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An Activity Theory Approach to Leak Detection and Mitigation in Patient Health Information (PHI)
Rohit Valecha, Shambhu Upadhyaya, and H. Raghav Rao
Abstract
The migration to Electronic Health Records (EHR) in the healthcare industry has raised issues with respect to security and privacy. One such issue that has become a concern for the healthcare providers, insurance companies and pharmacies are Patient Health Information (PHI) leaks. PHI leaks lead to violation of privacy laws, which protect the privacy of individual’s identifiable health information. Proper access control is essential in mitigating the risk of leakage with PHI. This study explores the issue of PHI leaks from an access control viewpoint. We utilize access control policies and PHI leak scenarios derived from semi-structured interviews with four healthcare practitioners and use the lens of Activity Theory to articulate the design of an access control model for detecting and mitigating PHI leaks. Subsequently we follow up with a prototype as a proof of concept.
Texting with Human-like Conversational Agents:
Designing for Anthropomorphism
Anna-Maria Seeger, Jella Pfeiffer, and Armin Heinzl
Abstract
Conversational agents (CAs) are natural language user interfaces that emulate human-to-human communication. Because of this emulation, research into CAs is inseparably linked to questions about anthropomorphism—the attribution of human qualities, including conscious-ness, intentions, and emotions, to nonhuman agents. Past research has demonstrated that anthropomorphism affects human perception and behavior in human-computer interactions, for example by increasing trust and connectedness or stimulating social response behavior. Based on the psychological theory of anthropomorphism and related research on computer interface design, we develop a theoretical framework for designing anthropomorphic CAs. We identify three groups of factors to stimulate anthropomorphism: technology design-related, task-related, and individual factors. Our findings from an online-experiment support the derived framework but also reveal novel, yet counterintuitive, insights. In particular, we demonstrate that not all combinations of anthropomorphic technology design cues increase perceived anthropomorphism. For example, we find that using only nonverbal cues harms anthropomorphism; however, this effect turns positive when nonverbal cues are comple-mented with verbal or human identity cues. We also find that whether CAs complete computer-like or human-like tasks and individuals’ disposition to anthropomorphize greatly affect perceived anthropomorphism. This work advances our understanding of anthropomorphism and makes the theory of anthropomorphism applicable to our discipline. We advise on the direction research and practice should take to find the right spot in anthropomorphic CA design.
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How Big Data Analytics Affects Supply Chain Decision-Making: An Empirical Analysis
Daniel Q. Chen, David S. Preston, and Morgan Swink
Abstract
This study investigates how different types of “big data analytics” (BDA) usage influence organizational decision-making in the area of supply chain management (SCM). Drawing on decision-making theory and organizational information processing theory, we conceptualize two patterns of BDA usage for supply chain (SC) activities (BDA use for SC optimization and BDA use for SC learning) and report two complementary channels via which the two BDA usage patterns impact a supply chain organization’s BDA-enabled decision-making capability. An analysis of questionnaire data from supply chain managers representing 157 North American-based companies suggests that BDA use for SC optimization is directly associated with better decision-making capability. In contrast, the influence of BDA use for SC learning does not impact decision making directly but indirectly as its effect is fully mediated by organizational integration. We discuss the implications of these findings for future academic research and for managers in practice who seek to maximize business values from BDA implementations.
Reconsidering the Role of Research Method Guidelines
for Interpretive, Mixed Methods, and Design Science Research
Mikko Siponen, Wael Soliman, and Philipp Holtkamp
Abstract
Information systems (IS) scholars have proposed guidelines for interpretive, mixed methods, and design science research in IS. Because many of these guidelines are also suggested for evaluating what good or rigorous research is, they may be used as a checklist in the review process. In this paper, we raise the question to what extent do research guidelines for interpretive, mixed methods, and design science research offer such evidence that they can be used to evaluate the quality of research. We argue that scholars can use these guidelines to evaluate what good research is if there is compelling evidence that they lead to certain good research outcomes. We use three well-known guidelines as examples and contend that they seem not to offer evidence such that we can use them to evaluate the quality of research. Instead, the “evidence” is often an authority argument, popularity, or examples demonstrating the applicability of the guidelines. If many research method principles we regard as authoritative in IS are largely based on speculation and opinion, we should take these guidelines less seriously in evaluating the quality of research. Our proposal does not render the guidelines useless. If the guidelines cannot offer cause-and-effect evidence for the usefulness of their principles, we propose seeing the guidelines as idealizations for pedagogical purposes, which means that reviewers cannot use these guidelines as checklists to evaluate what good research is. While our examples are from interpretive, mixed methods, and design science research, we urge the IS community to ponder to what extent other research method guidelines offer such evidence that they can be used to evaluate the quality of research.
