Research Spotlights
When Does Beauty Pay? A Large-Scale Image-Based Appearance Analysis on Career Transitions (p. 1524)
Nikhil Malik, Param Vir Singh, Kannan Srinivasan
In this study, we collect up to 15 years of career histories for over 40,000 MBA graduates from top 100 MBA programs in the United States. We find that attractive MBA graduates earn at least $2,508 more in yearly salary compared with plain-looking (unattractive) MBA graduates. The attractiveness premium is even larger for top 10 percentile attractive graduates, for those with arts undergraduate majors, and those in managerial roles, nontechnical jobs, and non–information technology industries. Policymakers should note that the attractiveness bias is not much smaller in size than gender bias. It is pervasive over time (in individuals in their 30s and 40s and not just 20s) and across industries. It may need a similar focus as gender or racial bias in labor markets. Companies can craft their HR trainings and procedures guided by this finding. A study of this scale is only possible using cutting-edge machine learning and generative artificial intelligence methods (instead of human subjects) for large-scale data processing.
Social Trading, Communication, and Networks (p. 1546)
Jiaying Deng, Mingwen Yang, Matthias Pelster, Yong Tan
Social trading is an emerging market in the sharing economy, allowing investors (followers) to duplicate the trades of other investors (leaders) in real time. We analyze the formation and dissolution of links in a large social trading network. Such networks are characterized by the rapid dissolution of links, increasing the importance of studying network dissolution. We investigate how social communication, along with financial performance and demographics, affects dynamic network evolution. We show that different types of social communication, such as posts and comments, have different implications for link formation and dissolution. Moreover, we find financial performance to be highly important for link formation and dissolution, whereas demographic characteristics are only relevant for link formation. In social trading, the extreme flexibility of followers in dissolving links and thereby, terminating their relationship instantaneously brings about large income uncertainty for leaders. Thus, a thorough understanding of network evolution and its determinants is crucial for leaders. Our results can provide guidance on when and ho to communicate with followers. As vocal leaders on social media may exert a significant influence on financial markets—as demonstrated by recent the GameStop frenzy—a better understanding of the evolution of investment networks is also important for regulators.
Are Neighbors Alike? A Semisupervised Probabilistic Collaborative Learning Model for Online Review Spammers Detection (p. 1565)
Zhiang Wu, Guannan Liu, Junjie Wu, Yong Tan
Online platforms continually face the menace of review spammers who manipulate ratings and comments for personal gain, eroding trust in online reviews. Despite various methods proposed to tackle this issue, challenges, like collusion between spammers, scarce labels, and skewed data distribution, persist. This paper introduces a practical solution to these problems by integrating individual behavioral features with a reviewer network, and it proposes a novel semisupervised collaborative learning model. Imagine our approach as a detective tool for online platforms. It works by investigating who is reviewing what and how they are linked within the reviewer network, which is like uncovering hidden relationships among reviewers to catch spammers. Through this method, our model significantly boosts the accuracy and transparency in identifying spammers. We tested this approach on real-world review data from different types of online platforms, and it outperformed other methods. Importantly, the model is resilient, even when you have limited data or when some labels might not be perfect. By using our approach, online platforms can spot fake reviews and spammers in a more cost-effective way, ensuring that the reviews are more reliable and that it is easier for consumers to make informed decisions. It is a practical step forward in the fight against fake reviews online.
Exploring Contrasting Effects of Trust in Organizational Security Practices and Protective Structures on Employees’ Security-Related Precaution Taking (p. 1586)
Malte Greulich, Sebastian Lins, Daniel Pienta, Jason Bennett Thatcher, Ali Sunyaev
Encouraging employees to take security precautions is a vital strategy that organizations can use to reduce their vulnerability to information security (ISec) threats. This study investigates how the bright- and dark-side effects of trust in organizational information security impact employees’ intention to take security precautions. Employees who trust organizational security practices are more committed to protecting the organization and are more willing to take security precautions. To foster trust in organizational security practices and security commitment, ISec managers should establish a trusting security climate to ensure that employees can speak freely about the security problems they face in their work and receive support to resolve those problems if needed. This study also alerts managers to the potential adverse consequences of employees’ trust in the organization’s protective structures. We find that employees’ trust in the organization’s protective structures can backfire, making employees complacent regarding security. Further analyses indicate that security mindfulness mediates the influence of security complacency and security commitment on precaution taking. This study contributes by exploring and verifying the bright- and dark-side effects of trust in organizational ISec.
