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Generative artificial intelligence has moved rapidly from experimental use to practical adoption across digital business and management contexts. Its diffusion has been accelerated by large language models, image generators, code generators, and conversational systems that can support content creation, analysis, automation, and decision support. This systematic review examines the evidence on Generative AI in business and management studies, with particular attention to productivity, decision quality, governance, and organizational risk. The review addresses the need for a balanced synthesis that recognises both the performance promise of Generative AI and the risks created by its probabilistic, opaque, and adaptive nature. The findings show that Generative AI can improve productivity by reducing task completion time, expanding output volume, supporting creative work, and assisting knowledge workers. However, the evidence also indicates uneven benefits across tasks, expertise levels, organizational contexts, and governance conditions, while decision quality remains vulnerable to hallucination, bias, over-reliance, and weak accountability. The review concludes that Generative AI should be understood not merely as a productivity technology but as an organizational transformation phenomenon. Its business value depends on the co-development of human oversight, governance structures, risk controls, workforce capabilities, and context-sensitive implementation practices.

Subscription-based digital services have become a central business model across software, streaming, membership platforms, curated commerce, replenishment services, and digitally mediated retail. Their appeal rests on replacing irregular transactions with continuing customer relationships that generate recurring revenue, richer behavioural data, and stronger possibilities for long-term engagement. Despite this growth, the literature on digital subscriptions remains fragmented. Studies often examine subscription architecture, customer lifetime value, churn prediction, or engagement separately, which limits understanding of how revenue generation, retention management, and value renewal interact as a connected system. The review shows that subscription performance cannot be understood through acquisition growth or churn reduction alone. Sustainable subscription management depends on aligning revenue metrics, customer experience, predictive retention systems, and ongoing value creation across the subscriber lifecycle. The article argues for an integrated view of digital subscription strategy. Future research must connect financial logic with behavioural retention and value renewal, while managers must move beyond reactive churn control toward proactive lifecycle governance.

Digital self-service systems have become central to contemporary digital business, reshaping how customers access, perform, and evaluate service tasks. Across retail, hospitality, banking, healthcare, transport, and platform-based services, organisations increasingly rely on kiosks, mobile apps, chatbots, online portals, and automated interfaces to move routine activities from employees to customers. This shift changes the service encounter from an employee-led interaction into a digitally mediated process in which customers are expected to participate more actively. The objective of this scoping review is to map the literature on digital self-service business systems through three interconnected dimensions: customer autonomy, service efficiency, and managerial control. Rather than treating self-service technologies only as technical tools, the review frames them as business systems that redistribute work, responsibility, and decision-making among customers, employees, and managers. The review therefore focuses on how digital self-service systems create value, where they create friction, and how firms attempt to govern service quality at scale. The review follows a scoping synthesis approach informed by PRISMA-ScR principles. Some peer-reviewed journal articles were included to capture recent developments in self-service technology, service automation, customer participation, chatbot service, frontline technology, service robots, and scoping review methodology. The synthesis charts dominant technology types, service contexts, customer outcomes, operational claims, and managerial control mechanisms. The review contributes a structured map of the digital self-service literature and identifies gaps in how autonomy, efficiency, and control are studied together. Existing research is rich but fragmented, with many studies concentrating on customer acceptance, service failure, or specific technologies rather than integrated business-system governance. Future research should examine how firms can design self-service systems that balance customer agency, operational productivity, ethical responsibility, and service quality assurance.

Digital firms increasingly operate through more than one revenue logic. Freemium tiers, subscriptions, transaction fees, marketplace commissions, advertising, and data-enabled monetization often coexist within the same business model. This multiplicity has become central to business model innovation in digital environments. Despite this practical reality, the literature on digital revenue models remains fragmented. Freemium research often focuses on conversion, subscription research on retention, marketplace research on platform transactions, and data monetization research on privacy and value extraction. As a result, the combined strategic role of these revenue logics remains insufficiently integrated. The review identifies four major revenue logics: freemium and subscription logic, marketplace and transaction-based logic, data-enabled revenue logic, and portfolio-level diversification logic. It shows that these logics can complement one another by linking acquisition, retention, transaction volume, personalization, and customer lifetime value. However, it also finds tensions involving cannibalization, privacy risk, platform governance, customer fairness, and strategic complexity. The review concludes that digital revenue diversification should not be treated as the simple addition of revenue streams. It is a deliberate process of designing, aligning, and governing multiple monetization mechanisms within a coherent business model. Future research should examine how firms configure, evolve, and govern multi-logic revenue portfolios over time.