Social Networking Site Use Resumption: A Model of Return Migration
Christian Maier, Sven Laumer, Jason Bennett Thatcher, Heshan Sun, Christoph Weinert, and Tim Weitzel
Abstract
This research explains why individuals resume using social networking sites (SNSs) after quitting using them. Drawing on return migration theory, we developed a theory-driven model of SNS resumption, which includes two novel antecedents of SNS resumption behavior: non-use-related dissatisfaction and use-related satisfaction. We also hypothesize that dispositional resistance to change moderates the impact of non-use-related dissatisfaction and use-related satisfaction on resumption. We used a mixed-method approach to refine and evaluate the research model. Study 1 uses the critical incident method to identify SNS specific antecedents of non-use-related satisfaction and use-related satisfaction, allowing us to refine the research model. Study 2 uses structural equation modelling to evaluate our research model using two three-wave surveys: one with recent ex-users who recently decided to stop using and to delete their profile on Facebook and one with long-standing ex-users who had stopped using and deleted their profile on Facebook a long time ago. We found support for most relationships in our model: non-use-related dissatisfaction and use-related satisfaction drive resumption intentions and dispositional resistance moderates these relationships. Furthermore, we found that the time elapsed since users discontinued Facebook moderated these relationships such that the effect of non-use-related dissatisfaction on resumption intention is stronger for recent ex-users and the one of use-related satisfaction stronger for long-standing ex-users. Our findings advance understanding of resumption, an understudied behavior of the IT lifecycle and IT use and acceptance research.
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Understanding Characteristics of Popular Streamers on Live Streaming Platforms: Evidence from Twitch.tv
Keran Zhao, Yuheng Hu, Yili Hong, and Christopher Westland
Abstract
Live streaming platforms such as Twitch and Periscope have become some of the most popular synchronous social networking services. In order to attract viewers, streamers are motivated to broadcast exciting video content while actively interacting with viewers. The emerging stream of research on the live streaming community has examined the streamers’ motivations and how the viewers react to streamers. However, few studies have focused on understanding the characteristics of popular streamers. Popular streamers create tremendous business value for social media influencer investors, as they have high potential to create persuasive advertisements and endorsements for firms by promoting their products and services. We aim at examining the key characteristics that associate with the streamers’ viewer base, namely their personality, professionalism, and streaming affordance. Based on text mining and analyses of video content, our results show: 1) certain personality traits (such as openness) are negatively associated with both cumulative and current popularity; 2) professional players are more likely to attract a larger viewer base; 3) and the social affordance, including profile building affordance, social connectivity, and social interactivity, is positively associated with both cumulative and current popularity. Our results provide useful insights into measuring and evaluating streamers’ popularity, which in turn generates actionable strategies for social media influencer investors and platform operators.
The Chief Information Officer: Impact on Organizational Forecasting Outcomes
Xiaotao (Kelvin) Liu, David S. Preston, and John (Jianqiu) Bai
Abstract
Management earnings forecasts are essential sources of information for organizational shareholders. However, many companies remain in a quandary about how to develop an appropriate governance structure within top management through which quality forecasts can best be derived. This study investigates how firms that employ a Chief Information Officer (CIO) within its ranks impact organizational outcomes as reflected in both the frequency and bias of management earnings forecasts. For the theoretical basis, we integrate the following theories to formulate our hypotheses: upper echelons theory, agency theory, and information processing theory (in conjunction with strategic management literature). Using a sample of firm-years (2000 to 2010), we find robust support for the proposition that the firms with CIOs are associated with reduced opportunistic bias in earnings forecasts. In addition, we find that as information uncertainty increases, firms with CIO generate management forecasts less frequently, yet also with a reduction in optimistic forecasting bias. Collectively, these findings provide a theory-based understanding of how firms with CIOs can influence forecasting outcomes while also providing guidance to practice.
Comparing Three Theories of the Gender Gap in Information Technology Careers: The Role of Salience Differences
JKevin A. Harmon and Eric A. Walden
Abstract
The information technology (IT) field faces a skills shortage. Only 17 percent of a projected 3.5 million computing job openings are expected to be filled by 2026 (National Association for Women & Information Technology, 2018). Yet, the number of women pursuing IT careers continues to decrease—only 19 percent of IT bachelor’s degrees in 2016 were awarded to women compared to 57 percent of overall bachelor’s degrees. We compared three theories that could explain this gender gap in IT career pursuit: expectancy-value theory, role congruity theory, and field-specific ability beliefs theory. We find that women and men are similar in their levels of important factors related to career interest, but that two of these—technical learning self-efficacy and agentic goals—hold increased salience for women. This suggests that some of the gender gap in the IT field could be addressed by placing more focus on developing technical learning self-efficacy in both men and women. This could help both women and men but could have an outsized effect on the IT career pursuit of women.
