Information & Management最新文献

Successful public service projects are few. An extreme case in this study shows that the performance of a public service project in Beijing adopting a platform not only meets but goes beyond the expectations of the policy designer. Existing studies on platforms focus on the impact of a platform from a static perspective in information systems studies, but have not investigated the transformational process where a platform was built and functioned. A platform development and governance model was developed. Our findings suggest that in a public service project, (1) working as a technological tool, the platform built through contextual pressures, authorized and technological capacities of multiple stakeholders affected the roles of the government and social sector entities, and then reorganized the services to reduce corruption risks and standardized actions to better serve the people; and (2) working as a meta-organization, the platform was built through the authorized and link-building capacities of stakeholders. It integrated multiple stakeholders to better serve the people and calmed conflicts and catalyzed innovations to build norms to regulate actions and offer better service. The above governance factors promoted the multi-oriented performance of the project. In the course of generating the grounded model, we clarify the key factors in the dynamic process of how a platform works.

The proliferation of third-party platforms has led to the same product or service appearing across multiple platforms. To facilitate consumers' purchase decisions, it is essential to rank products based on online ratings from various platforms. However, ranking such products poses challenges due to discrepancies across platforms. In this paper, we propose a model for ranking products based on the evidential reasoning approach. The proposed model aims to overcome these challenges by determining a finite set of possible hypotheses, with the power set containing all possible subsets and a basic probability assignment (BPA) based on the distribution of ratings on a given platform. The model then calculates the weight of each platform and adjusts the BPA using the importance discounting method. It combines discounted BPAs using the proportional conflict redistribution rule number 5. The belief structure is then transferred into a score to rank alternatives. Finally, we validate our model by ranking hotels in Hong Kong, China, collected from popular platforms such as TripAdvisor, Agoda, Booking.com, Expedia, and Trip.com. Our case study demonstrates that our model leverages evidence combination to neutralize inconsistent information across platforms and maintain consistent opinions.

Resistance has historically been considered a salient obstacle to the implementation of information systems, including healthcare information technology. However, artificial intelligence in health care (AIH) reshapes the relationships among technologies, physicians, and patients, and the nature of resistance has thus been transformed. To gain a comprehensive understanding of this phenomenon, this study systematically examines resistance to AIH across health care providers and recipients by reviewing 94 articles. Combining innovation resistance theory and the sociotechnical perspective, we develop an overarching framework to synthesize research and provide agendas for future research. This study constitutes an initial guideline for understanding resistance to AIH.

The digital entrepreneurial ecosystem is a complex and dynamic network of entrepreneurs, businesses, institutions, and systems that use digital technology to interact with one another. It is conducive to the creation, development, and sustainment of innovation in digital ventures. However, the building blocks of any digital entrepreneurial ecosystem remain loosely integrated and under-orchestrated in the early stages of its evolution. The entrepreneurial organizations that act as matchmakers and transaction catalysts have thus come to play significant roles in digital entrepreneurial ecosystems’ functioning, but the understanding of these roles remains relatively limited. Through an in-depth qualitative study of the organizational category observed within multi-channel network (MCN) organizations, a new type of intermediary role was identified and conceptualized as that of a facilitator in live streaming e-commerce, a new role in the digital entrepreneurial ecosystem. The findings suggest that the category emergence of facilitators is based on resource orchestration in the dimensions of people, goods, and scenes, enabled by three primary mechanisms: “novelty perception” (for category awareness), “exemplar projection” (for category scalability), and “structure shaping” (for category recognizability). The theoretical framework developed through this research contributes to the emergence of a new organizational category in the context of the digital economy, as well as offering practical guidance for the category strategy for venture growth in the digital entrepreneurial ecosystem.

