Knowledge that Transforms

Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex sociotechnical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating these problems requires research methods that scale beyond the traditional areas of inquiry of information systems (IS) individuals, organizations, and markets and that deliver solutions in addition to insights. We describe an approach to address these challenges through competitive benchmarking (CB), a novel research method that helps interdisciplinary research communities tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions.

The two leading online consumer-to-consumer platforms use very different revenue models: eBay.com in the United States uses a brokerage model in which sellers pay eBay on a transaction basis, whereas Taobao.com in China uses an advertising model in which sellers can use the basic platform service for free and pay Taobao for advertising services to increase their exposure. This paper studies how the chosen revenue model affects the revenue of a platform, buyers’ payoffs, sellers’ payoffs, and social welfare. We find that when little space can be dedicated to advertising under the advertising model, the brokerage model generates more revenue for the platform than the advertising model. When a significant proportion of space is dedicated to advertising under the advertising model, matching probability on a platform plays a critical role in determining which revenue model can generate more revenue: If the matching probability is high, the brokerage model generates more revenue; otherwise, the advertising model generates more revenue. Buyers are always better off under the advertising model because of larger participation by the sellers in the platform’s free service. Sellers are better off under the advertising model in most scenarios. The only exception is when the matching probability is low and the platform dedicates considerable space to advertising. Under these conditions, the sellers with payoffs similar to the marginal advertiser who is indifferent about advertising can be worse off under the advertising model. Finally, the advertising model generates more social welfare than the brokerage model.

In this article, we propose a new data-analytic approach to measure firms’ dyadic business proximity. Specifically, our method analyzes the unstructured texts that describe firms’ businesses using the statistical learning technique of topic modeling, and constructs a novel business proximity measure based on the output. When compared with existent methods, our approach is scalable for large datasets and provides finer granularity on quantifying firms’ positions in the spaces of product, market, and technology. We then validate our business proximity measure in the context of industry intelligence and show the measure’s effectiveness in an empirical application of analyzing mergers and acquisitions in the U.S. high technology industry. Based on the research, we also build a cloud-based information system to facilitate competitive intelligence on the high technology industry.

The number of mobile apps launched in the market has exponentially grown to more than 2 million, but little is known about how users choose and consume apps of numerous categories. This study develops a utility theory-based structural model for mobile app analytics. We use the theoretical concepts of utility and satiation along with the factor analytic approach, as bases in simultaneously modeling the complex relationships among choice, consumption, and utility maximization for consumers of various mobile apps. Using a unique panel dataset detailing individual user-level mobile app time consumption, we quantify the baseline utility and satiation levels of diverse mobile apps and delineate how app preferences and consumption patterns vary across demographic groups affected by persistent use and time trends. The findings suggest that users’ baseline utility substantially diverges across app categories and that their demographic characteristics and habit formation explain the appreciable heterogeneity in baseline utility and satiation. These parameters also exhibit positive and negative correlations in mobile websites and app categories. Our modeling approaches and computational methods can unlock new perspectives and opportunities for handling large-scale, micro-level data, while serving as important resources for big data analytics and mobile app analytics.

This paper examines the role of information and communication technology (ICT) in enhancing the well-being of nations. Extending research on the role of ICT in the productivity of nations, we posit that the effects of ICT may not be limited to productivity (e.g., GDP), and we argue that the use of ICT can also improve the well-being of a country by helping citizens to develop their social capital and achieve social equality, enabling access to health-related information and health services, providing education to disadvantaged communities, and facilitating commerce. Using a number of empirical specifications, specifically a fixed-effects model and an instrumental variable approach, our results show that the level of ICT use (number of fixed telephones, Internet, mobile phones) in a country predict a country’s well-being (despite accounting for GDP and several other control variables that also predict a country’s well-being). Furthermore, by using an exploratory method (biclustering) of identifying both country-specific and ICT-specific variables simultaneously, we identify clusters of countries with similar patterns in terms of their use of ICT, and we show that not all countries increase their level of well-being by using ICT in the same manner. Interestingly, we find that less developed countries increase their level of well-being with mobile phones primarily, while more developed countries increase their level of well-being with any ICT system. Contributions and implications for enhancing the well-being of nations with ICT are discussed.

I am excited about working with the diverse intellectual talent in the IS scholarly community and taking MISQ to new heights in service to the knowledge creation and scholarly conversation activities in the IS discipline!Over the last few months, people have asked me about my vision for MISQ, the initiatives that I will undertake as EIC,

In this study, we set out to better understand the dynamics behind group-based technology adoption by investigating the underlying mechanisms of changes in collective adoption decisions over time. Using a longitudinal multi-case study of production teams in the British oil and gas industry, we outline how internally or externally triggered modifications to the constellation of adoption rationales and attitudes toward a focal technology between subgroups caused changes to adoption decisions within a team. The constellations further seemed to impact usage patterns including conflicts about ICT use and the stability of adoption. Based on these observations, we suggest that group-based adoption can be differentiated in qualitatively different technology adoption states (TAS), which emerge as the result of disparate attitude–rationale configurations across subgroups in a user collective. With this reconceptualization of collective adoption as technology adoption states, our study extends current group-based models by providing a new, qualitative lens to view the creation and stability of adoption patterns in complex user groups. With this, our study offers a process view on the (dis)continuance of information systems and provides a basis for practical guidelines on how to deal with problematic adoption situations when actors from multiple (sub)groups are involved.

The capability to produce a secure, reliable form of identification on request is taken for granted by many citizens, especially those living in countries with advanced economies. This capability provides numerous development benefits for individuals, from accessing government and business services to establishing their right of residence and employment in a region. Furthermore, nationwide use of reliable means of identification can help to combat crime and illegal immigration. Efforts to introduce identity verification services in Nigeria have been presented by policymakers as an intervention that would lead to a wide range of such development outcomes. However, these benefits are proving difficult to realize. The use of identity smart cards aims to improve the current situation in which most Nigerian citizens do not possess reliable means of identifying themselves by, say, an international passport or driving license. Although IS research is well aware that the provision of a service does not of itself deliver development outcomes, the nature and role of ICT-based services in development is not well understood. Therefore, this research contributes in two ways. First, it directly addresses the relationship between ICTs and development policies and outcomes, with which much IS research engages minimally or not at all. Second, it explains citizens’ suspicion of the intervention in Nigeria and then uses secondary data from more successful cases to address the question of why some countries achieve desired development outcomes from the provision of identity verification services while others do not.

The proliferation of online auctions has attracted significant research interest in understanding real-life bidding behavior. However, most of the empirical work has focused on business-to-consumer (B2C) auctions. A natural question is whether the findings obtained from B2C auctions are applicable to business-to-business (B2B) auctions, which often involve much higher stakes. In this paper, we examine how professional bidders choose their bidding strategies in multichannel, sequential B2B auctions. Using an extensive data set from the world’s largest B2B market for cut flowers, we find a stable taxonomy of bidding behavior and identify five distinctive bidding strategies. In addition, we demonstrate that bidders’ choice of strategies is associated with their demand, budget constraint, and transaction cost. These findings challenge the conventional view that bidders’ bidding strategies will converge as they gain experience. We also analyze the economic impacts of different strategies. Our results provide useful implications for practical design of B2B auctions.