Digital pricing has become a central managerial capability in online business models, where firms increasingly move beyond fixed prices toward dynamic, algorithmic, and personalised pricing systems. This shift reflects the growing availability of behavioural data, competitive intelligence, platform analytics, and machine learning tools that allow prices to be adjusted more frequently and precisely. However, the same capabilities that support revenue optimization also intensify concerns about fairness, transparency, and customer trust. This systematic review examines how digital pricing management has been studied across pricing strategy, revenue management, consumer psychology, retailing, tourism, platform economics, and algorithmic governance. The review focuses on three connected themes: dynamic pricing and revenue optimization, customer fairness and trust perceptions, and managerial control of automated pricing practices. The aim is to clarify what is known, where evidence remains fragmented, and how future research can integrate performance and governance perspectives. Following a PRISMA-informed approach, the review synthesises peer-reviewed journal articles published between. The selected evidence includes conceptual, empirical, modelling, experimental, and review-based contributions relevant to digital pricing in online and data-rich business environments. The review does not introduce new empirical data but systematically collates and interprets existing research. The review concludes that digital pricing management should be understood as both a revenue optimization system and a trust-sensitive managerial practice. Firms need pricing architectures that combine analytical precision with transparency, accountability, and human oversight. Future research should develop integrated models that examine profitability, fairness, governance, and long-term customer relationships together rather than as separate research streams.

Firms increasingly manage portfolios of digital channels that include direct-to-consumer storefronts, third-party marketplaces, social commerce environments, and digitally enabled partner channels. Each channel provides different opportunities for reach, margin capture, customer engagement, and data access. Yet each also introduces different governance demands because the firm does not exercise equal control across all digital routes to market.The central problem addressed in this article is that many firms expand their digital channel presence faster than they develop governance systems capable of coordinating these channels. As a result, channel conflict, inconsistent pricing, fragmented customer data, brand dilution, and misaligned partner incentives become recurring managerial problems. These issues are especially acute when channels simultaneously cooperate and compete for customers, revenue, information, and strategic attention.The objective of this article is to develop the Digital Channel Governance Framework as a conceptual tool for managing diverse digital channels in a coordinated manner. The framework is designed for firms operating across direct-to-consumer, marketplace, social commerce, and partner channels. It treats digital channel management not as a collection of separate tactical decisions but as an integrated governance problem.The framework shows that effective digital channel governance requires balancing channel-specific autonomy with portfolio-level alignment. It defines governance requirements for direct-to-consumer, marketplace, social commerce, and partner channels, and it proposes mechanisms for pricing consistency, data integration, brand control, incentive alignment, and conflict resolution. The contribution is a practical and conceptual framework for moving from siloed digital channel management toward integrated channel portfolio governance.

Modern digital work environments increasingly fracture tasks across email, instant messaging, project platforms, customer systems, shared documents, dashboards, and video meetings. This article conceptualises this condition as digital task fragmentation, defined as the systemic dispersion of work activities across multiple digital tools, communication channels, and organizational interfaces. Unlike traditional interruption or individual multitasking, digital task fragmentation is embedded in the architecture of contemporary work systems. Existing theories of work design, coordination, information processing, and managerial attention explain important aspects of task demands, communication complexity, and cognitive strain. However, they do not fully capture how digitally mediated work fragments tasks across platforms while simultaneously making workload harder to see, sequence, and govern. The result is a growing theoretical gap between how work is formally designed and how it is actually experienced in digitally intensive organizations. The objective of this article is to develop a theory-driven model of digital task fragmentation and its organizational consequences. The model links fragmentation to workload dispersion, coordination costs, managerial attention depletion, and downstream outcomes for productivity, employee well-being, decision quality, and organizational agility. The article positions fragmentation as both a work-design problem and an attention-allocation problem. The article contributes by naming and theorising digital task fragmentation as a distinct organizational phenomenon. It shows how tool proliferation, always-on communication, and hybrid work arrangements disperse tasks and increase hidden coordination burdens. The proposed framework provides a basis for future measurement, empirical testing, and managerial redesign of digital work systems.

