Knowledge that Transforms

Digital ventures, start-ups growing by drawing on and adding to digital infrastructures, can scale their business at an unprecedented pace. We view such rapid scaling as a generative process by which a venture’s user base increases significantly between two points in time through digital innovation. We studied WeCash, a Chinese digital venture, nearly doubling its user base monthly, to learn more about this generative process. We trace three contingent mechanisms underpinning rapid scaling: data-driven operation, instant release, and swift transformation. We explain these mechanisms and how they interact in the rapid scaling of digital ventures. The research offers an agency perspective on scaling of digital ventures that speaks to the digital innovation literature.

Rapid and pervasive digitization of innovation processes and outcomes has upended extant theories on innovation management by calling into question fundamental assumptions about the definitional boundaries for innovation, agency for innovation, and the relationship between innovation processes and outcomes. There is a critical need for novel theorizing on digital innovation management that does not rely on such assumptions and draws on the rich and rapidly emerging research on digital technologies. We offer suggestions for such theorizing in the form of four new theorizing logics, or elements, that are likely to be valuable in constructing more accurate explanations of innovation processes and outcomes in an increasingly digital world. These logics can open new avenues for researchers to contribute to this important area. Our suggestions in this paper, coupled with the six research notes included in the special issue on digital innovation management, seek to offer a broader foundation for reinventing innovation management research in a digital world.

A large body of research uses data from social media websites to predict offline economic outcomes such as sales. However, recent research also points out that such data may be subject to various limitations and biases that may hurt predictive accuracy. At the same time, a growing body of research shows that a new source of online information, search engine logs, has the potential to predict offline outcomes. We study the relationship between these two important data sources in the context of sales predictions. Focusing on the automotive industry, a classic example of a domain of high-involvement products, we use Google’s comprehensive index of Internet discussion forums, in addition to Google search trend data. We find that adding search trend data to models based on the more commonly used social media data significantly improves predictive accuracy. We also find that predictive models based on inexpensive search trend data provide predictive accuracy that is comparable, at least, to that of social media data-based predictive models. Last, we show that the improvement in accuracy is considerably larger for “value” car brands, while for “premium” car brands the improvement obtained is more moderate.

Drawing on expectation confirmation research, we develop hypotheses regarding the effect of compensation on key customer outcomes following a major data breach and consequent service recovery effort. Data were collected in a longitudinal field study of Sony customers during their data breach in 2011. A total of 144 customers participated in the two-phase data collection that began when the breach was announced and concluded after reparations were made. Using polynomial modeling and response surface analysis, we demonstrate that a modified assimilation–contrast model explained perceptions of service quality and continuance intention and a generalized negativity model explained repurchase intention. The results of our work contribute to research on data breaches and service failure by demonstrating the impacts of compensation on customer outcomes. We discuss theoretical and practical implications.

Gamification, an emerging idea for using game design elements and principles to make everyday tasks more engaging, is permeating many different types of information systems. Excitement surrounding gamification results from its many potential organizational benefits. However, few research and design guidelines exist regarding gamified information systems. We therefore write this commentary to call upon information systems scholars to investigate the design and use of gamified information systems from a variety of disciplinary perspectives and theories, including behavioral economics, psychology, social psychology, information systems, etc. We first explicate the idea of gamified information systems, provide real-world examples of successful and unsuccessful systems, and, based on a synthesis of the available literature, present a taxonomy of gamification design elements. We then develop a framework for research and design: its main theme is to create meaningful engagement for users; that is, gamified information systems should be designed to address the dual goals of instrumental and experiential outcomes. Using this framework, we develop a set of design principles and research questions, using a running case to illustrate some of our ideas. We conclude with a summary of opportunities for IS researchers to extend our knowledge of gamified information systems, and, at the same time, advance existing theories.

Based on the “wisdom of the crowd” effect, consumer-generated online reviews are supposed to help consumers make more accurate product evaluations. However, the large amount of information in the reviews, coupled with conflicting opinions, can make it difficult for consumers to identify and consider those attributes relevant to their decision. Thus, while online product reviews are generally believed to empower consumers, we suggest that they may have “swaying” effects in that attribute preferences (i.e., the relative importance consumers place on various product attributes in product evaluation) are more heavily influenced by characteristics of the online reviews rather than by the relevance of the attributes to the consumers’ decision context. We propose that three characteristics of online reviews affect the assessment of attribute preferences: (1) the amount of information about attribute-level performance, which is often unevenly distributed across attributes, (2) the degree of information conflict about attribute-level performance, and (3) the relationship between the overall numeric rating and the attribute-level performance information in the reviews. We test our hypotheses in two randomized experiments and a free simulation study. Results from the three studies show that the three review characteristics influence attribute preferences and that their effects are strong enough such that attribute preferences are influenced more by these online review characteristics than by the relevance of the attributes to the consumers’ decision context. Our work, which illustrates a dark side to online reviews, has implications for both online word-of-mouth and preference construction research.