The emergence of the sharing economy has led to a proliferation of novel platform-based business models in recent years. A particularly prominent type of platform is the C2B2C platform, but our knowledge of the development of these platforms is limited. Through an in-depth examination of the development of Airparking, one of China's most successful parking-space-sharing platforms, our study reveals that a C2B2C platform develops in a specific sequence across three phases, with distinct required actions and developmental outcomes. We have also identified the facilitating conditions that are conducive to this process.

The current understanding of how digital orientation and corporate social responsibility activities collectively affect alliance relationship stability remains limited. This study explores the impact of these two strategic orientations on the alliance relationship stability in contract farming. Using a sample of Chinese agribusinesses, the results show that the two strategic orientations improve relationship quality between agribusinesses and farmers, which in turn enhances alliance relationship stability. Relationship quality fully mediates the effect of digital orientation on alliance relationship stability and partially mediates the effect of corporate social responsibility activities. The balance between the two strategic orientations is critical for alliance relationship stability.

Online games have emerged as a breath of fresh air in the lives of the current digital generation amidst the backdrop of COVID-19 and subsequent global lockdowns. Within Internet communities, online gamers actively engage in knowledge sharing, exchanging insights and updates on their experiences. This study introduces a model designed for Massive Multiplayer Online Gamers of Travian, focusing on intrinsic factors such as perceived usefulness, perceived ease of use, and the flow state, supported by the Technology Acceptance Model. Amidst the COVID-19 pandemic, with lockdowns and quarantine as moderators, this study uses partial least squares structural equation modeling (PLS-SEM) methodology, encompassing 496 gamers, to elucidate the impact of technology acceptance and flow theory on intrinsic motivational factors that drive online gamers' knowledge-sharing activities, underpinning the social protection theory. This pioneering research explores the implications of COVID-19 restrictions on gamers' behavioral patterns, contributing significantly to the existing literature by providing a deeper psychological understanding of the less-explored realm of diverse online gamers' activities during the pandemic.

Firm-hosted open innovation platforms (OIPs) enable multidirectional communication and interaction among users, and they have become a vital IT artifact that absorbs external resources for enterprise innovation. A key challenge for OIPs is how to motivate users’ idea contributions. The support community appears as a Q&A section in a few OIPs, in which the special role of user behavior on individuals’ idea contribution is understudied. Based on a theoretical synthesis of innovation management studies, we propose hypotheses regarding the spillover effects of user interactions in a support community (i.e., help giving and help seeking) on individuals’ innovation performance (i.e., the quantity and quality of ideas). Using a dataset from a typical OIP, we employ two empirical studies to investigate the spillover effects of user interactions and their characteristics, including breadth and depth, on innovation performance. Our results confirm the existence of spillover effects and reveal a positive effect of interaction breadth and a nonlinear effect of interaction depth on innovation. Notably, the nonlinear relationship can be moderated by the interaction breadth. Our findings provide both theoretical and practical implications for user innovation and OIP operations.

Although the extant literature demonstrates that mobile device icons change consumers’ cognition of review helpfulness, it reports contradictory findings: displaying mobile device icons either helps or hurts review helpfulness. Drawing on the instability of peripheral routes (ELM) and perceived effort, we found that the impact of mobile device icons on review helpfulness is contingent on review writing effort, represented by review length. Econometric and experimental analyses ensured the external and internal validity of results, respectively. Moreover, we applied interpretable machine learning to designing mobile device icons varying with review writing effort, aiming at maximizing their effects on review helpfulness.

We propose a personalized algorithmic decision support (PADS) tool, facilitating premium pricing for pregnant women by accounting for the risk of gestational diabetes mellitus (GDM). The insurance premium with PADS is derived from true negative and positive ratios of machine learning algorithms. Hybrid sampling with uniform designs improves ML algorithm performance under unbalanced data. Feature selection approaches guarantee the accuracy and interpretability of the prediction models. PADS reduces the premium for most patients with a lower risk of GDM. A smaller fraction of patients will pay more premiums under PADS; however, they can benefit from an earlier GDM diagnosis.