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Digital platforms transform supply chains. However, no unified theoretical understanding of digital platforms exists. Thus, the underlying research aims at investigating platform theories for supply chain management tasks.

The authors conduct a systematic literature review to identify relevant theories in the context of digital platforms and synthesize the respective findings for supply chain management tasks.

In total, 43 papers and 41 different relevant theories are identified. The most prominent theories are the resource-based view, transaction cost economics, internalization theory and the dynamic capabilities approach. Digital platforms alter and change the boundary decisions of firms. Therefore, they have various implications for supply chain management tasks such as make-or-buy decisions or the orchestration of resources to sustain a competitive advantage.

The identified supply chain theories as well as platform theories and their overlap provide a meaningful starting point for discussing and developing new and platform-based supply chain management approaches in the B2B domain.

The conducted systematic literature review provides a first starting point for building a holistic theoretical approach to digital platforms in supply chains. Thus, the paper contributes a missing link for discussing digital platforms and their theoretical foundations for supply chain management tasks.

This study explores the factors that influence platform-to-platform cooperation (PPC) and designs a theoretical framework for platform research.

This multi-case study includes a combination of exploratory and explanatory case studies.

From the internal factor perspective, channel integration capability, technology-based order matching capability and service innovation capability positively affects the PPC. From the perspective of external factors, the impact of a new platform entry on the PPC depends on market power and complementarities between platforms in the supply and value chains. Diversity of demand also has a positive effect on the PPC, which is moderated by network externalities. It is worth noting that the incumbent platform prefers to diversify its services for collaborating platforms with a higher level of cooperation. In addition, the higher diversity of demand, the stronger the service innovation capability, which indirectly impacts cooperation positively.

The PPC has gained immense popularity in recent years. However, no scholars have investigated the factors influencing the PPC decisions, which warrants further exploration. This study sheds light on the factors and mechanisms that influence the PPC from both internal and external perspectives.

In developing nations, the utility and intention to use algorithmic trading (AT) platforms and financial services are predominantly reliant on investors’ technological knowledge. This study aims to investigate the effect of investor awareness of AT (AAT), trust in AT (TAT) and acceptance of innovativeness (AOI) on intention to use the AT (IUAT) platforms among Indian investors.

The authors used a structured questionnaire with a five-point Likert scale to collect the data from 392 Indian retail investors through a purposeful sampling approach. And, the authors carried out structural equation modelling to analyse the serial mediation among the latent (independent) and observed (dependent) variables.

The findings suggest that investor awareness exerts a statistically significant and positive effect on the IUAT platforms. Additionally, TAT platforms and innovation acceptance, independently as well as mediator, significantly influences the usage decision of AT platforms among Indian investors.

The findings on determinants of AT platform usage can guide investment regulators to promote technological awareness, build trust and provide a safe algorithmic trading environment for retail investors in India. The suggestions may take the edge off a few behavioural impediments among the investors w.r.t. AT platform usage.

Off the back of extensive literary exploration our field research is among the first that probes an intellectual discourse and documents the empirical evidence on linkages between investor AAT, TAT, AOI and the IUAT platforms in the Indian stock market.

Investigating antecedents that may affect the usage of electronic tax declaration platforms is not a new research topic. Nevertheless, investigating the antecedents that affect the continuance usage of these platforms beyond the COVID-19 pandemic is a relatively new research trend. Accordingly, the purpose of this study is to investigate the antecedent factors that directly influence electronic tax declaration platforms’ continuance usage in the post-pandemic era by integrating the Unified Theory of Acceptance and Use Technology Model with other related factors.

To achieve the main purpose of the study, an online questionnaire was used to gather data from 231 Jordanian taxpayers.

The findings revealed the positive and significant effects of performance expectancy, social influence, knowledge and trust in e-government services on continuance usage of electronic tax declaration platforms. Contrary to what is expected, effort expectancy is insignificant, and hence the related hypothesis (H2) was rejected.

The findings of current research provided theoretical and practical implications by using a well-established theoretical lens to investigate what factors affect the continuance usage of electronic tax declaration platforms within the context of a developing country, namely, Jordan.

It has become increasingly clear that the objectives of privacy and competition policy are in conflict with one another with regard to platform data. While privacy policies aim at limiting the use of platform data for purposes other than those for which the data were collected in order to protect the privacy of platform users, competition policy aims at making such data widely available in order to curb the power of platforms.