Platform Loophole Exploitation, Recovery Measures, and User Engagement: A Quasi-Natural Experiment in Online Gaming (p. 1609)
Jianqing Chen, Shu He, Xue Yang
Online platforms often encounter the challenge of system vulnerabilities, such as bugs, which can be exploited by certain users for illicit gains. These platforms face a dilemma when devising countermeasures, particularly in deciding whether to penalize rule breakers. Different countermeasures can lead to varying economic impacts, including subsequent user engagement. In this study, based on unique field data from a prominent online gaming platform, we discovered that the occurrence of bugs has a negative effect on the online duration and consumption of observing players. Surprisingly, although the platform is responsible for the bugs, not penalizing rule breakers results in more substantial reductions in platform engagement among observing players compared with penalizing them. This effect is particularly pronounced for observers who are directly affected by rule violations. Our findings emphasize the essential role of the platform in fairly punishing rule breakers. To ensure the long-term prosperity of an online platform and the overall welfare of its participants, it is crucial for the platform to maintain high-quality system control and implement effective governance mechanisms for rule enforcement, thereby restoring justice and order to the online community.
Longitudinal Impact of Preference Biases on Recommender Systems’ Performance (p. 1634)
Meizi Zhou, Jingjing Zhang, Gediminas Adomavicius
Recommender systems are ubiquitous on various online platforms and provide significant value to the users in helping them find relevant content/items to consume. After item consumption, users can often provide feedback (i.e., their preference ratings for the item) to the system. Research studies have shown that recommender systems’ predictions, observed by users, can cause biases in users’ postconsumption preference ratings. Because these ratings are typically fed back to the system as training data for future predictions, this process is likely to influence the system’s performance over time. We use a simulation approach to investigate the longitudinal impact of preference biases on the dynamics of recommender systems’ performance. Our results reveal that preference biases significantly impair recommendation performance and users’ consumption outcomes, and larger biases cause disproportionately large negative effects. Additionally, less popular and less distinctive (in terms of their content) items are more susceptible to preference biases. Furthermore, considering the substantial impact of preference biases on recommendation performance, we examine the issue of debiasing user-submitted ratings. We find that relying solely on historical rating data is unlikely to be effective in debiasing; thus, we propose/evaluate new debiasing approaches that use additional relevant information that can be collected by recommendation platforms.
Crowdworking: Nurturing Expert-Centric Absorptive Capacity (p. 1657)
Elham Shafiei Gol, Michel Avital, Mari-Klara Stein
Organizations increasingly engage with external communities for value generation through an ever-growing multitude of digital services. Absorptive capacity, or the organizational capability to identify, assimilate, and apply new knowledge for commercial ends, is a key determinant of how organizations successfully generate value from external sources of knowledge and sustain a competitive advantage. Crowdworking—a novel form of digitally mediated work—allows organizations to hire on-demand highly skilled external experts to leverage their knowledge, skills, and networks. The approach of integrating crowdworking into organizations is increasingly gaining traction among large corporations seeking to harness the knowledge in external communities for value generation. Building on an in-depth embedded case study in a large organization that relies on two established crowdwork platforms, we explore and shed light on how the organization developed its crowdworking-related absorptive capacity to generate value from external experts. The paper offers new insights into the prevailing modus operandi related to harnessing external knowledge in today’s organizations.
Do “Likes” in a Brand Community Always Make You Buy More? (p. 1681)
Chen Liang, Ji Wu, Xinxin Li
Online brand communities often use social plug-in features, such as the Like button, to facilitate social interactions and engage users with the brands. However, whether and how such a community feature affects users’ purchases remain open questions. Analysis of user behavior following the adoption of the Like feature indicates a surprising downturn in purchases, with a 4.1% decrease in orders and a 25.0% reduction in expenditure. Notably, online purchases dip by 3.4% in order numbers and 21.1% in expenditure, with a slighter offline decrease. The treatment effect of the adoption is not always negative but varies over time and across users. First, the Like feature adoption has a positive effect on users’ purchases in the first two months (primarily through enhancing their community participation), and the treatment effect turns negative in subsequent months, leading to the overall negative treatment effect on purchases. Second, the negative treatment effect likely stems from unflattering social comparison and can become weaker or even positive when users accrue more Likes. However, only a small proportion of users receive sufficient Likes to be motivated to purchase more. Our results caution against potential downsides of the Like feature in online communities and provide valuable managerial implications.