Digital service failures have become routine features of contemporary markets, yet their consequences are rarely routine for customers. Platform outages, data breaches, personalisation errors, failed transactions, and online service breakdowns can quickly damage customer confidence because they interrupt access, create uncertainty, and expose the fragility of digital relationships. Traditional service recovery models provide important foundations, but they do not fully address the speed, visibility, scale, and technical opacity of digital failure. The central problem addressed in this article is that digital service failures differ from face-to-face or phone-based failures in both mechanism and meaning. A late employee apology or a replacement offer may work in a human-delivered service encounter, but digital failures often involve thousands or millions of affected users, automated systems, sensitive data, and unclear accountability. These characteristics require a dedicated conceptual model that connects failure type to recovery response. The objective of this article is to propose the Digital Service Recovery Model. The model explains how firms can restore customer confidence after three core categories of digital service failure: platform failures, data errors, and online service breakdowns. It argues that recovery effectiveness depends on matching the recovery strategy to the failure mechanism rather than applying a generic service recovery script. The resulting model identifies failure triggers, recovery strategy selection, confidence restoration pathways, and feedback loops for systemic improvement. It shows that rapid response, transparent communication, tangible redress, personalised reassurance, and demonstrable technical fixes are not separate tactics but interdependent recovery capabilities. The article contributes a practical and forward-looking framework for firms operating in the digital trust economy.

Modern businesses increasingly depend on a wide array of digital vendors, including SaaS applications, payment gateways, analytics platforms, and outsourced digital services. These vendors no longer sit at the periphery of operations; they shape how firms sell, serve, analyse, automate, and innovate. As digital transformation deepens, the vendor landscape becomes more complex, distributed, and strategically consequential. Many firms still manage digital vendors through fragmented ownership structures, with procurement, IT, finance, marketing, operations, and business units each controlling different vendor relationships. This siloed approach produces integration debt, uncontrolled spending, duplicated functionality, weak renewal discipline, and fragmented data flows. It also increases dependency on external platforms and service providers whose pricing, APIs, data policies, and continuity risks can directly affect firm performance. The objective of this article is to develop a Digital Vendor Portfolio Framework that enables firms to coordinate and govern all digital vendors as an integrated strategic portfolio. The framework treats digital vendors not as isolated contracts but as interdependent assets, risks, and capabilities. It provides a governance logic for mapping vendor roles, identifying dependencies, monitoring performance, and aligning external digital resources with business strategy. The proposed framework addresses coordination for SaaS providers, payment platforms, analytics tools, and outsourced digital services. It identifies governance mechanisms for each vendor category and outlines how portfolio mapping, contractual safeguards, technical integration, relational governance, and performance dashboards can reduce complexity. The article argues that proactive digital vendor portfolio management is now a strategic imperative for firms seeking efficiency, data integrity, resilience, and control.

Organizations have enthusiastically adopted a multitude of digital tools, from messaging platforms and project management suites to cloud repositories, dashboards, video conferencing systems, customer relationship management platforms, and workflow automation applications. These technologies are usually introduced with the promise of faster collaboration, improved visibility, and greater organizational efficiency. Yet the practical experience of many employees is increasingly defined by fragmented attention, overlapping systems, and an expanding burden of digital coordination. This perspective argues that digital tool proliferation produces hidden managerial costs that remain largely invisible in conventional performance measurement. These costs include workflow duplication, communication noise, employee switching costs, and productivity leakage. Rather than treating tool expansion as a neutral sign of modernization, the article frames uncontrolled tool sprawl as a management failure that silently erodes organizational performance. The objective is to expose the invisible productivity tax created when organizations add digital tools without sufficient governance, consolidation, or attention-design principles. The article challenges the common assumption that each additional tool improves productivity by solving a discrete operational problem. It instead argues that local tool benefits can accumulate into system-wide friction when managers fail to consider the total digital work environment. The article catalogues the manifestations and organizational impacts of digital tool proliferation, explains how duplication and noise create a hidden coordination tax, and outlines how switching costs accumulate into productivity leakage. It also provides practical recommendations for managers seeking to audit, rationalize, and govern their digital tool landscapes. The central conclusion is that digital tools should be treated as a strategic work-design resource rather than as an endlessly expandable productivity solution.

Digital business has expanded the number and variety of revenue models through which firms capture value from software, platforms, data, digital services, and online ecosystems. Subscription, freemium, marketplace, pay-per-use, licensing, and data-enabled models are now widely used across sectors, but their conceptual boundaries are often blurred. This makes it difficult for researchers and managers to compare models systematically. The problem addressed in this article is the lack of a clear taxonomy for distinguishing digital revenue models in business management. Existing terminology often mixes pricing mechanisms, business model architectures, customer access rights, platform intermediation, and data monetization under overlapping labels. As a result, revenue model analysis may become imprecise, fragmented, or overly case-specific. The objective of this article is to develop a rigorous taxonomy of digital revenue models based on explicit classification criteria. The taxonomy classifies digital revenue models into six categories: subscription, freemium, marketplace, pay-per-use, licensing, and data-enabled models. These categories are differentiated according to revenue generation logic, payment structure, value unit, customer relationship, scalability mechanism, and governance requirement. The resulting taxonomy provides definitions, sub-types, comparative criteria, and governance implications for each model. It contributes a shared vocabulary for studying digital revenue strategies and offers managers a practical tool for designing, combining, and governing revenue portfolios. The article concludes that digital revenue model selection should be treated not only as a monetization choice but also as a strategic governance decision.

