, Germany
While only about one decade old, the phenomenon of crowdfunding is quickly expanding. The idea to draw funds from an anonymous crowd via the internet started with small personal loans, financing culture or ventures in developing countries (Agrawal et al. 2011; Khavul 2010). Today, it provides increasing competition for traditional financing agents such as venture capitalists (VCs), business angels (BAs) and banks (Khavul et al. 2013; Steinberg 2012, p. 15). It also offers alternative means to finance individuals and entrepreneurs (Bruton et al. 2015). According to a recent industry study, the global volume of crowdfunding grew from less than 3 bn. USD in 2012 to over 34 bn. USD in 2015: more than 1,000% growth in only 3 years (Massolution 2015). Unlike in highly regulated traditional financing markets, entry barriers are low, and almost anyone with an internet connection can invest money on crowdfunding platforms (Bruton et al. 2015). But not everyone is qualified for this type of financial decision-making and amateur investments can quickly turn into expensive mistakes. Especially considering that human decision-making often seems irrational (Kahneman 2011; Thaler 2000).
Investor decision-making in traditional financing, such as providing startup capital or bank loans, is well researched. For crowdfunding, however, prior research focusses more on a multitude of different factors influencing the investment decision without providing an integrated view. Creating a more comprehensive picture of crowdfunding decision-making will help investors to make more informed choices and potentially avoid bad investments. In addition it can lead to new insights into how financial decision-making in general is changed by information systems. Therefore the research question we address is: Which factors influence investor decision-making in crowdfunding?
We address this question by conducting a systematic and interdisciplinary review on the extant crowdfunding literature, following Webster & Watson’s (2002) as well as Okoli and Schabram’s (2010) guides. Our final selection includes 67 articles that research crowdfunding decision-making. We extracted all factors that do or might influence crowdfunding investment decisions and inductively developed more abstract clusters of similar factors based on an continuously evolving concept matrix (Webster and Watson 2002). We allowed those clusters to freely emerge and further develop throughout our analysis instead of defining categories in advance (Glaser and Strauss 1967). We continuously refined the clusters to exhaustive cover all factors and ensure minimal overlap (Wolfswinkel et al. 2013). Based on the final clusters constructed a crowdfunding decision-making framework.
The framework allows to analyze the many influencing factors from the extant literature and structure our findings. It also enables future discussion and research to build on a more abstract basis, instead of handling a multitude of small factors. We describe the influence of each cluster and the associated factors on decision-making based on the findings and examples from the analyzed literature. Based on a meta-analysis of the extant qualitative research, we determine the relevance of each cluster for crowdfunding decisions.
For each of the four types of crowdfunding (equity-, reward-, donation-, and lending-based (Beaulieu et al. 2015)) we introduce how decisions are made in their corresponding type of traditional financing (venture capital, purchases/pre-ordering, donations, bank loans) and compare it to choice in
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crowdfunding. This comparison shows common systematic differences between all types of crowdfunding and traditional financing. Social capital, social dynamics and the context in which the decision takes place seem to be of higher importance in crowdfunding than in the respective traditional financial decisions. This is not surprising, as the concept of crowdfunding is built around information technology and the dynamics of social networks. In addition, crowdfunders often need to find means to substitute a lack of reliable financial information. But it can pay off: the “wisdom of the crowd” is frequently able to match the competence of professional financial decision-makers (Iyer et al. 2009; Mollick and Nanda 2015).
In addition to the systematic differences between decision-making in traditional finance and crowdfunding, two further findings emerge from our review. First, the reviewed articles show a very high methodological concentration. Most articles are built on secondary data from crowdfunding platforms. Many use quantitative methods and focus on the outcome of crowdfunding campaigns, instead of the decision-making process itself. Second, while many aspects of our framework are well covered in research, the influence of the context in which a decision takes place, as well as the impact of cognitive features and experience of the decision-maker have received scant attention.
Based on these findings, we will further investigate cognitive aspects of crowdfunding decisions. Our review shows that not only visible and obvious factors influence behavior in crowdfunding situations, but many subconscious factors steer investors’ actions. This coexistence of reflective and intuitive thinking has been long researched in cognitive and behavioral research (Kahneman 2011). For further studies, we will focus on how subconscious and contextual factors influence investor decisions in equity crowdfunding.
References
Beaulieu, T., Sarker, S., and Sarker, S. 2015. “A conceptual framework for understanding crowdfunding,” Communications of the Association for Information Systems (37:1), pp. 1–31.