Can Telework Adjustment Help Reduce Disaster-Induced Gender Inequality in Job Market Outcomes? (p. 1701)
Jingbo Hou, Chen Liang, Pei-Yu Chen, Bin Gu
This study investigates the role of telework adjustment in addressing gender inequality in the labor market induced by disasters, taking the COVID-19 disaster as an example. Disasters often disrupt labor markets, disproportionately impacting female workers because of traditionally greater domestic responsibilities, thus increasing gender inequality. In such a case, telework adjustment has emerged as a silver lining, granting enhanced flexibility, particularly benefiting female workers and catering to their needs. Our analysis reveals that (1) comparing workers in the same industry and holding the same occupation, we find that female workers’ telework adjustment rate is more responsive to external constraints and is 7% higher than that of male workers. (2) Telework adjustment helps reduce gender inequality in labor market outcomes via two means: (i) the higher telework adjustment rate among female workers (which reduces gender inequality by 25.48%) and (ii) the stronger marginal effect of telework adjustment on female workers (which reduces gender inequality by 31.94%). (3) Better digital infrastructure can enhance the mitigating effect of telework adjustment. Our findings offer compelling insights for policymakers and business leaders, emphasizing the strategic role of telework adjustment and digital infrastructure investments as crucial levers in promoting gender inequality during and beyond disaster scenarios.
Calibration of Heterogeneous Treatment Effects in Randomized Experiments (p. 1721)
Yan Leng, Drew Dimmery
Machine learning is commonly used to estimate the heterogeneous treatment effects (HTEs) in randomized experiments. Using large-scale randomized experiments on Facebook and Criteo platforms, we observe substantial discrepancies between machine learning-based treatment effect estimates and difference-in-means estimates directly from the randomized experiment. This paper provides a two-step framework for practitioners and researchers to diagnose and rectify this discrepancy. We first introduce a diagnostic tool to assess whether bias exists in the model-based estimates from machine learning. If bias exists, we then offer a model-agnostic method to calibrate any HTE estimates to known, unbiased, subgroup difference-in-means estimates, ensuring that the sign and magnitude of the subgroup estimates approximate the model-free benchmarks. This calibration method requires no additional data and can be scaled for large data sets. To highlight potential sources of bias, we theoretically show that this bias can result from regularization, and further use synthetic simulation to show biases result from misspecification and high-dimensional features. We demonstrate the efficacy of our calibration method using extensive synthetic simulations and two real-world randomized experiments. We further demonstrate the practical value of this calibration in three typical policy-making settings: a prescriptive, budget-constrained optimization framework; a setting seeking to maximize multiple performance indicators; and a multitreatment uplift modeling setting.
Rethinking Gamification Failure: A Model and Investigation of Gamified System Maladaptive Behaviors (p. 1743)
Shih-Lun “Allen” Tseng, Heshan Sun, Radhika Santhanam, Shuya Lu, Jason B. Thatcher
Current studies show gamification, the integrating of game design elements into target systems, enhances user engagement and instrumental task outcomes. Despite its potential for improving behavioral outcomes, gamification can also lead to maladaptive behaviors, behaviors directed at misappropriating gamified systems. We conceptualized gamified system maladaptive behaviors (GSMB), which involve technology and gamified task maladaptations. We developed a model that depicts three drivers of GSMB from design elements, how they fulfill or frustrate psychological innate needs, which in turn drive GSMB, and how GSMB affect task performance. We tested how the three drivers of design elements affect GSMB in Study 1 by empirically examining users of a gamified system, Pocket Points. The results support our conceptualization of GSMB, and design issues as its antecedents. To further unpack this relationship, we then employed a within-subject experiment and a follow-up survey in Study 2. By manipulating the design issues, we found that GSMB adversely affect task performance, because these users may focus too intently on winning the game, at the expense of task performance. By assessing the fulfillment of psychological needs, our findings suggest that design in gamified systems may not uniformly fulfill the satisfaction of psychological needs and consequently triggers GSMB.
