How Story Works in Mobile App Stores? Exploring the Same-Side Effect from the Storytelling Perspective

The growing number of mobile apps has contributed to an innovation diffusion paradox whereby the accelerated pace with which mobile apps are being developed and updated has stymied their own diffusion. Due to consumers’ limited personal involvement with mobile apps, storytelling, as an emerging and novel product recommendation format, is gaining traction as a promotional mechanism for diffusing mobile apps within the ecosystem. Storytelling is particularly amenable to the context of mobile app stores by giving affective meaning to the focal app being promoted and strengthening its association with other apps available from these stores. To this end, we construct a research model to illustrate how consumers’ demand for related mobile apps is shaped by similarity in functional and visual attributes between these apps and the focal app being promoted via storytelling. Our model also sheds light on how the preceding effects could be mitigated by within-developer influence

Effects of Personality on Trading Performance in Social Trading Platforms

Social trading platforms offer opportunities for amateur investors to copy professional traders’ behavior. However, past studies on behavioral finance have largely neglected the role of personality in shaping traders’ behavior. To this end, we aim to scrutinize the effects of leader traders’ personality on their trading behaviors and subsequent performance on social trading platforms. Particularly, we employ the Myers–Briggs Type Indicator (MBTI) personality classification scheme to delineate leader traders’ personality into the four dimensions of Extraversion-Introversion (E-I), Sensing-Intuition (S-N), Thinking-Feeling (T-F), and Judging-Perceiving (J-P). Next, we draw on machine learning techniques to advance a novel text-based approach for extracting the personality dimensions of leader traders automatically. Analytical results attest to the impact of personality dimensions on trading behavior and that of trading behavior on performance. Findings from this study yield insights for both social trading platforms and followers by identifying profitable leader traders based on their personality

Telling an Attractive Digital Story: Unraveling the Effects of Digital Product Placement Strategy on Product Exposure

The accelerated pace with which mobile apps are being launched has translated into an innovation diffusion paradox for mobile app stores. To cope with the avalanche of newly launched apps, conventional product promotion has given way to digital storytelling as a means of bolstering individuals’ exposure to these apps. Digital storytelling, as an emerging and novel format of product placement, has been credited for boosting consumers’ receptivity to featured products through compelling narrative, direct links, and rich media. In this study, we construct and empirically validate a research model that illustrates how digital storytelling can be strategized for product promotion in mobile app stores. In so doing, we endeavor to not only offer an in-depth appreciation of how digital storytelling can aid in promoting mobile apps through the presentation of engaging content but to also shed light on how these promotional effects could be moderated through rich delivery

Learning from Winners: A Strategic Perspective of Improving Freelancers’ Bidding Competitiveness in Crowdsourcing

The rapid growth of crowdsourcing grants freelancers unprecedented opportunities to materialize their expertise by bidding in specific tasks. Despite lowering freelancers’ participation costs, the bidding mechanism meanwhile induces intense competition, rendering it difficult for freelancers to submit competitive bids. Although previous research has disentangled several bidding strategies, scant attention was paid to whether and how freelancers should learn to adjust their bidding strategies and improve bidding competitiveness during the journey of participating in multiple tasks. To fill in this gap, we adapt a set of bidding strategies from auction literature into the crowdsourcing context. Leveraging the lens of vicarious learning, we advance that freelancers’ learning from winners on bidding strategies will enhance their bidding competitiveness, which is moderated by task complexity. Our preliminary results suggest a significant relationship between strategic learning and bidding competitiveness, along with the moderating effect of task complexity. Expected contributions and future schemes are discussed finally

Classification of Online Customer Reviews for Digital Product Innovation: A Motivation Perspective

With rapid technological advances, digital products are becoming increasingly prevalent. Although past studies have examined the contribution of online reviews extensively in the context of physical products, there is limited understanding of the contribution of online reviews in digital product innovation. To this end, this study reviews previous work related to online reviews of physical and digital products in an attempt to reclassify online reviews of digital products from the perspective of consumer motivation. Taking game-related app reviews as an example, we employed a topic modeling model to extract insights related to consumer motivation. An in depth appreciation of consumers’ motivation from analyzing online reviews can yield invaluable insights in driving digital product innovation

The Innovation Waltz: Unpacking Developers’ Response to Market Feedback and Its Effects on App Performance