When IT Evolves Beyond Community Needs: Coevolution of Bottom-Up IT Innovation and Communities
Aljona Zorina and Stan Karanasios
Abstract
This paper examines how innovative uses of IT artifacts and their repurposing to fulfill emerging or unsatisfied user needs (bottom-up innovation, BUI) develop in community settings. Based on a longitudinal analysis of “HomeNets,” communities that have developed residential internet access in Belarus over a 20-year period, we illustrate that the development of community BUI is driven not only by the needs of the innovating members. Instead, community BUI development emerges from the interplay between the innovating members’ community context and technology, as well as from the interplay between the BUI technology and context. We demonstrate how these dynamics trigger community BUI development that goes beyond the needs and expectations of the innovating actors and impacts community evolution and long-term survival. Based on our findings, we develop a model of community BUI development. We discuss the theoretical implications of our findings, highlighting the role of technology and context in community BUI and its processual unfolding beyond the needs and intensions of the innovating members.
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A Dual-Identity Perspective of Obsessive Online Social Gaming
Xiang Gong, Christy M.K. Cheung, Kem Z.K. Zhang, Chongyang Chen, and Matthew K.O. Lee
Abstract
Obsessive online social gaming has become a worldwide societal challenge that deserves more scholarly investigation. However, this issue has not received much attention in the information systems (IS) research community. Guided by dual-system theory, we theoretically derive a typology of obsessive technology use and contextually adapt it to conceptualize obsessive online social gaming. We also build upon identity theory to develop a dual-identity perspective (i.e., IT identity and social identity) of obsessive online social gaming. We test our research model using a longitudinal survey of 627 online social game users. Our results demonstrate that the typology of obsessive technology use comprises four interrelated types: impulsive use, compulsive use, excessive use, and addictive use. IT identity positively affects the four obsessive online social gaming archetypes and fully mediates the effect of social identity on obsessive online social gaming. The results also show that IT identity is predicted by embeddedness, self-efficacy, and instant gratification, whereas social identity is determined by group similarity, group familiarity, and intragroup communication. Our study contributes to the IS literature by proposing a typology of obsessive technology use, incorporating identity theory to provide a contextualized explanation of obsessive online social gaming and offering implications for addressing the societal challenge.
Finding A Needle In The Haystack - Recommending Online Communities On Social Media Platforms Using Network and Design Science
Srikar Velichety and Sudha Ram
Abstract
We address the problem of recommending online communities on social media platforms using design science. Our method is grounded in network science and leverages the random surfer model of the web, small world networks, strength of weak connections and connectivity to analyze three types of large-scale networks. In doing so, we design features for structural hole assortativity and Local Clustering Coefficient rank to capture both the diversity and evolution of user interests. We also extract general online community features such as sizes and overlaps. Experiments conducted on a large dataset of 34,000 lists created and subscribed by 1600 active Twitter users over a six-month period show that our network features outperform the general and content features in terms of recommending communities at the top position. In addition, a combination of general and network features generated the best results in the top position with a significant performance improvement over using only the content features. A combination of all the three types of features gives best results in the top 5 and 10 positions while improving the quality of recommendations at every other position. Finally, our work also outperforms the latest work on community recommendation in social media platforms and has major implications for the design of online community recommenders.
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Too Engaged to Contribute? An Exploration of Antecedents and Dual Consequences of Newcomer Engagement in Online Social Groups
Hsien-Tung Tsai and Peiyu Pai
Abstract
Both scholars and practitioners recognize the importance of newcomers’ contribution behavior in online social groups. However, extant research has largely focused on existing members’ behavior, leaving the issue of newcomers’ contribution behavior relatively unexplored. This research proposes a theoretical framework for understanding why newcomers engage in contribution processes, how group engagement leads to normative pressure, and whether normative pressure has curvilinear effects on information contributions. Drawing on the theory of engagement, we propose that newcomer group engagement, characterized by vigor, dedication, and absorption, exerts dual effects on information contributions. We also argue that newcomers who personally engage in contribution processes tend to reveal three key psychological conditions: meaningfulness (i.e., a sense of return on investments of the self in contribution processes), safety (i.e., a sense of being able to show and express oneself without fear of negative consequences), and availability (i.e., a sense of readiness to engage personally in contribution processes). We further investigate a focal antecedent for each psychological condition. Using multisource data collected at three points in time, this research finds that value congruence, perceived group support, and contribution self-efficacy positively influence newcomer group engagement, which in turn leads to greater information contribution behaviors. This study also shows that greater group engagement can initiate a spiral of social role expectations, leading to heightened levels of normative pressure. Moreover, normative pressure has an inverted U-shaped relationship with information contribution behavior. These findings offer both theoretical and practical implications.