In digital markets, customer exit has become faster, quieter, and more difficult to reverse. Customers can reduce usage, compare alternatives, migrate to competitors, or cancel subscriptions with minimal friction. Despite this reality, many firms still allocate disproportionate managerial attention to acquisition rather than structured exit management. Existing research offers important insights into customer churn prediction, switching behaviour, engagement, loyalty programmes, service recovery, and win-back campaigns. However, these streams are often treated as separate domains rather than as connected stages in a customer exit process. This fragmentation limits managers’ ability to detect early churn signals, interpret switching intentions, and deploy retention interventions at the right time. This article proposes the Digital Customer Exit Management Framework as an original conceptual framework for digital customer retention. The framework links observable churn signals, psychological and contextual switching intentions, and targeted retention interventions into a unified process model. It positions customer exit not as a single cancellation event but as a dynamic trajectory that can be anticipated, interpreted, and influenced. The article argues that proactive exit management is a strategic capability for digital businesses. By moving from reactive retention to early signal detection and intervention alignment, firms can reduce avoidable churn, protect customer lifetime value, and improve the quality of customer relationship management. The framework provides a structured roadmap for managers and a foundation for future empirical testing.

Digital growth and digital transformation are often treated as interchangeable signs of managerial success, yet they describe fundamentally different organizational realities. Digital growth refers to visible expansion in users, transactions, revenue, traffic, and market reach. Digital transformation refers to deeper changes in organizational capabilities, processes, culture, data use, and strategic logic. This article argues that the distinction is not semantic but managerial. The problem is that firms, boards, and investors frequently reward digital growth as though it were proof of transformation. Rapid increases in customer acquisition, app usage, online revenue, or platform participation can create the impression that a firm has become digitally mature. Yet such growth may occur while the organization remains operationally fragile, culturally analogue, technically debt-laden, and strategically dependent on external platforms or paid acquisition channels. The objective of this viewpoint article is to disentangle digital growth from digital transformation and to show why the confusion matters for strategic management. It develops the argument that digital growth can be accelerated through scaling mechanisms, whereas digital transformation requires slower and more difficult capability building. The article therefore challenges the dominant managerial habit of treating growth dashboards as transformation evidence. The article contributes a practical distinction between growth logic and transformation logic. It shows that rapid scaling can coexist with weak transformation, that customer acquisition can mask organizational underdevelopment, and that operational strain often remains invisible until growth slows. The central conclusion is that leaders must measure what they transform, not only what they grow.

Firms increasingly pursue digital channel expansion through online marketplaces, social commerce, direct-to-consumer platforms, mobile applications, and digitally integrated retail ecosystems. These channels promise broader market reach, faster customer acquisition, richer data capture, and new revenue opportunities. Yet expansion into a new channel is not automatically equivalent to strategic readiness. The managerial challenge is to determine whether the firm is prepared to scale the channel without weakening existing commercial, operational, or brand systems. Ad-hoc digital channel expansion can produce several unintended consequences. Firms may overestimate market demand, underestimate customer acquisition costs, or duplicate channels that already serve overlapping segments. They may also trigger conflict with distributors, franchisees, retailers, or internal sales teams. When operational systems cannot absorb added complexity, the new channel may expose fulfilment delays, service gaps, inventory problems, and inconsistent customer experiences. This article develops an original decision framework for evaluating digital channel expansion readiness. The framework integrates four assessment pillars: market reach potential, channel conflict risk, operational capacity, and brand consistency. It is designed as a practical managerial tool rather than an empirical prediction model. Its purpose is to help decision makers move from opportunity enthusiasm toward structured readiness evaluation. The central argument is that digital channel expansion should be treated as a readiness decision, not merely a growth initiative. A structured assessment can reduce the likelihood of channel conflict, operational breakdown, brand inconsistency, and under-realised market potential. The framework provides managers with a disciplined way to compare opportunity attractiveness against internal preparedness. It also creates a foundation for future empirical testing and refinement.