Digital Contact Tracing for Pandemic Response: The Roles of Cultural Worldviews and Technology Awareness (p. 1766)
Jingguo Wang, Yuan Li
Information technologies have been developed and used by government agencies and public authorities to address societal issues, but their effectiveness often hinges on public support and participation. This is evidenced in the use of digital contact tracing (DCT) technology to contain the spread of the coronavirus. Despite the efforts of public authorities and technology firms to develop and promote DCT, its adoption in the United States had been low and uneven. This research resolves the puzzle by showing that the public’s mixed views on DCT are caused by their cultural worldviews, which represent their values and attitudes toward collective responsibility in addressing personal needs as well as social hierarchies and established norms in regulating behaviors. These worldviews influence not only their perceptions of the risks and benefits of the technology but also how they interpret information about the technology. Being more aware of the technology may contribute to, rather than correct, the biases resulting from individuals’ prominent cultural worldviews. This research has practical implications for policymakers and technology developers, highlighting the importance of considering cultural worldviews in communication strategies and technology design. It offers a unique perspective on the interplay between worldviews, technology, and public perception, providing valuable insights for navigating the complex landscape of emerging technologies addressing diverse societal issues.
Content Length Limit: How Does It Matter for a Consumer-to-Consumer Media Platform? (p. 1785)
Zheyin (Jane) Gu, Xuying Zhao
Our study is inspired by the rapid growth of consumer-to-consumer (C2C) media platforms such as TikTok. There are three key findings. First, we show that when content pieces on the platform are longer, viewers set a higher standard of match value in selecting content to view, leading to a lower click-through rate of contributed content on the platform. This finding suggests that a tight limit on content length increases click-through rate. Second, we show that extended content length on the platform first enhances platform performance but then hurts its performance, following an inverted U-shape curve. This pattern holds true for short-term performance measured by viewer traffic and total viewing time, as well as for long-term performance measured by total consumer surplus. This finding suggests the existence of an optimal content length. Third, we find that the optimal content length maximizing viewer traffic is smaller than the one maximizing total viewing time, which is further smaller than the one maximizing consumer surplus. As such, a platform that switches the strategic focus from short-term advertising revenue to long-term growth will benefit from extending the content length limit.
Managerial Response to Online Positive Reviews: Helpful or Harmful? (p. 1802)
Chaoqun Deng, T. Ravichandran
Managerial responses to negative reviews could be easily understood as a brand-safeguarding strategy by firms because negative reviews can damage a company’s reputation. However, it is unclear if managers should respond to positive reviews and if so, if such action helps or hurts the firm. We develop a theoretical framework to explicate the mechanisms underlying the effects of managerial responses to positive reviews on user reviewing behaviors in online platforms. We classify positive reviews into four types: one-sided affective reviews, two-sided affective reviews, one-sided instrumental reviews, and two-sided instrumental reviews. We classify managerial responses as tailored and template responses. Using natural language processing and deep learning algorithms, we extract information presented in the texts in the reviews and responses. We theorize and test which kinds of managerial responses to positive reviews are helpful and which of them are harmful. Overall, we find that a tailored response is more appropriate when responding to two-sided instrumental positive reviews and one-sided affective positive reviews, whereas template responses work for one-sided instrumental positive reviews and two-sided affective positive reviews. Not responding would be an effective strategy for mixed positive reviews.
Noisebnb: An Empirical Analysis of Home-Sharing Platforms and Residential Noise Complaints (p. 1824)
Gorkem Turgut Ozer, Brad N. Greenwood, Anandasivam Gopal
Externalities stemming from digital platforms have had a profound impact on the daily lives of people across the globe. In this work, we examine one such externality that contributes to urban quality of life, the noise stemming from home-sharing platforms, which has been subject to aggressive scrutiny by policymakers and the popular press but has received limited rigorous empirical attention. Against a backdrop of significant investment by municipalities to curb extant levels of urban noise, our findings suggest that these platforms are instead correlated with a decrease in noise complaints in New York City (notably when occupancy rates are lower or the residence is located near tourist attractions). These findings suggest that investments in abating the noise stemming from such short-term rentals are less necessary than indicated by anecdotal evidence and are better directed at other forms of urban noise sources, chiefly because such rental units are frequently unoccupied and therefore remain quieter than residential units. However, these findings also underscore the extent to which home-sharing networks may be further straining the already stressed housing market in large metropolitan areas like New York City.