The "Mechanics" of Enterprise Architecture Principles
Kazem Haki and Christine Legner
Abstract
Inspired by the city planning metaphor, enterprise architecture (EA) has gained considerable attention from academia and industry for systematically planning an IT landscape. Since EA is a relatively young discipline, a great deal of its work focuses on architecture representations (descriptive EA) that conceptualize the different architecture layers, their components, and relationships. Beside architecture representations, EA should comprise principles that guide architecture design and evolution toward predefined value and outcomes (prescriptive EA). However, research on EA principles is still very limited. Notwithstanding the increasing consensus regarding EA principles’ role and definition, the limited publications neither discuss what can be considered suitable principles, nor explain how they can be turned into effective means to achieve expected EA outcomes.
This study seeks to strengthen EA’s extant theoretical core by investigating EA principles through a mixed methods research design comprising a literature review, an expert study, and three case studies. The first contribution of this study is that it sheds light on the ambiguous interpretation of EA principles in extant research by ontologically distinguishing between principles and nonprinciples, as well as deriving a set of suitable EA (meta-)principles. The second contribution connects the nascent academic discourse on EA principles to studies on EA value and outcomes. This study conceptualizes the “mechanics” of EA principles as a value-creation process, where EA principles shape the architecture design and guide its evolution and thereby realize EA outcomes. Consequently, this study brings EA’s underserved, prescriptive aspect to the fore and helps enrich its theoretical foundations.
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This study seeks to strengthen EA’s extant theoretical core by investigating EA principles through a mixed methods research design comprising a literature review, an expert study, and three case studies. The first contribution of this study is that it sheds light on the ambiguous interpretation of EA principles in extant research by ontologically distinguishing between principles and nonprinciples, as well as deriving a set of suitable EA (meta-)principles. The second contribution connects the nascent academic discourse on EA principles to studies on EA value and outcomes. This study conceptualizes the “mechanics” of EA principles as a value-creation process, where EA principles shape the architecture design and guide its evolution and thereby realize EA outcomes. Consequently, this study brings EA’s underserved, prescriptive aspect to the fore and helps enrich its theoretical foundations.
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Improving the Design of Information Security Messages by Leveraging the Effects of Temporal Distance and Argument Nature
Sebastian W. Schuetz, Paul Benjamin Lowry, Daniel A. Pienta, and Jason Bennett Thatcher
Abstract
A substantial amount of previous research has examined the efficacy of fear appeals to elicit security-enhancing behaviors from users. However, despite more than a decade of research on fear appeals in security contexts, researchers have yet to understand which factors drive users’ responses to fear appeals. Instead, the literature is riddled with inconsistent findings on the antecedents that predict fear-appeal outcomes, fueling controversy over, and inhibiting progress, on the problem. This research addresses the inconsistent findings by using construal level theory (CLT) to explain how temporal distance and argument nature affect fear-appeal appraisal. Based on two online experiments, we report evidence that temporal distance determines which antecedents drive fear-appeal outcomes, which helps explain inconsistent results found in prior literature. Moreover, we found that depending on the temporal distance condition, argument nature (i.e., how or why arguments) can affect the effectiveness of fear appeals. Overall, our findings refine understanding of when certain factors influence users’ responses to fear-appeals and provide guidance for future research on how to create more effective fear appeals.
Thinking Technology as Human: Affordances, Technology Features, and Egocentric Biases in Technology Anthropomorphism
Jianqing (Frank) Zheng and Sirkka L. Jarvenpaa
Abstract
Advanced information technologies (ITs) are increasingly taking on tasks that have previously required human capabilities, such as learning and judgment. What drives this technology anthropomorphism (TA), or the attribution of human-like characteristics to IT? What is it about users, IT, and their interaction that influences the extent to which people think of technology as human-like? While TA can have positive effects, such as increasing user trust in technology, what are the negative consequences of TA? To provide a framework for addressing these questions, we advance a theory of TA that integrates the general three-factor anthropomorphism theory in social and cognitive psychology with the Need-Affordance-Features (NAF) perspective from the information systems (IS) literature. The theory we construct helps to explain and predict which technological features and affordances are likely: 1) to satisfy users’ psychological needs, and 2) to lead to TA. More importantly, we problematize some negative consequences of TA. Technology features and affordances contributing to TA can intensify users’ anchoring with their elicited agent knowledge and psychological needs, and also can weaken the adjustment process in TA under cognitive load. The intensified anchoring and weakened adjustment processes increase egocentric biases that lead to negative consequences. Finally, we propose a research agenda for TA and egocentric biases.
