Knowledge that Transforms

As digital devices increasingly integrate hardware and software features, firms must adopt innovation strategies that effectively balance novelty and familiarity to enhance consumer evaluation. While novel hardware components can introduce unique functionalities that attract consumers, excessive novelty may impede consumer acceptance. This study investigates how hardware component innovation strategies must navigate the delicate interplay between novelty and familiarity by examining two critical dimensions: the timing of hardware innovations (early vs. late) and the role of software-supported interaction (with vs. without related software support). By distinguishing between dominant design components familiar to consumers and non-dominant design components that are inherently unfamiliar, we uncover nuanced strategic insights. Our findings reveal that early introduction of dominant design innovations is crucial, and enhancing consumer interactions through software support significantly improves consumer satisfaction. Conversely, for non-dominant component innovations, a later market introduction proves more advantageous. Notably, software-supported interactions are less effective for these non-dominant innovations, as such support may inadvertently accentuate their unfamiliarity. These findings provide strategic guidance for smartphone manufacturers to leverage software-supported interactions and optimize the timing of hardware innovations to achieve an optimal balance between novelty and familiarity.

The online gaming industry increasingly incorporates virtual agents to enhance player experiences. Although prior literature has explored the provision of virtual agents in gaming, research on technological advancements remains limited. In this study, we investigate how introducing artificial intelligence (AI) powered agents as virtual opponents (versus rule-based opponents) influences human players’ engagement and performance. Leveraging a large-scale quasi-field experiment in a multiplayer online racing game, we employ difference-in-differences analyses with matching strategies and show that, on average, the introduction of AI opponents can have ‘discouragement effects’ on players, resulting in reduced player engagement and decreased performance. Our research also identifies differential long-term impacts of AI opponents on engagement and performance. In addition, our mechanism exploration reveals that introducing AI opponents increases competition intensity and immersion in the game, and these two factors exhibit opposing influences on players’ subsequent behavior. Specifically, heightened competition hinders players’ further engagement and performance progression, whereas a more immersive experience encourages more gaming participation and better performance. Further, we find the effects of AI opponents on player engagement and performance vary by player motivation and skill levels, such that competition-oriented and highly skilled players are more receptive to AI opponents. Moreover, our findings indicate that optimal engagement and performance outcomes occur when players compete against opponents with comparable and low competence levels, respectively. Lastly, we observe an inverted U-shaped relationship between the proportion of AI opponents and players’ engagement and performance, wherein both insufficient and excessive exposure to AI opponents lead to diminishing outcomes. Our study contributes to the literature on human-AI interactions by offering novel empirical evidence on the impact of AI opponents on human players’ experiential and instrumental outcomes and disentangling the underlying mechanisms. This work also offers practical implications for game designers and policymakers regarding the design of AI-integrated competitive environments.

In the last decade, social networking platform designers have made notable efforts to harness the power of networks for social good by elevating the prominence of individual donation information. This study investigates how digital profile enrichment that exhibits users’ charitable giving activities could influence users’ decisions about whether to give, how much to give, and whether to disclose contribution amounts. Our analyses are based on a profile enrichment intervention on Weibo, China’s largest social networking site. We found that the profile enrichment to exhibit users’ historical donation counts on social profiles decreased an average user’s odds of donating by 15.5% but increased the contribution amount by 2.69%. Strikingly, it increased the odds of revealing contribution amounts by 162%. Clustering analyses further revealed three patterns in response to the profile enrichment: “presenters” (9.7%), who reduced donation frequency but increased contribution amounts and the disclosure of contribution amounts; “restrainers” (47.3%), who reduced their giving; and “conformers” (43%), who increased giving after the profile enrichment. We discuss potential mechanisms by comparing the characteristics of different user clusters to underscore donor heterogeneity and uncover the nuanced impact of such digital profile enrichment.

The rise of inappropriate content (e.g., misinformation, spam, hate speech, etc.) has become a major concern for social media platforms. To deal with such challenges, platforms adopt various strategies to moderate the content on their websites. This study focuses on user bans, a common but controversial moderation strategy that suspends rule-violating users from further participation on a platform for a predetermined period. Specifically, we investigated the impacts of user bans on banned users’ content-generating behavior (both quantity and quality). Leveraging reactance theory, we formalized our hypotheses relating users’ behavioral reactions to this content moderation strategy. We implemented multiple empirical designs to analyze data from a major social media platform. Our results show that users provided more answers, on average, after bans were lifted. In contrast, we found that the quality of the content (measured by linguistic features and content appropriateness) decreased after user bans. Furthermore, we found that platform recognitions, such as badges and recommendations, alleviated individuals’ reactance toward bans. Specifically, users who have received platform recognitions reduced inappropriate postings and improved the quality of their content after bans. Lastly, we explored the heterogeneous effects of user bans for different banning causes and repeated bans. Our research is among the first to evaluate the effectiveness of user bans and has important implications for content moderation on social media.

As digital platforms evolve, app developers do more than passively participate; they actively reshape the competitive landscape through boundary-shifting moves (BSMs). By modularizing boundary resources at different levels of the technology stack, developers can provoke, delay, or neutralize competitive responses in hypercompetitive app markets. Drawing on an empirical analysis of mobile apps launched by leading Chinese internet firms, we uncover a striking asymmetry in how rival developers react to these moves. Specifically, higher-level app modules—those tailored to particular application domains—tend to delay rival responses, while lower-level technical modules—broadly applicable across domains—trigger faster competitive reactions. This effect is heightened among firms with a history of direct competition, as they respond more aggressively to changes at the lower levels of modularization. By foregrounding the strategic role of technology stack levels in shaping competitive interactions, our study advances a differential resourcing perspective and offers new insights into the dynamics of competition within digital platforms. These findings challenge the dominant host platform-centric and cooperative views on boundary resources, illuminating how app developers actively reshape platform ecosystems to gain temporary advantage.

