Knowledge that Transforms

Article title: The Empirical Reality of IT Project Cost Overruns: Discovering APower-Law Distribution Authors: Flyvbjerg, B., Budzier, A., Lee, J. S., Keil, M., Lunn, D., & Bester, D. W. Journal: J...

Mobile platforms provide important entrepreneurial opportunities by facilitating access to a global market composed of more than a hundred countries. These opportunities are crucial for mobile app startups as they seek to reach a wider audience and accelerate growth. Nevertheless, performing in foreign countries is challenging as mobile app startups face a “foreignness liability” since they are competing against complementors (i.e., app developers) who have experience in their domestic market. We offer a research model that assesses the impact of cultural distance on the app-level performance of mobile app startups in foreign countries. We hypothesize the moderating effects of investor-consumer cultural distance and environmental characteristics within foreign countries. We test the model using a dataset of over 550 mobile app startups operating on the iOS platform. Our findings support our hypotheses and provide insights into whether mobile platforms are level playing fields. Our study sheds light on the interactions between a startup and its investors’ cultural distances highlighting configurations of cultural distance that are beneficial for app performance in foreign countries when the environment is stable or uncertain.

The fierce competition among brands on online marketplaces makes the optimization of offerings within this context a significant challenge. To address this challenge, we draw upon network theory and model the degree of competition through consumers’ consideration sets. We use a large empirical dataset from one of the biggest online marketplaces to explore the dynamic relationship between network position and the degree of competition, and we depict the redistribution of market share of related offerings after adjusting their array. In doing so, we provide a theoretical reference on when and how brands should optimize their product offerings on online marketplaces. We further demonstrate that intra-brand cannibalization relations have a significantly greater impact on the degree of competition compared to inter-brand ones, while intra-brand cannibalization relations represent the main reason for fluctuations in the degree of competition. Hence, contrary to existing theoretical insights and practical intuitions, our findings demonstrate that brands should minimize the number and heterogeneity of their offerings within a market segment to increase their sales on online marketplaces.

Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, depend on voluntarily user-submitted data about various businesses to assist consumers in finding appropriate options for transactions. Yet the crowdsourced nature of such data restricts the availability of attribute values for many businesses on the platform. Crowdsourced data often suffer serious completeness and timeliness constraints, with negative implications for key stakeholders such as users, businesses, and the platform. We thus develop a novel, deep learning–based imputation method, premised in institutional theory, to estimate missing attribute values of individual businesses on an OBD platform. The proposed method leverages a deep model architecture and considers both inter-business and inter-attribute relationships for imputations. An application to a Yelp data set reveals our method’s greater imputation effectiveness relative to prevalent methods. To illustrate the method’s practical utilities and values, we further examine the efficacy of business recommendations empowered by its imputed business attribute values, in comparison with those enabled by data imputed by benchmark methods. The results affirm that the proposed method substantially outperforms benchmarks for imputing missing attribute values and empowers more effective business recommendations. This study addresses crucial, prominent completeness and timeliness constraints in crowdsourced data on OBD platforms and offers insights for downstream applications that can improve user experiences, firm performance, and platform services.

Firms often assemble digital infrastructures using continuously evolving software applications sourced from a multitude of vendors. Using the theoretical lens of the threat-rigidity thesis, we raise the possibility that during adverse environmental conditions, software-vendor diversification can be a source of organizational rigidity that may dampen firm performance. Empirical analysis using data on 918 large public U.S. firms operating during two severe environmental shocks, the global financial crisis and the burst of the dot-com bubble, lends strong support to our thesis. Results indicate that a variety of firm performance indicators (e.g., stock return and operating income measures) are negatively associated with software-vendor diversification during crisis periods. Mediation analysis highlights the role of IT-related material weakness in firms’ internal controls in transmitting threat-rigidity effects that decrease performance. These results underscore the importance of software portfolio optimization for countering the dysfunctional effects of software-vendor diversification during adverse environmental shocks.

How do investors’ risk preferences influence the relationships between investors’ online channel use intensity and both their trading behaviors and performance? This study answers this important question even as investors are increasingly rely on the Internet for their trading activities. We leverage rare and unique micro-level historical dataset from more than 7,000 investor accounts over a 44-month period between 2010 and 2013 at a large brokerage firm in China. The dataset and analyses enable us to provide new insights into how investors’ online channel use intensity and risk preferences jointly influence their trading behaviors and performance, even though some other aspects of financial markets have changed considerably over the years. The findings reveal that although online channel use intensity is associated with increased trading volume, trading frequency, and investment returns, these effects differ across investors with different risk preferences. We find that while online channel use intensity has strong positive effects on transaction frequency for both risk-seeking and risk-averse investors, it has a much lower effect on trading volume for risk-averse investors than for risk-seeking investors. We further find that risk-averse investors with higher online channel use intensity outperform investors with other risk preferences in terms of investment performance. This paper contributes to the emerging literature at the intersection of information systems and behavioral finance by revealing the moderating role of risk preferences in the relationships between investors’ online trading channel use intensity and both their trading behaviors and outcomes. We discuss the implications for research and practice.

This paper explores the formation of a learning community facilitated by custom collaborative learning software. Drawing on research in group cognition, knowledge building discourse, and learning analytics, we conducted a mixed-methods field study involving an asynchronous online discussion consisting of 259 messages posted by 50 participants. The cluster analysis results provide evidence that the recommender system within the software can support the formation of a learning community with a small peripheral cluster. Regarding knowledge building discourse, we identified the distinct roles of central, intermediate (i.e., middle of three clusters), and peripheral clusters within a learning community. Furthermore, we found that message lexical complexity does not correlate to the stages of knowledge building. Overall, this study contributes to the group cognition theory to deepen our understanding about collaboration to construct new knowledge in online discussions. Moreover, we add a much-needed text mining perspective to the qualitative interaction analysis model.

Media capabilities influence consumers’ trust in online exchanges. However, in the sharing economy, where consumers interact with service providers through a platform, conventional models of trust must be revisited. Our research identifies how media synchronicity and anonymity influence the relative importance of institution-based trust in sharing economy exchanges. We collected data from 248 ride-hailing customers and 288 cryptocurrency users to test a moderated mediation model of trust. We find that in the sharing economy media synchronicity and anonymity lead customers to develop trust toward service providers directly and undermine the impact of institutional trust mechanisms. This indicates that in sharing economy exchanges, trust can be built directly with the service provider, or alternatively, indirectly through the platform. Consequently, organizations in the sharing economy can strategically design their systems to engender trust by choosing between (1) emphasizing the platform’s reputation or (2) encouraging direct communication between the consumer and service providers.

Informed by theoretical perspectives on working memory demands and devices’ potential to “prime” different types of cognitive processing, this paper investigates whether we tend to think “faster” and more intuitively, with less reflection when we use a smartphone instead of a personal computer (PC) or notebook. Three complementary experimental studies with a total of 823 participants reveal that the results of using such devices surface only when participants can select the smartphone as their preferred device. Controlling for potential confounding variables reveals no evidence of general differences between devices. Our findings caution against overemphasizing the importance of the type of device in thinking slow or fast and establish self-selection bias as an important factor in explaining such differences. This study contributes to clarifying the psychology of smartphone screens and how humans make choices when they are using these devices.