When are Social Network Site Connections with Coworkers Beneficial? The Roles of Age Difference and Preferences for Segmentation between Work and Life
Ariane Ollier-Malaterre, and Annie Foucreault
Abstract
Individuals are increasingly connected with their coworkers on social network sites (SNS) that are personal and professional (e.g., Facebook), with consequences for workplace relationships. Drawing on SNS research and on social identity and boundary management theory, we surveyed 202 employees and found that coworkers’ friendship acts (e.g., liking, commenting) were positively associated with closeness to coworkers when coworkers were similar in age to or older than the respondent, as well as with organizational citizenship behaviors towards coworkers (OCBI) when coworkers were similar in age. Conversely, harmful behaviors from coworkers (e.g., disparaging comments) were negatively associated with closeness when coworkers were older than the respondent, and with OCBI when coworkers were older than the respondent and coworkers’ friendship acts were high. Preferences for segmentation between work and life moderated the relationship between coworkers’ friendship acts and OCBI (but not closeness) such that the positive relationship was stronger when the respondent had low (vs. high) preferences for segmentation. We discuss the theoretical and practical implications of this study and propose an agenda for future research.
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Impact of Social Media on the Firm’s Knowledge Exploration and Knowledge Exploitation: The Role of Business Analytics Talent
Ana Castillo, Jose Benitez, Javier Llorens, and Jessica Braojos
Abstract
Social media are one of the most disruptive technologies in executing the firm’s digital business transformation strategies. Does the firm’s ability to use social media affect its proficiency in exploring and exploiting knowledge? What could be the role of business analytics talent in this equation? We study theoretically and empirically these cutting-edge research questions. Our proposed research model argues that social media capability enables the development of knowledge exploration and knowledge exploitation, and business analytics talent exerts a positive reinforcing role in the impact of social media on knowledge exploration. We tested the proposed research model with a secondary dataset from a sample of U.S. firms. The proposed research model was empirically tested using PLS path modeling. After running a robustness test by estimating eight alternatives/competing models, the empirical analysis shows that social media capability is positively related to knowledge exploration and knowledge exploitation, but with a stronger effect on knowledge exploration. Moreover, business analytics talent plays a positive moderator role in the relationship between social media capability and knowledge exploration. This study contributes to the IS research by: 1) introducing, developing, and operationalizing the concepts of social media capability and business analytics talent; and 2) theoretically arguing and empirically showing the pivotal role of social media capability in exploring new knowledge and the complementary role of business analytics talent. Our study also provides several critical lessons learned for top executives and proposes promising future IS research avenues.
Reach Out and Touch: Eliciting Sense of Touch Through Gesture-Based Interaction
Yang (Alison) Liu, Yi Shen, Cheng Luo, and Hock Chuan Chan
Abstract
With the development of gesture-based interaction technologies (e.g., touchscreen devices and kinetic controllers), consumers can directly use their hands to interact with web interfaces, which may create a sense of touch for consumers. Drawing on feelings-as-information theory, this study investigates the impacts of two types of gesture-based interaction (i.e., touchscreen interaction and mid-air interaction) on consumers’ sense of touch. Results from a laboratory experiment showed that touchscreen interaction elicited a higher sense of touch than mid-air interaction when the importance of product haptic information was high. However, touchscreen interaction did not differ from mid-air interaction in terms of eliciting consumers’ sense of touch when the importance of product haptic information was low. Furthermore, consumers’ sense of touch improved their shopping experience satisfaction by reducing uncertainty about products and fostering attachment to products. Theoretically, this study contributes to the existing literature by empirically investigating the effects of gesture-based interaction on consumers’ bodily sensation, elucidating the role of sense of touch in affecting consumers’ virtual product experience, and highlighting the impacts of interaction method on consumer behavior. This study also provides practical insights into the application of gesture-based interaction technologies.
The Quest for Innovation in Information Systems Research:
Recognizing, Stimulating, and Promoting Novel and Useful Knowledge
Varun Grover and Fred Niederman
Abstract
Research in Information Systems (IS) is often challenged in the review process with the “what’s new” and the “so what” questions. While we believe that there is innovation in IS research, constituents in the field do not have a good, or at least a consistent understanding of what this entails. This creates a problem for editors, authors, and reviewers in assessing how innovative a study is, or what aspects of the work are indeed innovative. This paper is a response to this concern as we take on the challenging task of recognizing innovation in IS research. At the most basic level we offer a structure that examines a variety of ways that innovation may be manifested in our research output. We describe, illustrate, and discuss the challenges of using our categories of innovative research. We hope that such identification can stimulate and expand our capacity to generate innovative research and to recognize (and promote) it when it is forthcoming.
