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In a multi-stakeholder recommender system (MSRS), stakeholders are the multiple entities (consumer, producer, system, etc.) benefited by the generated recommendations. Traditionally, the exclusive focus on only a single stakeholders' (for example, only consumer or end-user) preferences obscured the welfare of the others. Two major challenges are encountered while incorporating the multiple stakeholders' perspectives in MSRS: designing a dedicated utility function for each stakeholder and optimizing their utility without hurting others. This paper proposes multiple utility functions for different stakeholders and optimizes these functions for generating balanced, personalized recommendations for each stakeholder.

The proposed methodology considers four valid stakeholders user, producer, cast and recommender system from the multi-stakeholder recommender setting and builds dedicated utility functions. The utility function for users incorporates enhanced side-information-based similarity computation for utility count. Similarly, to improve the utility gain, the authors design new utility functions for producer, star-cast and system to incorporate long-tail and diverse items in the recommendation list. Next, to balance the utility gain and generate the trade-off recommendation solution, the authors perform the evolutionary optimization of the conflicting utility functions using NSGA-II. Experimental evaluation and comparison are conducted over three benchmark data sets.

The authors observed 19.70% of average enhancement in utility gain with improved mean precision, diversity and novelty. Exposure, hit, reach and target reach metrics are substantially improved.

A new approach considers four stakeholders simultaneously with their respective utility functions and establishes the trade-off recommendation solution between conflicting utilities of the stakeholders.

The Multi-Stakeholder Recommendation System learns consumer and producer preferences to make fair and balanced recommendations. Exclusive consumer-focused studies have improved the recommendation accuracy but lack in addressing producers' priorities for promoting their diverse items to target consumers, resulting in minimal utility gain for producers. These techniques also neglect latent and implicit stakeholders' preferences across item categories. Hence, this study proposes a personalized diversity-based optimized multi-stakeholder recommendation system by developing the deep learning-based diversity personalization model and establishing the trade-off relationship among stakeholders.

The proposed methodology develops the deep autoencoder-based diversity personalization model to investigate the producers' latent interest in diversity. Next, this work builds the personalized diversity-based objective function by evaluating the diversity distribution of producers' preferences in different item categories. Next, this work builds the multi-stakeholder, multi-objective evolutionary algorithm to establish the accuracy-diversity trade-off among stakeholders.

The experimental and evaluation results over the Movie Lens 100K and 1M datasets demonstrate that the proposed models achieve the minimum average improvement of 40.81 and 32.67% over producers' utility and maximum improvement of 7.74 and 9.75% over the consumers' utility and successfully deliver the trade-off recommendations.

The proposed algorithm for measuring and personalizing producers' diversity-based preferences improves producers' exposure and reach to various users. Additionally, the trade-off recommendation solution generated by the proposed model ensures a balanced enhancement in both consumer and producer utilities.

Digital business platforms (DBPs) such as Alibaba and Google Shopping are partnership networks that use the Internet to bring service providers (e.g. retail vendors) and customers together. One of the benefits of DBPs is network effects, in which customers can purchase from multiple providers, giving rise to a unique network. However, few studies have explored which service providers benefit from network effects and which do not.

Using the theories of transaction costs and network analysis, the authors apply network models to DBPs to understand which service providers benefit from network effects.

The authors identify three segments of service providers: (1) those with high prominence (connection to providers with high network centrality), (2) those with high network constraint (adjacent to isolated providers) and (3) those with low prominence and constraint. The authors find that segments (1) and (3) benefit from reciprocated customer exchanges, and thus benefit from network effects, while high constraint segment (2) providers do not benefit from reciprocated exchanges. Moreover, the authors find that for segments (2) and (3) future sales have a negative association with unreciprocated customer exchanges, while segment (1) has no significant association between unreciprocated exchanges and future sales.

The authors discuss implications for a multisided platform (MSP), as it decides which service providers to attract, promote and recommend. They can use this study’s results to know which segments of providers will increase network effects to make the platform more valuable.

This paper provides managers of service platforms with strategies for managing relations with their service providers.

Service platforms are an important and disruptive business model. The authors need to understand how network effects operate to create efficient platforms.

This paper extends the literature on MSPs by quantifying network effects and showing not all service providers benefit equally on an MSP from network effects. Critical insights into network effects on the MSP are provided, including different ways it can impact provider sales.

Recommender systems (RS) are designed to communicate with users and drive consumers' engagement with the platform. However, little is known about the strength of this relationship and how RS can create stronger consumer engagement (CE) with the platform brand. Addressing this gap, this paper examines the role of RS in converting consumers' short-term engagement with the RS to their longer-term platform engagement.

To explore these issues, the authors review key literature in the areas of CE and RS, from which they develop a conceptual framework.

The proposed framework suggests RS design as an important precursor to consumers' RS use, which is expected to affect their platform engagement/disengagement, in turn impacting the firm's long-term outcomes. The authors also identify key managerial tactics, strategies and challenges to aid the conversion of consumers' RS to CE.

This research raises pertinent implications for research on the RS/CE interface, as synthesized in a proposed research agenda.

Based on the attained insight, authors outline implications for managing, facilitating and leveraging the proposed RS to CE conversion process. Correspondingly, authors argue that, to optimize RS effectiveness, RS designers should understand the nature of CE.

By exploring the effect of consumers' RS on their longer-term CE with the platform, the analyses offer pioneering managerial insight into RS effectiveness from a CE perspective.

This literature review explores the potential of gamification in workplace learning beyond formal training. The study also highlights research gaps and opportunities for scholars to develop new theories and methodologies to enhance the understanding and application of gamification in workplace learning. It provides guidance for managers to use gamification to enhance learning and engagement. Ultimately, this review presents gamification as a promising field of study to increase individual and organizational performance.