Bruton, G., Khavul, S., Siegel, D., and Wright, M. 2015. “New Financial Alternatives in Seeding Entrepreneurship: Microfinance, Crowdfunding, and Peer-to-Peer Innovations,” Entrepreneurship Theory and Practice (39:1), pp. 9–26.
Glaser, B., and Strauss, A. 1967. “The discovery of grounded theory,” London: Weidenfeld and Nicholson (24:25), pp. 288–304.
Iyer, R., Khwaja, A. I., Luttmer, E. F. P., and Shue, K. 2009. Screening in new credit markets: can individual lenders infer borrower creditworthiness in peer-to-peer lending?
Kahneman, D. 2011. Thinking, fast and slow, Macmillan. Mollick, E. R., and Nanda, R. 2015. “Wisdom or Madness? Comparing Crowds with Expert Evaluation in
Funding the Arts,” Management Science. Okoli, C., and Schabram, K. 2010. “A Guide to Conducting a Systematic Literature Review of Information
Systems Research,” (10:26). Thaler, R. H. 2000. “From Homo Economicus to Homo Sapiens,” The Journal of Economic Perspectives
(14:1), pp. 133–141. Webster, J., and Watson, R. T. 2002. “Analyzing the Past to Prepare for the Future: Writing a Literature
Review,” MIS Quarterly (26:2), pp. xiii–xxiii. Wolfswinkel, J. F., Furtmueller, E., and Wilderom, C. P. 2013. “Using grounded theory as a method for
rigorously reviewing literature,” European Journal of Information Systems (22:1), pp. 45–55.
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The Measurement and Implications of Cognitive Fit in Visualizing Big Data
International Conference on Information Systems 2016, Dublin, Ireland SIG IS-CORE: Information Systems – Cognitive Research Exchange Workshop
Sunday, December 11, 2016
Emre Yetgin, Ph.D. ([email protected]) Assistant Professor of Information Systems
Department of Information Systems and Global Supply Chain Management College of Business Administration
Rider University
In today’s business world, organizations must gather and analyze big data effectively, to create and maintain a competitive level of business advantage. Yet, the growth in the volume and variety of information contained in a typical big dataset have made it increasingly more difficult to make sense of the dataset’s contents and their implications. Visualization (i.e., representing data visually) has long been an aid in aggregating otherwise incomprehensible information and presenting it in a way that can provide business insights that are difficult, if not impossible, to obtain through conventional means (e.g., lists and summarizing statistics provided in simple tables or graphs). Nevertheless, despite its potential importance to big data analysis and competitive advantage, the opportunities and challenges visualization presents in today’s big data context remain mostly uninvestigated. Building on this gap, this study is the first one to explore the nature and consequences of cognitive fit in visualizing big data.
Cognitive fit, in this context, refers to the compatibility between the information requirements for a particular data analysis task and the information representation provided by a certain type of visualization. Accordingly, the focus of this study is on the interplay between different types of common data analysis tasks (i.e., information retrieval, information comparison, or information integration) and visualization methods (i.e., discrete vs. continuous and singular vs. multiple), plus how the defining characteristics of big data (i.e., the volume and variety of the information being visualized) moderate the consequences of this interplay regarding data analysis performance (i.e., decision speed and accuracy). For measuring cognitive fit and examining its consequences, this study utilizes an eye tracker to capture objectively the way in which the participants extract information from given visualizations, plus their efficiency in doing so.
This study contributes to the literature in at least two major avenues. First, this study improves our overall understanding of cognitive fit and how it manifests in data analysts’ problem solving behaviors when using different types of visualization tools. This is done by collecting eye tracker data
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during a laboratory experiment, and analyzing participants’ eye movement, gaze duration, and gaze fixation patterns while they work with different types of data analysis tasks and visualization methods. Based on this analysis, this study proposes an objective method for measuring and assessing cognitive fit, independent of participants’ recall and self-reporting biases. This constitutes both a methodological and a theoretical contribution to the literature, as our prior understanding of cognitive fit has been limited due to our inability to measure and assess it objectively.
Second, this study highlights the relative importance of cognitive fit in a big data context, and informs the choice of visualization tools among an ever-increasing number of alternatives for supporting the complex problems faced today by big data analysts. The examination of cognitive fit through different types of visualizations also offers insights that can lead to the development of new visualization tools that can accommodate different task and data characteristics. Overall, this study provides a deeper understanding of the role visualization plays in facilitating human cognition in the context of data analytics, which is critical to the successful design and implementation of future big data analytics and visualization technologies.