The Building Blocks of Software Platforms:
Understanding the Past to Forge the Future
He Li and William J. Kettinger
Abstract
This study takes a Review and Theory Development (RTD) approach to synthesize the software platform literature, offering theoretical perspective and research guidance. In doing so, we conceptualize platform and complementary capabilities for software platform owners and complementors. The review indicates that three dimensions reflect platform capabilities: intermediarity, generativity, and ambidexterity, while complementary capabilities include creativity, interconnectivity, and appropriability dimensions. We derive an integrative framework of software platforms, which explains (1) how software platform owners and complementors improve performance by enhancing their capabilities; and (2) how software platform owners, complementors, and the ecosystem environment co-evolve. We conclude with a discussion of how future research can build on and enrich the research framework.
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A Representation Theory Perspective on the Repurposing of Personal Technologies for Work-Related Tasks
James Burleson, Varun Grover, Jason Bennett Thatcher, and Heshan Sun
Abstract
Individuals often blur the line between technologies used for personal means and those used to complete work-related tasks. The increasing level of capabilities offered by personal technologies presents opportunities for repurposing them to accomplish work-related tasks. With guidance from representation theory, we describe how cross-context representational fidelity predicts repurposing intentions across domains of use (e.g., personal to work-related). An empirical study of 311 full-time employees demonstrates that congruence between prior technology use and potential work use increases an individual’s belief that a technology can be useful for work purposes. Furthermore, we show that, in repurposing situations, usefulness is also influenced by an individual’s confidence in using the technology on his or her work device(s). These findings, among others, shed new light on our understanding of the influence of experience on repurposing technologies for use in the professional domain.
From Other Worlds: Speculative Engagement Through Digital Geographies
Dirk S. Hovorka and Sandra Peter
Abstract
Our ability to predict, explain, or control sociotechnical realities is called into question by unprecedented phenomena in surveillance, in markets, and in other social and political domains. The apparatus of research - our current categories, instruments, arguments, and epistemic choices, rely on what is empirically accessible – on the past. Our research orientation to the future assumes continuity and the extension of past patterns into a predictable and thus manageable future. In this research we propose speculative engagement through digital geographies to make visible the processes of technological and cultural reconfiguration which result in unprecedented change. After describing the conception of ‘the future’ in widely used research methods, we describe speculative engagement as a research orientation to disclose new categories, relationships and values and a commitment to the performative relationships of our current research practices with potential future(s). Digital geographies are internally consistent and coherent worlds that are cognitively plausible but estranging. They are carriers of meaning and culture that underpin a broad class of methods to provide richly experienced ‘other worlds’. We posit principles for effective digital geographies and provide an illustrative example of a digital human artifact which estranges us from current assumptions. Finally, we argue that our approach enables researchers to engage with the future on its own terms. In this way researchers, designers and policy-makers can open current practices to new categories, relationships, logics and values and make visible the unprecedented reconfigurations in which our research is implicated.
Challenge and Hindrance IS Use Stressors and Appraisals: Explaining Contrarian Associations in Post-acceptance IS Use Behavior
Christian Maier, Sven Laumer, Monideepa Tarafdar, Jens Mattke, Lea Reis, and Tim Weitzel
Abstract
Post-acceptance IS use is the key to leveraging value from IS investments. However, it also poses many demands on the user. Drawing on the challenge-hindrance stressor framework, this study develops a theory to explain how and why IS use stressors influence post-acceptance use. We identify two different types of IS use stressors: challenge IS use stressors and hindrance IS use stressors. We hypothesize that they are appraised through challenge IS use appraisal and hindrance IS use appraisal respectively, through which they influence routine use and innovative use. We evaluate our hypotheses by surveying 178 users working in one organization and analyze the data collected using consistent partial least square (PLSc). We find that challenge IS use stressors positively influence routine use and innovative use via challenge IS use appraisal. Hindrance IS use stressors negatively influence routine use via hindrance IS use appraisal. We then dive deeper into these findings using a two-step fuzzy set qualitative comparative analysis (fsQCA), identifying the presence of challenge IS use stressors and challenge IS use appraisal as necessary conditions for high innovative use. We also reveal that the presence of hindrance IS use stressors and hindrance IS use appraisal only influences routine use and innovative use in the absence of challenge IS use stressors and challenge IS use appraisal. We discuss the practical relevance and transferability of our findings based on a comprehensive applicability check. Our findings advance IS scholarship of IS use stress and post-acceptance use by showing how routine use and innovative use emanate from IS use stressors.