Literature review of 6625 papers in the timeframe 1990–2020, with an update to include papers published in 2023.

This article examines the impact of gamification beyond formal learning and its potential to enhance employee productivity and well-being in the workplace. While there has been extensive research on gamification in formal learning contexts, little is known about its impact on informal learning. The study argues that the context of gamification is crucial to extending its effects and discusses the role, antecedents and consequences of game design elements in the workplace. The article also explores how the learning context relates to employee learning during work. Further research is necessary to investigate the impact of individual characteristics on work experience and performance.

Intended contribution of the present study is the development of a theoretical framework exploring the benefits of gamification in a work context.

For practicing managers, this paper shows how to use gamification to increase workplace learning and employee engagement, not just in the context of formal learning—as some companies already do today—but also systematically, in the context of informal learning.

This study explores the impact of gamification on informal workplace learning and emphasizes the significance of the context of gamification in extending its effects to improve individual and organizational performance.

This study aims to identify the key drivers of consumer engagement in e-commerce among young consumers at bottom-of-pyramid (BoP) markets and their impact on continued usage intention.

A cross-sectional research design was used to understand low-income customers’ engagement in e-commerce, specifically online shopping. The data for this study were collected from BoP customers in the Indian market. A conceptual model was proposed, and hypotheses were developed using the stimulus–organism–response (S-O-R) framework. For analysis, structural equation modeling was performed using AMOS 20.0 software to test the structural model.

The results of the study highlight that perceived importance, technology and infrastructure and social influence are key drivers of e-commerce at BoP customers. Key drivers have shown a significant positive impact on customer engagement which leads to continue usage intention of e-commerce. Furthermore, customer engagement has shown a strong relationship with continue usage intention of e-commerce.

This study indicates that young consumers’ engagement is important for e-commerce service providers to gain a market share. BoP markets offer immense opportunities to create, develop and sustain e-commerce firms for a long time, especially in India. Managers should recognize the potential of BoP markets, which can generate a huge demand for products and services on e-commerce platforms.

This study contributes both theoretically and empirically. Theoretically, this adds to the existing knowledge of customer engagement, especially in e-commerce and BoP market segment. Empirically, it tested the conceptual research model of low-income customer engagement in the e-commerce marketplace using the S-O-R framework. The study recommended practical implications for e-retailers/e-commerce service providers engaging BoP customers in a digitally connected and intensively competitive era.

The below-average innovative capacity of the tourism sector raises the question on the potentials of digital business ecosystems (DBEs) to overcome these shortages at a destination level – especially within a smart city environment. Using the example of the German Capital Berlin, this article aims to discuss both the possibilities and inhibitors of innovative knowledge-creation by building scenarios on one specific design option: the integration of digital deep learning (DL) functionalities and traditional organizational learning (OL) processes.

Using the qualitative GABEK-method, major characteristics of a DBE as resource-, platform- and innovation systems are analyzed toward their interactions with the construction of basic action models (as the basic building blocks of knowledge).

Against the background of the research findings, two scenarios are discussed for future evolution of the Berlin DBE, one building on cultural emulation as a trigger for optimized DL functionalities and one following the idea of cultural engineering supported by DL functionalities. Both scenarios focus specifically on the identified systemic inhibitors of innovative capabilities.

While this study highlights the potential of the GABEK method to analyze mental models, separation of explicit and latent models still remains challenging – so does the reconstruction of higher order mental models which require a combined take on interview techniques in the future.

The resulting scenarios innovatively combine concepts from OL theory with the concept of DBE, thus indicating possible pathways into a tourism future where the limitations of human learning capacities could be compensated through the targeted support of general artificial intelligence (AI).

Customer engagement (customers’ behavioral manifestations going beyond customer-firm purchase transactions) has been regarded as strategic imperatives for generating enhanced corporate performance. The plethora of new media has provided customers with different options to interact with firms and other customers. However, the primacy of value-laden interactive customer relationships and value co-creation raises challenges for firms and customers, especially in the context of broader business ecosystems such as brand partnership for extending value co-creation. This study aims to explore how customer engagement with well-designed choreograph of various new media’s channels can increase the value co-creation extent in the context of broader business ecosystems, resulting in higher levels service offerings, experiences and innovation.

This exploratory study presents a new framework of customer engagement that holistically integrates the elements of multiple new media and broader business ecosystem, stimulating a virtuous circle of realizing customer engagement toward superior results or innovations. The framework considers new media’s different information service and technologies (e.g. search engine, social recommender, social media) that can be properly choreographed to achieve a virtuous customer engagement circle.

This paper uses an exemplar framework's instantiation – an information technology enabled engagement platform (called iEngagement) – that can demonstrate how to empower the central companies together with their eco-stakeholders to holistically perform customer engagement utilizing new media toward fruitful customer engagement.

This exploratory study is among the first that addresses the theory and practice of customer engagement within multiple new media and broader business ecosystem. This paper presents a customer engagement framework and an exemplified engagement platform that holistically integrate the elements of multiple new media and broader business ecosystem, for stimulating a virtuous circle of realizing customer engagement toward superior results or innovations.

This chapter explores the role of artificial intelligence (AI), particularly its subfield of machine learning (ML) methods, as a core technology of the fintech revolution in the financial services industry. It simplifies some of the complex concepts related to AI by introducing the main ML paradigms and related techno-methodic aspects. This chapter uses real-world examples to illustrate how next-generation AI powered by ML is transforming the financial services industry. Next, in illustrating the risks associated with AI adoption, this chapter discusses the need for regulation to address the essential facets of AI governance, including transparency, accountability, ethics, and responsible use. Lastly, it looks at emerging regulatory approaches across leading global jurisdictions. The primary goal is to give readers an initial understanding of AI's profound impact on the financial sector.