Online businesses increasingly rely on third-party digital payment providers to process customer payments, authenticate transactions, manage settlement, handle disputes, and enable scalable digital commerce. This reliance is often treated as a technical or operational arrangement rather than as a strategic dependency embedded in the firm’s revenue architecture. Yet payment dependence can shape how firms access transaction data, control customer experience, manage cash flow, and respond to disruptions.The central problem addressed in this article is that digital payment providers simultaneously enable and constrain online business ecosystems. They make commerce faster, more secure, and more scalable, but they also introduce external control points into the customer journey and revenue process. When these control points are poorly governed, firms may experience reduced transaction visibility, checkout friction, settlement uncertainty, and exposure to provider decisions.This article proposes the Digital Payment Dependence Model as an original conceptual framework for understanding how dependence on external payment infrastructures creates three interconnected strategic risks. These are loss of transaction control, customer friction at checkout, and revenue vulnerability. The model explains how these risks reinforce one another and why payment dependence should be treated as a managerial concern rather than a back-office technology issue.The proposed model contributes to digital business and payment systems research by reframing payment infrastructure as a strategic dependency within online business ecosystems. It shows that payment governance is not limited to transaction fees or technical uptime, but extends to control, experience design, and revenue continuity. The article concludes that managers should govern payment systems as strategic assets whose failure, concentration, or misalignment can directly threaten business resilience.

Digital personalization has become central to contemporary business management because firms increasingly use customer data, predictive analytics, and automated service systems to tailor experiences, recommendations, communications, and offers. When designed well, personalization can increase relevance, reduce search effort, improve service convenience, and strengthen customer relationships. Yet the same practices can become intrusive when customers feel excessively tracked, profiled, or targeted without meaningful control.The central problem addressed in this article is that personalization is often managed as a performance instrument rather than as a responsible customer relationship practice. Many firms evaluate personalization through conversion rates, engagement metrics, or transaction outcomes, while privacy expectations, service quality perceptions, and trust consequences remain secondary. This creates a managerial blind spot because personalization can generate short-term effectiveness while quietly weakening long-term trust.This article proposes a Responsible Digital Personalization Framework for business management. The framework integrates four interdependent pillars: customer relevance, privacy expectations, service quality, and brand trust. It argues that responsible personalization is not achieved by reducing personalization but by governing how, when, why, and with what customer data personalization is delivered.The framework contributes by repositioning personalization as a strategic design choice rather than a purely technical capability. It shows that customer relevance and service quality must be pursued within privacy-respecting and trust-building boundaries. Responsible digital personalization therefore becomes a source of durable customer value, not merely a mechanism for immediate targeting efficiency.

Social commerce has transformed digital competition by blending product discovery, peer interaction, creator influence, entertainment, and transaction into a single platform-mediated experience. Unlike traditional e-commerce, where firms compete through search visibility, website efficiency, pricing, and fulfilment reliability, social commerce shifts competition into dynamic social environments where attention, trust, and participation are continuously produced. This perspective argues that existing digital business strategy frameworks do not fully capture the competitive logic of social commerce. Strategies designed around owned websites, paid advertising, email conversion funnels, and transactional optimisation remain important, but they are no longer sufficient when consumer decisions are shaped inside algorithmic content feeds, creator communities, and live interactive selling events. The article develops an evidence-based perspective on how digital firms compete through three emerging social-relational capabilities. These are creator partnerships that produce authentic influence, community engagement that generates belonging and loyalty, and live selling that integrates entertainment, interaction, urgency, and immediate purchase. The core conclusion is that social commerce should not be treated as a minor extension of digital marketing. It represents a new competitive battleground in which firms must build capabilities for managing creators, cultivating communities, designing live interaction, and converting social engagement into strategic advantage.

Firms now collect vast quantities of customer, operational, and transactional data through digital platforms, enterprise systems, service encounters, logistics infrastructures, and payment architectures. Yet much of this data is used only for its immediate operational purpose and then stored, fragmented, or ignored. This creates a strategic paradox: data abundance does not automatically produce value abundance. Existing theories of digital business value have largely emphasised data collection, analytics capability, governance, and decision support. These perspectives explain how firms analyse data for predefined purposes, but they do not fully explain how firms systematically repurpose existing data assets for new uses. This leaves an important gap in understanding how data becomes renewable rather than merely accumulated. This article conceptualises enterprise data reuse as a distinct digital business capability. It defines data reuse as the organisational capacity to identify data already collected, prepare it for a new context, recombine it with other data assets, and apply it to new business problems, products, services, or strategic insights. The article develops a theory of how customer, operational, and transactional data can be repurposed to support value renewal. The article contributes by positioning data reuse as a higher-order capability rather than a secondary analytics activity. It argues that firms able to renew the value of existing data can generate new revenue opportunities, reduce the marginal cost of innovation, and strengthen strategic adaptability. Enterprise data reuse therefore transforms data from a one-time input into a renewable strategic asset.