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A Strategic Group Analysis of Competitor Behavior
in Search Advertising
Cheng Nie, Zhiqiang (Eric) Zheng, and Sumit Sarkar
Abstract
Firms compete intensely in sponsored search. Their bidding strategies hinge on understanding who competes with whom, how they compete, and how consumers react to competing advertisements. In this context, we investigate how firm competition impacts consumers’ click-through behaviors in search advertising from a strategic group perspective. Using search results from Google and consumers’ clickstream data, we find strong negative externality for competitors within the same strategic group relative to competitors across strategic groups: firms reap fewer click-throughs when an advertisement of another firm from the same strategic group is also displayed in search results, relative to when other displayed advertisers are not from the same group. This indicates that when competitors from the same strategic group are likely to appear in the result of a sponsored search auction, the focal firm would be better off avoiding head-to-head competition in the auction. However, we do not find empirical evidence of such behaviors of firms, suggesting myopia or inability of firms to avoid such competition. We also show that when multiple firms from the same strategic group appear in a search result, the closer the focal firm is located with such competing firms, the more click-throughs the firm accrues. This suggests that firms should stay close to their within-group competitors when they compete in the same search auction. Further, our empirical results indicate that firms are indeed doing so. Using another set of data from Google AdWords reports, we are able to show that our findings are robust to multi-keyword bidding scenarios as well. These findings represent the first attempt to understand the impact of strategic groups in search advertising, and provide interesting implications for advertisers and search engines.
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Mining Online Social Networks: Deriving User Preferences Through Node Embedding
Mahyar Sharif Vaghefi and Derek L. Nazareth
Abstract
In the last decade, online social networks have become an integral part of life. These networks play an important role in the dissemination of news, individual communication, disclosure of information, and business operations. Understanding the structure and implications of these networks is of great interest to both academia and industry. However, the unstructured nature of the graphs and the complexity of existing network analysis methods limit the effective analysis of these networks, particularly on a large scale. In this research, we propose a simple but effective node embedding method for the analysis of graphs with a focus on its application to online social networks. Our proposed method not only quantifies social graphs in a structured format, but also enables the user preference identification, community detection, and link prediction in online social networks. We demonstrate the effectiveness of our approach using a network of Twitter users. Results of this research provide valuable insights for marketing professionals seeking to target personalized content and advertising to individual users, as well as social network administrators seeking to improve their platform through recommender systems as well as detection of outliers and anomalies.
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A Motivation-Hygiene Model of Open Source Software Code Contribution and Growth
Pratyush Nidhi Sharma, Sherae L. Daniel, Tingting (Rachel) Chung, and Varun Grover
Abstract
The success of Open Source Software (OSS) projects depends on sustained contributions by
developers who often display a wide variety of contribution patterns. Project leaders and stakeholders would strongly prefer developers to not only maintain – but preferably increase – their contributions over time as they gain experience. Corporations increasingly complement OSS developer motivations (such as fit in terms of shared values with the project community) by paying them to sustain contributions. However, practitioners argue whether payment helps or hurts projects because imbursement may dampen developer motivation in the long run. This may make it difficult for project leaders to understand what to expect from developers over time.
Using Herzberg’s motivation-hygiene framework, we explore how developers’ perceptions of value fit with the project and being paid interact to determine the level of code contribution and its rate of change over time (i.e., growth). Using a survey of 564 developers across 431 projects
on GitHub, we build a three-level growth model explaining the code contribution and its growth over a six-month period. We find that value fit with the project positively influences both the level and growth of code contribution. However, there are notable differences among paid and
unpaid developers in the impact of value fit on their level and growth in code contributions over time. The implications of our work will be of interest to researchers, practitioners, and organizations investing in open source projects.
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The Design of a System for Online Psychosocial Care: Balancing Privacy and Accountability in Sensitive Online Healthcare Environments
Jonas Sjöstrom, Pär Ågerfalk, and Alan Hevner
Abstract
The design of sensitive online healthcare systems must balance the requirements of privacy and accountability for the good of individuals, organizations, and society. Via a design science research approach, we build and evaluate a sophisticated software system for the online provision of psychosocial healthcare to distributed and vulnerable populations. Multi-disciplinary research capabilities are embedded within the system to investigate the effectiveness of online treatment protocols. Throughout the development cycles of the system, we build an emergent design theory of scrutiny that applies a multi-layer protocol to support governance of privacy and accountability in sensitive online applications. The design goal is to balance stakeholder privacy protections with the need to provide for accountable interventions in critical and well-defined care situations. The research implications for the development and governance of online applications in numerous privacy-sensitive application areas are explored.