Many information technology (IT) projects fail to deliver the promised value within the initial budget and the estimated schedule. These shortfalls materialize in part due to unaddressed project risks. The IT project risk and control literature has demonstrated that process controls can alleviate the adverse effects of technical IT project risks on project outcomes. However, the literature has yet to investigate why certain IT project teams respond better than others to process controls. To fill this gap, we extend the IT risk control framework of Venkatesh et al. (2018) by integrating the contingent role of team personality, which includes two meta-traits based on the Big Five personality traits: team stability (conscientiousness, agreeableness, emotional stability) and team plasticity (extroversion, openness to experience). We conducted a field study of 424 offshore unique IT projects, comprising 4,516 unique project team members, to test our model. We found that, in general, teams with a higher level of stability respond better to internal process control, and teams with a higher level of plasticity respond better to external process control. We discuss further nuances of the three-way interactions of technology (technical risks), process (process controls), and people (team personality), and their effects on product and process performance. Our work thus contributes to the IT project management literature by extending the nomological network of IT project risk and control and incorporating the people aspect into this framework. In addition, it outlines how the consideration of team personality can assist managers in better deploying process controls to achieve IT project success.

Crowd-voting mechanisms are commonly used to implement scalable evaluations of crowdsourced creative submissions. Unfortunately, the use of crowd-voting also raises the potential for gaming and manipulation. Manipulation is problematic because (1) submitters’ motivation depends on their belief that the system is meritocratic, and (2) manipulated feedback may undermine learning, as submitters seek to learn from received evaluations and those of peers. In this work, we consider a design approach to addressing the issue, focusing on the notion of strategic opacity, i.e., purposefully obfuscating evaluation procedures. On the one hand, opacity may reduce the incentive and thus the prevalence of vote manipulation, and submitters may instead dedicate that time and effort to improving their submission quantity or quality. On the other hand, because opacity makes it difficult for submitters to discern the returns to legitimate effort, submitters may also reduce their submission effort or simply exit the market. We explored this tension via a multimethod study employing field experiments at 99designs and a controlled experiment on Amazon Mechanical Turk. We observed consistent results across all experiments: opacity leads to reductions in gaming in these crowdsourcing contests and significant increases in the allocation of effort toward legitimate vs. illegitimate activities, with no discernible influence on contest participation. We discuss boundary conditions and the implications for contest organizers and contest platform operators.

This research examines how digital platforms influence the performance of nonbinding contracts. Businesses in many industries with high uncertainty, such as trucking freight and construction, simultaneously use nonbinding contracts, which impose no legal sanctions for refusals, and spot markets, which facilitate real-time, flexible transactions with market-determined prices. Understanding the conditions under which nonbinding contracts perform is a major concern in these industries. Leveraging the entry of the Amazon Freight platform in the trucking freight industry, we demonstrate that adding a digital platform-enabled spot marketplace improves the performance of nonbinding contracts. We identified several mechanisms driving this effect: (1) expanding carriers’ transportation capacity, (2) lowering spot market prices, and (3) reducing shippers’ reliance on nonbinding contracts for shorter-haul truckloads. Moreover, the digital platform’s impact on enhancing nonbinding contract performance is particularly pronounced in markets with volatile demand and shorter hauls. This research contributes to understanding the impacts of digital platforms in highly uncertain industries that simultaneously use nonbinding contracts and spot markets. Our findings provide implications to policymakers and business managers on leveraging digital platforms to improve operational efficiency in highly uncertain industries.

A slowdown in revenue growth, referred to as revenue stall in this study, is a key concern for any firm. We examine how information technology-producing firms (i.e., IT firms) differ from non-IT firms in experiencing revenue stall and in benefiting from R&D investments in terms of reduced revenue stall. We hypothesize that whereas IT firms experience more revenue stall than non-IT firms, R&D investments reduce revenue stall to a greater extent in IT firms than in non-IT firms. Our empirical analyses of a longitudinal dataset of more than 1,400 large public firms in the United States from 1950 to 2015 broadly support our hypotheses. Consistent with the theoretical arguments underlying our hypotheses, we also find that IT firms experience higher competition, dynamism, and turbulence, and have higher intangible assets than non-IT firms.

Businesses are increasingly providing online assistance through help features to enhance the user experience in the digital era. Understanding factors influencing users’ tendencies to seek online help is crucial for optimizing resources and improving support and the overall user experience. Drawing on utilitarian- and hedonic-motivation systems theory, this paper examines how the type of experience and the type of help provider impact users’ online help-seeking behavior. Across three studies—including secondary data analysis, an online experiment, and an observational study of actual user behavior—we found that users are more (vs. less) inclined to seek help when encountering difficulties in utilitarian (vs. hedonic) experiences. This pattern was driven by users’ greater focus on achieving specific outcomes in utilitarian contexts, in contrast to their emphasis on experiential enjoyment in hedonic contexts. Importantly, users showed a stronger preference for seeking assistance from human agents over service robots when facing challenges in utilitarian experiences. Nevertheless, during hedonic experiences, no notable difference between humans and robots emerged in users’ inclination to seek help. These findings highlight the importance of considering the type of experience and the type of help provider when planning online support services, contributing valuable insights to the literature on hedonic vs. utilitarian motivation systems, users’ online help-seeking behavior, and user experience.