We draw on Commons' Institutional Economics to contrast the current control-based approaches to ensuring the protection as well as the sharing of platform data with an ownership approach. We also propose the novel category of platform use data and contrast this with the dichotomy of personal/non-personal data which underlies current regulatory initiatives.

We find that current control- and ownership-based approaches are ineffective with regard to their capacity to balance these conflicting objectives and propose an alternative approach which makes platform data saleable. We discuss this approach in view of its capacity to balance the conflicting objectives of privacy and competition policy and its effectiveness in supporting each separately.

Our approach clarifies the fundamental difference between data markets and other concepts such as data exchanges.

This study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.

This study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.

The findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.

This study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.

This study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.

The proliferation of industry platforms has disrupted several industries. Firms adopting a platform business model have experienced a substantial expansion in size and scale, positioning themselves as the foremost valuable entities in market capitalization. Over the past two decades, there has been a substantial expansion in the body of literature dedicated to platforms, and different streams of research have emerged. Despite considerable efforts and the significant progress made in recent years toward a comprehensive understanding of industry platforms, there is still room for further harnessing the field’s diversity. As a result, the aim of this article is to examine the field’s structure, identify research concerns and provide suggestions for future research, thereby enhancing the overall understanding of industry platforms.

We conducted a thorough examination of 458 articles on the topic using bibliometric methods and systematic review techniques.

Through co-citation analysis, we identified five distinct clusters rooted in various bodies of literature: two-sided markets, industry platforms, digital platforms, innovation platforms and two-sided networks. Furthermore, the examination of these five clusters has revealed three key areas that demand further consideration: (1) terminologies, (2) classifications and (3) perspectives.

While previous reviews have provided valuable insights into the topic of industry platforms, none have explored the structure of the field so far. Consequently, as a first step toward advancing the field, we uncover the structure of the literature, identifying three major areas of concern. By addressing these concerns, our goal is to converge different clusters, thereby harnessing the diversity in the field and enhancing the overall understanding of industry platforms.

The dynamic and evolving nature of the market calls for attention to digital platform firms' survival strategies, building agility for persistence in a continuously changing business environment. In India, the government’s adoption of the Digital Policy is one such change in the business environment for the firms that impact almost all sectors. Such policies cause a disruption wherein digital platform firms must be agile and create a strategic response that will endure any changes. The present study attempts to gain insight into the competitive strategies adopted by the digital platform firms of the consumer durables industry in India, which are implemented to facilitate their growth.

The entire study is conducted in two phases. Phase one includes identifying strategies sampled digital platform firms adopted in response to the digitalization policy, and the second phase evaluates the significance of the adopted plans to persist.

While clubbing the 42 strategic responses to a few aggregate dimensions, the study found four types of responses adopted by the digital platform firms in the consumer durable industry to persist in the market. Using a two-step system, the Generalized Method of Moments (GMM) approach, the study found that all four dimensions are statistically significant, positively impacting these firms' profitability.

The study contributes to the knowledge base of strategic responses to persist for the incumbent platform firms in a dynamic business environment.

The study answers the pertinent research question of how such strategic decisions may be informed in favor of profitability.

Remote working has brought forward many challenges for employees as the phenomenon is still new for most employees across the globe. Some of these challenges may be addressed by the recent adoption of digital technologies by organizations. In this vein, our study explores the impact of digital platform capability on the creativity of employees through the mediating mechanism of explicit and tacit knowledge sharing.

The data were gathered from higher education institutes (HEIs) in a developing country, Pakistan which recently saw a major disruption during the Covid-19 pandemic. The proposed hypotheses were tested through Structural Equational Modeling (SEM) and the results confirmed our hypotheses.

The findings confirmed that the digital platform capabilities impact both tacit and explicit knowledge sharing among these remote employees. Likewise, the results also supported the mediating role of both explicit and tacit knowledge sharing on the creativity of these remote workers.

Our results are significant as they confirm the impact of digitalization on remote workers’ creativity predisposition. We thus advance the academic debate on the problems of knowledge sharing in remote working. We prove that digital capabilities outweigh the challenges created due to new forms of work driven by the pandemic. It further highlights the important areas to focus on while planning human resource policies in the new normal.