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This study aims to investigate the impact of an institutionalized market context on platform ecosystem development. It studies how platform ecosystems are set up and evolve in the asset management industry and explores the role of the platform leader and selected core network partners in unleashing value innovation notwithstanding institutional barriers. A problematization lens is used to identify deviations between the management practices in this industry setting and the prescriptions and suggested practices in the extant literature on platform ecosystem development.

The research follows a retrospective longitudinal single-case design focusing on the development of a new platform ecosystem to which several PaaS initiatives are linked. It is based on 13 in-depth interviews over a one-year period triangulated with documentation and member checks. This study identifies the impact of regulations and norms on the early stages of platform ecosystem development.

In this institutionalized market, intensified interactions between carefully selected strategic market players focusing on platform development, lead to growing value innovation initiatives. The collaboration between core actors evolves “under the radar” with select partners and with lots of controls by incumbents. The value innovation process evolves in a non-disruptive way. Initially, the new value initiatives are rather incremental and focus on optimizing the present business models while slowly adding new peripheral services shared as successful signs of value innovation initiatives. This “submerged” direction enables platform actors to gather critical mass and stimulates co-evolution with key players.

This paper outlines one vertical and looks at various principles involved during early stages of platform development. Because the authors have chosen a deep dive into one institutionalized setting, future studies could investigate a broader scope of institutionalized settings/verticals and a broader scope of management stages and related practices to replicate the study and corroborate the findings. The idea raised from hybrid platform ecosystem development also warrants further study.

Practitioners in institutionalized business-to-business markets find suggestions on how to overcome institutional barriers to platform ecosystem development and this study shows which levers can be used by core actors of ecosystems to strengthen established business models and simultaneously unleash value innovation initiatives.

This study contributes to the understanding of the challenges to be faced when setting up and expanding platform ecosystems in a highly institutionalized setting and identifies “levers” to create a smooth flow and snowball effect for platform ecosystem development. It “fine-tunes” the extant literature on platform ecosystem development to institutionalized markets.

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

This study proposes a conceptual framework for analyzing customer management strategies and their effects on Internet-based platform performance based on a review of the relevant literature, and provides directions for future research.

A literature review of relevant research articles on customer management in platform firms was conducted.

First, a framework based on the market maker view of platform firms suggests customer acquisition, customer retention and customer governance are the main customer management subprocesses toward improving platform firm performance. Second, the most studied customer management strategies for each subprocess contribute to platform performance based on the mechanisms of building customer network, developing customer network effect and managing sustainable customer networks.

This study proposes a framework that identifies customer acquisition, customer retention and customer governance as three key customer management subprocesses in platform firms. It also summarizes the most studied customer management strategies/actions for each subprocess. With this analytical framework, it identifies underexplored key issues in customer management for further research.

The literature review aims to facilitate a broader understanding of on-demand service platform operations management and proposes potential research directions for scholars.

This study searches four databases for relevant literature on on-demand service platform operations management and selects 72 papers for this review. According to the research context, the literature can be divided into research on “a single platform” and research on “multiple platforms”. According to the research methods, the literature can be classified into “Mathematical Models”, “Empirical Studies”, “Multiple Methods” and “Literature Review”. Through comparative analysis, we identify research gaps and propose five future research agendas.

This paper proposes five research agendas for future research on on-demand service platform operations management. First, research can be done to combine classic research problems in the field of operations management with platform characteristics. Second, both the dynamic and steady-state issues of on-demand service platforms can be further explored. Third, research employing mathematical models and empirical analysis simultaneously can be more fruitful. Fourth, more research efforts on the various interactions among two or more platforms can be pursued. Last but not least, it is worthwhile to examine new models and paths that have emerged during the latest development of the platform economy.

Through categorizing the literature into two research contexts as well as classifying it according to four research methods, this article clearly shows the research progresses made so far in on-demand service platform operations management and provides future research directions.

In this study, specific measures adopted by the social media platforms in China supporting personal information management are investigated via surveys targeting such platforms. The purpose of this paper is to find out how social media platforms understand information management, and from which aspects and through what specific methods they provide support for information management, which contributes to understanding the issues and strategies associated with personal information management on social media.

The dimensions and specific contents of the current platform support provided for information management are clearly defined by performing qualitative text analysis based on the content obtained from 11 platform policies published by five representative Chinese social media platforms.