Encouraging Eco-driving with Post-trip Visualized Storytelling: An Experiment Combining Eye-Tracking and a Driving Simulator (p. 1848)
Zhiyin Li, Ben C. F. Choi
Air pollution contributes to global warming and climate change, leading to extreme weather events and rising sea levels. Promoting sustainable practices has become the focus of policy programs and awareness campaigns. In this study, we propose an effective and powerful way to promote eco-driving behaviors by drawing on data storytelling. Our study shows that animated narrative and narrative sequence can trigger varying emphases on the feasibility and desirability of eco-driving practices, affecting actual driving behaviors and attitudes toward efficient driving. Specifically, in two experiments, we find that a chronological narrative sequence with animation improves subsequent driving efficiency and efficient driving attitudes. Visualization designers may consider employing narrative sequence and animation to facilitate individuals’ information comprehension and behavioral changes. Policymakers can also encourage ecological practices through effective designs of data storytelling.
A Smart Ad Display System (p. 1873)
Li Xiao, D. J. Wu, Min Ding
This paper proposes a smart ad display system to provide personalized delivery of video ads. The proposed system records consumers’ facial expression and eye gaze stream data as they watch an ad and analyzes data at the frame level. The recognized facial expression and detected eye gaze are matched to the corresponding frame of the video ad, thereby linking facial expressions to specific visual objects appearing in the ad. By tracking a consumer’s facial expressions in response to various visual objects in real time, the system learns the consumer’s individual preferences toward different ads, searches the ad pool, and selects and subsequently displays a new ad that is most likely to elicit positive attitudinal and behavioral responses. We demonstrate the feasibility and effectiveness of the proposed system with two empirical studies. The results show that by tracking a consumer’s facial responses to only one ad or even part of an ad, our proposed system is able to make reasonably accurate inferences about a consumer’s ad preferences, with or without using information about other consumers. These inferences are used to make personalized recommendations that help enhance consumers’ ad viewing experiences and elicit favorable responses.
Background Music Recommendation on Short Video Sharing Platforms (p. 1890)
Jiawei Chen, Luo He, Hongyan Liu, Yinghui (Catherine) Yang, Xuan Bi
On short video sharing platforms, users often choose background music for their videos. In this paper, we study the problem of background music recommendation for short videos on short video sharing platforms. In our recommendation setting, the item (music) is not recommended directly to the user, but to the video created by the user. When making music recommendations for videos, we consider three important players: users, videos, and music. We define a unique background music recommendation problem and design a novel background music recommendation model to address the problem. We propose a model based on the deep learning framework to effectively address the distinctive three-way relationships among users, videos, and music. Our model considers not only of the conventional user–music alignment, but also the alignment between videos and music. To evaluate our model, we conduct comprehensive experiments on real-world data collected from one of the most popular short video sharing platforms. Our proposed model significantly outperforms other existing models in recommendation performance. The superiority of our proposed model remains consistent across various scenarios, including cold-start recommendations, data sets with varying density levels, and data sets spanning diverse video categories.
The Impact of Process- vs. Outcome-Oriented Reviews on the Sales of Healthcare Services (p. 1909)
Hongfei Li, Jing Peng, Gang Wang, Xue Bai
With the rise of digital health platforms, consumers increasingly rely on online reviews when choosing healthcare services. Understanding how these reviews shape consumer decisions is crucial for both platforms and healthcare providers. To explore this, we analyzed a comprehensive data set from a leading online cosmetic surgery platform to understand how process-oriented (focusing on the recovery experience) and outcome-oriented (focusing on the end results) reviews influence the demand for healthcare services. Our findings reveal a striking disparity in the effectiveness of these two types of reviews. Generally, outcome-oriented reviews exhibit greater efficacy in boosting sales. However, the influence of each review type varies with the complexity and popularity of the services. Process-oriented reviews are more compelling for complex healthcare services, while outcome-oriented reviews prove more impactful for simpler, popular services. These insights underscore the need for tailored strategies in incentivizing and managing consumer reviews, vital for healthcare providers and digital health platforms. Furthermore, for policy makers, the study highlights the importance of regulating and guiding online review designs to ensure they accurately reflect the service process and outcome, aiding consumers in making informed decisions.