In the Backrooms of Data Science
Elena Parmiggiani, Thomas Østerlie, and Petter Grytten Almklov
Abstract
Much Information Systems research on data science treats data as pre-existing objects and focuses on how these objects are analyzed. Such a view, however, overlooks the work involved in finding and preparing the data in the first place, such that they are available to be analyzed. In this paper we draw on a longitudinal study of data management in the oil and gas industry to shed light on this backroom data work. We find that this type of work is qualitatively different from the front-stage data analytics in the realm of data science, but is also deeply interwoven with it. We show that this work is unstable and bidirectional. That is, the work practices are constantly changing and must simultaneously take into account both what data it might be possible to get hold of as well as the potential future uses of the data. It is also a collaborative endeavor, involving cross-disciplinary expertise, that seeks to establish control over data and is shaped by the epistemological orientation of the oil and gas domain.
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Meta-Analysis of The Unified Theory of Acceptance and Use of
Technology (UTAUT): Challenging Its Validity and Charting A Research Agenda In The Red Ocean
Markus Blut, Alain Yee Loong Chong, Zayyad Tsiga, and Viswanath Venkatesh
Abstract
There are both formal and informal cries that UTAUT and by association the stream of research on technology adoption has reached its limit, with little or no opportunities for new knowledge creation. Such a conclusion is ironic because the theory has not been sufficiently and suitably replicated. It is possible that the misspecifications in the various replications, applications, and extensions led to the incorrect conclusion that UTAUT was more robust than it really was and opportunities for future work were limited. Although work on UTAUT has included important variables, predictors and moderators, absent a faithful use of the original specification, it is impossible to assess the true nature of the effects of the original and additional variables. The present meta-analysis uses 25,619 effect sizes reported by 737,112 users in 1,935 independent samples to address this issue. Consequently, we develop a clear current state-of-the-art and revised UTAUT that extends the original theory with new endogenous mechanisms from different, other theories (i.e., technology compatibility, user education, personal innovativeness, and costs of technology) and new moderating mechanisms to examine the generalizability of UTAUT in different contexts (e.g., technology type and national culture). Based on this revised UTAUT, we present a research agenda that can guide future research on the topic of technology adoption in general and UTAUT in particular.
Examining the Impacts of Airbnb’s Review Policy Change on Listing Reviews
Reza Mousavi and Kexin Zhao
Abstract
In July 2014, Airbnb, one of the biggest firms in the sharing economy, decided to change the way the guest and the host review each other on the platform. Before this change, the guest/ host could post reviews about their experiences asynchronously- the guest/ host would be able to see the other party’s review whenever it was posted. The new review policy though, rolled out a simultaneous review system in which reviews are viewable only after both the guest/ host post their own reviews. This study empirically evaluates the impacts of this new review policy on guest reviews’ informativeness, measured by both the informational content (semantic diversity and objectivity) and the personal opinions (sentiment and sentiment heterogeneity).
Using Regression Discontinuity Design and a variety of techniques in the text analytics domain, we demonstrate that Airbnb’s review policy change enhanced guest reviews’ informational content in terms of semantic diversity and objectivity. We also show that the reviews’ sentiment deflated while became more diverse. Subgroup analysis revealed that lower quality listings were subject to more changes than did high quality listings. We further explore short-term and long-term effects of the review policy change, and demonstrate that the simultaneous review system has a long-lasting impact on the guest reviews’ informativeness.
Using Regression Discontinuity Design and a variety of techniques in the text analytics domain, we demonstrate that Airbnb’s review policy change enhanced guest reviews’ informational content in terms of semantic diversity and objectivity. We also show that the reviews’ sentiment deflated while became more diverse. Subgroup analysis revealed that lower quality listings were subject to more changes than did high quality listings. We further explore short-term and long-term effects of the review policy change, and demonstrate that the simultaneous review system has a long-lasting impact on the guest reviews’ informativeness.
Inventing Together: The Role of Actor Goals and Platform Affordances In Open Innovation
Kaveh Abhari, Elizabeth J. Davidson, and Bo Xiao
Abstract
With ubiquity of the Internet and social platforms, open innovation (OI) opportunities now extend to individuals with creative ideas and interests in innovation. Understanding why individuals are willing to engage in open innovation and how their diverse goals affect their participation is important for assessing the viability of various OI models and to inform platform design. In this paper, we develop a theoretical model that examines the impact of three categories of human goals––extrinsic, intrinsic and internalized extrinsic––on actors’ continuous intentions to participate in three general categories of open innovation behaviors––ideation, collaboration and socialization. The model also considers how perceived platform participation affordances mediate the influence of goals on these innovation behaviors. We validate this goals-affordances-behavior model via a field survey of participants on a Social Product Development (SPD) platform. By theorizing and empirically examining how goals influence participation in the SPD context, our study advances knowledge about open innovation behaviors, provides a foundation for future research across various OI models, and highlights practical insights for OI platform design.