How social media platforms support personal information management on creation, collection, utilisation, sharing, storage, protection, removal and modification is identified. By analysing the status quo of support provided by the Chinese social media platform, some issues are proposed for discussion. Improved normative management is required to address the coexistence of multivalued information and management risks. However, the user rights are limited because the platform policies tend to be more focused on the perspective of the social media platform. Furthermore, the platform policy contents regarding information management are incomplete, and the applicability of these policies should be improved.

This study seeks to contribute to personal information management on social media from the perspective of platform support. The perspective from the platforms as the service providers supporting information management also helps identify information management challenges and potential strategies. Furthermore, combining with the personal information management perspective, this study provides a background understanding of information management under a social collaborative framework for platforms, authorities, users and memory institutions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2020-0249

For an entrant platform in the on-demand service market, choosing an appropriate employment model is critical. This study explores how the entrant optimally chooses the employment model to achieve better performance and investigates the optimal pricing strategies and wage schemes for both incumbent and entrant platforms.

Based on the Hotelling model, the authors develop a game-theoretic framework to study the incumbent's and entrant's optimal service prices and wage schemes. Moreover, the authors determine the entrant's optimal employment model by comparing the entrant's optimal profits under different market configurations and analytically analyze the impacts of some critical factors on the platforms' decision-making.

This study reveals that the impacts of the unit misfit cost of suppliers or consumers on the pricing strategies and wage schemes vary with different operational efficiencies of platforms. Only when both the service efficiency of contractors and the basic employee benefits are low, entrants should adopt the employee model. Moreover, a lower unit misfit cost of suppliers or consumers makes entrants more likely to choose the contractor model. However, the service efficiency of contractors has nonmonotonic effects on the entrant's decision.

This study focuses on an entrant's decision on the optimal employment model in an on-demand service market, considering the competition between entrants and incumbents on both the supplier and consumer sides, which has not been investigated in the prior literature.

The purpose of this paper is to explain how ecosystems and platforms have evolved to manage customer information and to identify the management, research and teaching implications of this evolution.

This paper is based on research and industrial experience of two of the co-authors in customer relationship management, further developed with other co-authors in the field of business models, the research and teaching experience of the university authors and cross-functional literature reviews in the areas of strategy, marketing, economics, organizational behaviour and information management.

This paper shows that digitalization, cloud computing and new information-based platforms are beginning to change how customer information is being managed, creating new opportunities for improving marketing, customer relationship management and business strategy.

The impact of platforms on the management of customer information needs to be confirmed by primary empirical research.

This paper identifies the need for senior marketing management to examine closely how internal and external/public customer information platforms may enhance their capability for managing customers and setting new strategic directions.

The emergence of giant multi-sided platforms has clear implications for data protection and privacy, which need to be explored more in research.

This paper highlights the move to customer information platforms and identifies how senior managers should consider them as an option for better customer information management and as a basis for new business strategies.

Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

This study aims to enhance the theoretical foundations of servitization research by establishing a theoretical connection with complexity management. The authors develop a conceptual framework to describe complexity management mechanisms in servitization and digital platforms' specific role in allowing synergies between complexity reduction and absorption mechanisms.

A theory adaptation approach is used. Theory adaptation introduces new perspectives and conceptualization to the domain theory (servitization, with a focus on the role of digital platforms) by informing it with a method theory (complexity management).

This study provides four key contributions to the servitization literature: (1) connecting the servitization and complexity-management terminologies, (2) identifying and classifying complexity-management mechanisms in servitization, (3) conceptualizing digital platforms' role in servitization complexity management and (4) recognizing digital platforms' complexity-management synergies.

This study highlights that by using digital platforms in servitization and understanding the platform approach more thoroughly, companies can gain new capabilities and opportunities to manage and leverage complexity.

This paper aims to provide top-level design and basic platform for intelligent application in China high-speed railway.

Based on the analysis for the future development trends of world railway, combined with the actual development needs in China high-speed railway, The definition and scientific connotation of intelligent high-speed railway (IHSR) are given at first, and then the system architecture of IHSR are outlined, including 1 basic platform, 3 business sectors, 10 business fields, and 18 innovative applications. At last, a basic platform with cloud edge integration for IHSR is designed.

The rationality, feasibility and implementability of the system architecture of IHSR have been verified on and applied to the Beijing–Zhangjiakou high-speed railway, providing important support for the construction and operation of the world’s first IHSR.

This paper systematically gives the definition and connotation of the IHSR and put forward the system architecture of IHSR for first time. It will play the most important role in the design, construction and operation of IHSR.