Operation Dumbo Drop: To Airdrop or Not to Airdrop for Initial Coin Offering Success? (p. 1928)
Jian Li, Xiang (Shawn) Wan, Hsing Kenneth Cheng, Xi Zhao
Initial Coin Offerings (ICOs) have become a new and popular fundraising approach for blockchain start-ups. To motivate blockchain individuals to invest in the subsequent ICO, a growing number of blockchain-based project founders employ the airdrop campaign, through which they distribute a specific amount of free official tokens or promotional tokens to potential investors on the blockchain with or without their permission. Of paramount concern to the blockchain founders contemplating whether to launch an airdrop campaign are whether the airdrop campaign has a positive effect on the potential investors’ investment behaviors in their ICOs and how the efficacy of the airdrop may vary with investors. We find that the promotional airdrop significantly increases the potential investors’ ICO investment. We further find that the airdrop is more effective in increasing the investment for individuals with transacted projects dissimilar to the focal project than those with similar ones. By incorporating the insights from our study into their airdrop campaign strategy, blockchain start-ups can effectively target the right segment of potential investors to enhance the success of their ICOs.
User-Generated Content Shapes Judicial Reasoning: Evidence from a Randomized Control Trial on Wikipedia (p. 1948)
Neil C. Thompson, Xueyun Luo, Brian McKenzie, Edana Richardson, Brian Flanagan
User-generated content, for example, on Wikipedia, is easily accessed but has uncertain reliability. This makes it attractive to use but also creates risk, so there should be limits to who uses Wikipedia and for what purposes. In this paper, we use a randomized control trial to show that Wikipedia’s influence extends to judicial decision making, a field that is highly professional and supposed to follow strict procedures. This causal evidence further emphasizes the widespread influence of Wikipedia and other frequently accessed user-generated content on important social outcomes. Our findings also reveal boundaries to user-generated content’s influence. Although Wikipedia’s influence does extend to courts of “first instance” (where the case is first decided), it does not extend to higher courts (Court of Appeals, Supreme Court). These results suggest that normative prohibitions do seem to be sufficient to keep Wikipedia from influencing the most-important, well-resourced parts of law but that these prohibitions are insufficient in areas where time and resource pressures are greater. By showing that Wikipedia is influencing such an important and formal domain, our paper reinforces the importance of improving the accuracy and reliability of user-generated content, especially in domains with far-reaching societal consequences. Because there is no obvious way to prevent individuals from taking advantage of user-generated content professionally or nonprofessionally, our findings also contribute to the ongoing discussion of how to build public repositories of knowledge into more reliable storehouses.
Strategic Expectation Setting of Delivery Time on Marketplaces (p. 1965)
Si Xie, Siddhartha Sharma, Amit Mehra, Arslan Aziz
Delivery speed is an essential component of the service provided by online delivery platforms. Because improving actual delivery speed is expensive, platforms can instead create a perception of faster delivery by showing a conservative estimate of the delivery duration when a customer places an order. We use detailed transaction-level data from a major food delivery marketplace to examine the effects of setting conservative delivery speed expectations on customers’ likelihood of future purchases and restaurant choices. When delivery is faster than expected, we find that customers are more likely to purchase again from the platform and the same (focal) restaurant they ordered from. However, we find no significant effect on future purchases from other (nonfocal) restaurants. This is possibly because of a spillover effect, as customers may switch to other restaurants. Our findings thus highlight the effect of setting conservative expected delivery times in a platform setting. Finally, we investigate the trade-off between current and future demand because of setting of a conservative estimated delivery time and show that the gain in future demand is greater than the loss in current demand, establishing the efficacy of our suggested strategy.