In this work, we investigate how the entry of ride-sharing services influences the rate of alcohol related motor vehicle fatalities. While significant debate has surrounded ride-sharing, limited empirical work has been devoted to uncovering the societal benefits of such services (or the mechanisms which drive these benefits). Using a difference in difference approach to exploit a natural experiment, the entry of Uber Black and Uber X into California markets between 2009 and 2014, we find a significant drop in the rate of fatalities after the introduction of Uber X. Further, results suggest that not all services have the same effect, insofar as the effect of the Uber Black car service is intermittent and manifests only in selective locations (i.e., large cities). These results underscore the importance of coupling increased availability with cost savings in order to exploit the public welfare gains offered by the sharing economy. Practical and theoretical implications are discussed.

Prior information systems research highlights the vital role of information technology (IT) for innovation in firms. At the same time, innovation literature has shown that accessing and integrating knowledge from sources that reside outside the firm, such as customers, competitors, universities, or consultants, is critical to firms’ innovative success. In this paper, we draw on the knowledge-based view of the firm to investigate how search in external knowledge sources and information technology for knowledge absorption jointly influence process innovation performance. Our model is tested on a nine-year panel (2003–2011) of Swiss firms from a wide range of manufacturing industries. Using instrumental variables, and disaggregating by type of IT, we find that data access systems and network connectivity hold very different potential for the effective absorption of external knowledge, and the subsequent realized economic gains from process innovation. Against the backdrop of today’s digital transformation, our findings demonstrate how firms should coordinate strategies for sourcing external knowledge with specific IT investments in order to improve their innovation performance.

The growing importance of online social networks provides fertile ground for researchers seeking to gain a deeper understanding of fundamental constructs of human behavior, such as trust and forgiveness, and their linkage to social ties. Through a field experiment that uses data from the Facebook API to measure social ties that connect our subjects, we separate forward-looking instrumental trust from static intrinsic trust and show that the level of instrumental trust and forgiveness, and the effect of forgiveness on deterring future defections, crucially depend on the strength of social ties. We find that the level of trust under social repeated play is greater than the level of trust under anonymous repeated play, which in turn is greater than the level of trust under anonymous one shot games. We also uncover forgiveness as a key mechanism that facilitates the cooperative equilibrium being more stable in the presence of social ties: If the trading partners are socially connected, the equilibrium is more likely to return to the original cooperative one after small disturbances.

Emotion can influence important user behaviors, including purchasing decisions, technology use, and customer loyalty. The ability to easily assess users’ emotion during live system use therefore has practical significance for the design and improvement of information systems. In this paper, we discuss using human–computer interaction input devices to infer emotion. Specifically, we utilize attentional control theory to explain how movement captured via a computer mouse (i.e., mouse cursor movements) can be a real-time indicator of negative emotion. We report three studies. In Study 1, an experiment with 65 participants from Amazon’s Mechanical Turk, we randomly manipulated negative emotion and then monitored participants’ mouse cursor movements as they completed a number-ordering task. We found that negative emotion increases the distance and reduces the speed of mouse cursor movements during the task. In Study 2, an experiment with 126 participants from a U.S. university, we randomly manipulated negative emotion and then monitored participants’ mouse cursor movements while they interacted with a mock e-commerce site. We found that mouse cursor distance and speed can be used to infer the presence of negative emotion with an overall accuracy rate of 81.7 percent. In Study 3, an observational study with 80 participants from universities in Germany and Hong Kong, we monitored mouse cursor movements while participants interacted with an online product configurator. Participants reported their level of emotion after each step in the configuration process. We found that mouse cursor distance and speed can be used to infer the level of negative emotion with an out-of-sample R2 of 0.17. The results enable researchers to assess negative emotional reactions during live system use, examine emotional reactions with more temporal precision, conduct multimethod emotion research, and create more unobtrusive affective and adaptive systems.