To remain competitive in the intensely competitive mobile app market, developers often rely on user feedback to fuel the innovation process. Past studies, however, have rarely examined the impact of developers’ incremental innovation strategies by treating app innovation as a continuous process. This knowledge gap prompted us to advance a framework of developers’ incremental innovation strategies comprising four coping strategies: sailing, optimizing, supplementing, and patching. Employing a multi-state Markov model to capture the probability of a developer employing an incremental innovation strategy in response to distinct types of market feedback during the app innovation process, we analyze data sourced from the Android app store that consists of 4,583 apps, 29,307 updates, and 231,817 reviews. We discovered that market feedback affects the adoption of the four incremental innovation strategies differently. Additionally, we found that sailing, supplementing, and optimizing strategies boost app downloads, while supplementing, optimizing, and patching strategies improve app ratings

Unraveling the Relationship between Content Design and Kinesthetic Learning on Communities of Practice Platforms

As a variant of the sharing economy, Communities of Practice (CoP) platforms have allowed kinesthetic learners to acquire skillsets corresponding to their interests for immediate or future use in practice. However, the impact of digital learning content design on kinesthetic learning remains underexplored in the field of information systems. We hence extend prior research by advancing content richness and structure clarity as antecedents affecting kinesthetic learners’ digestibility of contents, culminating in differential kinesthetic learning effects. To substantiate our arguments, we collected data from a leading Chinese recipe sharing platform. Whereas content richness was measured in terms of readability, verb richness, and prototypicality, structure clarity was operationalized as block structure, block quantity, and block regularity. Employing a machine learning model, we simulated and tested learners’ digestibility of image content embodied within recipes. Plans for future research beyond the current study are also discussed

Effects of Personality on Social Performance in Social Trading

On social trading platforms, the income of leader traders is largely dictated by the number of copy trades conducted by their followers. Consequently, it is imperative for leader traders to exhibit appealing personalities to entice their followers to conduct copy trades. Drawing on social capital theory, we endeavor to scrutinize the effects of traders’ personalities on the accumulation of social capital, which in turn bolsters social performance as measured by the number of copy trades. Data was extracted from a leading social trading platform. The Myers–Briggs Type Indicator personality classification system was then employed to depict leader traders’ personalities based on a novel text-based, machine learning approach. Preliminary analytical results reveal significant relationships among personality traits, social capital dimensions, and social performance. Findings from this study generate insights for social trading platforms and leader traders on exhibiting desirable personalities conducive for accumulating social capital that entice followers to conduct copy trades

Divergent Innovation: Directing the Wisdom of Crowd to Tackle Societal Challenges

Crowdsourcing is acknowledged as a promising avenue for addressing societal challenges by drawing on the wisdom of the crowd to offer diverse solutions to complex problems. Advancing a new conceptual framework of ‘divergent innovation’ which delineates between topic and quality divergence as focal metrics of performance when crowdsourcing for solutions to societal challenges, this study investigates the impacts of four ideation stimuli on divergent innovation. These four stimuli include task description concreteness, resource richness, topic entropy, and judging criteria comprehensiveness. Empirical analysis based on data sourced from an online crowd-ideation platform reveals that task description concreteness negatively affects topic divergence but positively influences quality divergence, whereas resource richness positively affects topic divergence but negatively influences quality divergence. Additionally, the relationship between topic entropy and topic divergence is U-shaped, with no significant impact on quality divergence. These findings contribute to extant literature on crowdsourcing and offer invaluable insights for practitioners

Come Rain and Shine? Exploring the Effects of Mobile Weather Applications on Users’ Movements

All Weather conditions affect human behaviors and the growing number of Mobile Weather Applications (MWAs) has amplified this effect. Yet, little is known about how human seek to actively control their behavior by appropriating mobile technology to anticipate changing weather conditions. Guided by Anticipatory Behavioral Control Theory (ABCT), this study endeavors to bride the abovementioned knowledge gap by investigating how the interface design and usage of MWAs would impact the relationship between abnormal weather conditions and users’ movement patterns. From analyzing panel data collected on the hourly movement trajectories of over 1.95 million anonymous mobile phone users over a 2-month period, we strive to shed light on the moderating influence of content representation and usage intensity of MWAs on the relationship between weather conditions and human behaviors