How Information Technology Overcomes Deficiencies for Innovation in Small and Medium-Sized Enterprises: Closed Innovation vs. Open Innovation (p. 1981)
Mariana G. Andrade-Rojas, Terence J. V. Saldanha, Abhishek Kathuria, Jiban Khuntia, Waifong Boh
Innovation is vital for the growth of small and medium-sized enterprises (SMEs). However, SMEs face deficiencies that hinder their innovation output. This study examines how information technology (IT) helps SMEs address two salient deficiencies: technological deficiency (deficiency in internal technical knowledge and skills) and government support deficiency (deficiency in favorable government policies and incentives). Our findings suggest that SME managers can achieve greater innovation output by orienting their IT-enabled innovation efforts in an open or closed manner to address specific deficiencies. They can address technological deficiency by focusing their IT efforts on promoting innovation within the firm, that is, using IT to support closed innovation. In contrast, SMEs that face government support deficiency should give preference to IT Use for Open Innovation Activities to collaborate with external constituents such as customers and suppliers. Furthermore, because of the emergence of digital platforms (e.g., crowdsourcing), managers may be overly biased toward the use of open innovation. Our results suggest that both open and closed IT-enabled innovation have value. We exhort SME managers not to disregard either form of IT-enabled innovation but rather to tailor their approach to suit their organizational context based on specific deficiencies that their firm faces.
Mr. Right or Mr. Best: The Role of Information Under Preference Mismatch in Online Dating (p. 2013)
Hongchuan Shen, Chu (Ivy) Dang, Xiaoquan (Michael) Zhang
The rise of two-sided matching platforms such as Uber, Airbnb, Upwork, and Tinder has changed the way we commute, travel, work, and even date. The success of these platforms depends on the role of information: What information and how much information should be provided? In this study, we focus on a defining characteristic of two-sided matching markets—that is, a match depends on the possibly different preferences of the two sides—and argue that the optimal amount of information released depends on the extent to which the preferences of the two sides are mismatched. Specifically, in an empirical context of online dating, we find that when there exists preference mismatch between the two sides, having less match-relevant information about the other side leads to a better matching outcome. Our study provides insights into how the amount of information available to each side affects matching outcomes on two-sided platforms and offers guidance on information design strategies. Additionally, our findings are not confined to dating websites and can be extended to other matching platforms, such as Airbnb and Upwork, where misaligned preferences can exist between the two sides.
The Open Prison of the Big Data Revolution: False Consciousness, Faustian Bargains, and Digital Entrapment (p. 2030)
Ojelanki Ngwenyama, Frantz Rowe, Stefan Klein, Helle Zinner Henriksen
This paper critically examines the mechanisms and implications of digital giants’ Big Data practices on individual autonomy and privacy. The paper argues that digital platforms manipulate users into accepting surveillance and data harvesting, benefiting tech firms while economically and socially harming individuals. Using concepts from critical social theory, the authors highlight how digital platforms create a digital habitus, conditioning users to perceive their work and life through a digital lens. This false consciousness leads to disastrous compromises (Faustian bargains) with tech companies, eroding the foundations of the “good life”: freedom, liberty, and personal privacy. The paper uses the example of Microsoft Viva, an employee experience platform promising to improve performance and well-being, to illustrate how everyday digital tools can encourage users to accept Faustian bargains and entrap them in an “open prison” of constant surveillance and data exploitation. While digital innovations have often started with idealism and noble intentions, the dominant business models effectively encourage the adoption of exploitative Big Data practices. The authors call for critical interrogation of these practices and advocate for policy interventions to protect personal liberties and prevent digital entrapment. They emphasize the need for regulations that ensure transparency, accountability, and user empowerment in the digital age.
Digital Approaches to Societal Grand Challenges: Toward a Broader Research Agenda on Managing Global-Local Design Tensions (p. 2059)
Satish Nambisan, Gerard George
Despite considerable and continued resource investments, effective solutions to broad-scope problems of social interest or societal grand challenges (GCs) have proven to be elusive in many domains. In multiactor situations that characterize GCs, divergent goals, needs, priorities, and capabilities of global and local actors create organizing design tensions that need to be considered before solutions can be enacted. Emergent digital technologies can play an important and transformative role in addressing the organizing design tensions that pervade such collective action problems. In this paper, we draw on Elinor Ostrom’s principles of public value creation and identify a set of eight organizing design tensions that arise from employing global and local perspectives in addressing GCs. We consider novel digital approaches—that involve alternative arrangements of digital and socio-political elements in GC settings—to resolving each of these design tensions. Our discussion foreshadows the considerable opportunity for information systems research to contribute to the broader dialog on GCs; inform GC-related policy and practice at global and local levels; and, more broadly, speed the identification and enactment of effective solutions to grand challenges.
