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Mobile location-based service (m-LBS) seems like a new class of personalized service due to location positioning technologies. This work aims to investigate consumer readiness (RED) toward m-LBS based on integrating pull effect- and push effect-related factors into the technology acceptance model (TAM).

An online survey collected data from 423 participants, and the research framework was analyzed using structural equation modeling (SEM).

The results divulge that consumer RED is determined by TAM antecedents, including usefulness (USE) and ease of use (EOU). EOU motivates USE in m-LBS. Regarding pull effect-related factors, absorptive capacity (ABC) is the strongest positive factor influencing consumer RED to use m-LBS, followed by technology willingness (TWI) and innovativeness (INN). Moreover, INN, trust (TRU) and perceived risk (RIS) significantly influence USE and EOU.

This work endeavors to explicate customer RED toward m-LBS by incorporating some meaningful pull effect-related dimensions (i.e. ABC, TWI and INN) and pushing effect-related dimensions (i.e. RIS) into crucial antecedents rooted in TAM. Thus, the findings assist practitioners in developing marketing strategies by boosting pull effects and controlling push effects on customer engagement in m-LBS.

Today, customers’ perceived value does not only depend on the products, but also on the services provided by a firm. In e-commerce, it is important to shift the focus beyond the product and discuss the value of personalized services in the context of e-commerce fulfillment. Therefore, the purpose of this paper is twofold: to develop a conceptual framework proposing satisfaction through personalized services as a middle-range theory; and to suggest foundational premises supporting the theoretical framework, which in turn shape middle-range theory within the context of apparel e-commerce fulfillment.

In this theory-driven paper, the authors apply the scientific circle of enquiry, as it demonstrates the role of theorizing with the help of middle-range theory and empirical evidence and as such provides a methodological scaffolding that connects theory formulation and verification. The authors synthesize literature related to customer perceived value (CPV) and satisfaction, followed by abduction focusing on understanding the empirical domain as it occurred in practice from company cases. The presented case studies are based on semi-structured interviews with three Swedish online retailers within the apparel industry. The theory-driven analysis results in suggestions of foundational premises.

Based on the theoretical foundations and empirical generalizations, three propositions are suggested. The premises regarding satisfaction through personalized service applied in the domain of apparel e-commerce fulfillment are: to ensure customer satisfaction requires a value co-creation perspective using data during the pre-purchase phase; to ensure customer satisfaction and retention require added-value perspective during the post-purchase phase of the shopping journey; and to ensure satisfaction and convenience require an added-value perspective at the last mile.

The apparel firms lose a substantial amount of revenue because of poor online customer satisfaction, leading to e-commerce not reaching its full potential. To enhance customer value, online retailers need to find a resort in advanced technologies and analytics to address customer satisfaction, and it is suggested that retailers shift their focus beyond the products and find ways to improve personalized service offerings to gain market advantage, improve fulfillment, drive sales and increase CPV.

To consider personalized services as a source for improving e-commerce fulfillment and CPV, the main contribution of this study is conceptual as it presents a theoretical model developed from general theory, middle-range theory and verified with empirical claims.

Based on the case study of an SME company in the United Kingdom (which we will call SweetStar Cloud), this paper examines the attempts of the company to achieve significant strategic change. The company is attempting to move from being a tradition managed service provider of information services towards becoming a significant influencer in the market for digital services in the UK. As part of a knowledge transfer partnership (KTP), a local UK University has been closely involved in developing this new strategic direction and it is well poised to present and analyse the story. From the use of tried and tested strategic tools, including Porter's generic strategies and segmentation and targeting, the company has also embraced digital-specific approaches for developing partnerships with clients, developing pilot projects and experimenting with its use of social media. At the heart of this research is an analysis of the move from push marketing towards models of attraction. This paper aims to explore how traditional strategic tools are still applicable in the digital era alongside new tactical approaches in the digital sector. This aim has led to an approach to business that is responsible, in terms of moving away from a traditional push-selling model to one of partnership with customers at a strategic level. Strategy in dynamic markets often highlights responsiveness as a key success factor. The ability to respond (a response-ability) requires more agile companies. As SweetStar Cloud has developed its strategy, it has focused in achieving this more effective ability to respond through a more collaborative approach. In this sense, agile response-ability converges with business responsibility, as new abilities in communication, cooperation and trust development become key.

The proliferation of (big) data provides numerous opportunities for service advances in practice, yet research on using data to advance service is at a nascent stage in the literature. Many studies have discussed phenomenological benefits of data to service. However, limited research describes managerial issues behind such benefits, although a holistic understanding of the issues is essential in using data to advance service in practice and provides a basis for future research. The purpose of this paper is to address this research gap.

“Using data to advance service” is about change in organizations. Thus, this study uses action research methods of creating real change in organizations together with practitioners, thereby adding to scientific knowledge about practice. The authors participated in five service design projects with industry and government that used different data sets to design new services.

Drawing on lessons learned from the five projects, this study empirically identifies 11 managerial issues that should be considered in data-use for advancing service. In addition, by integrating the issues and relevant literature, this study offers theoretical implications for future research.

“Using data to advance service” is a research topic that emerged originally from practice. Action research or case studies on this topic are valuable in understanding practice and in identifying research priorities by discovering the gap between theory and practice. This study used action research over many years to observe real-world challenges and to make academic research relevant to the challenges. The authors believe that the empirical findings will help improve service practices of data-use and stimulate future research.

The purpose of this paper is to analyse the future of the implementation of artificial intelligence (AI) technologies in services experience provided by cultural institutions (e.g. museums, exhibition halls and cultural centres) from experts’, cultural tourists’ and users’ point of view under the Industry 5.0 approach.

The research was conducted using a qualitative approach, which was based on the analysis of the contents obtained from two roundtable discussions with experts and cultural tourists and users. A thematic analysis using NVivo was done to the data obtained.

From a futuristic Industry 5.0 approach, AI is considered to be more than a tool – it as an integral part of the entire experience. AI aids in connecting cultural institutions with users and is beneficial since it allows the institutions to get to know the users better and provide a more integrated and immersive experience. Furthermore, AI is critical in establishing a community and nurturing it daily.

The most important contribution of this research is the theoretical model focused on the user experience and AI application in services experiences of museums and cultural institutions from an Industry 5.0 approach. This model includes the visitors’ and managers’ points of view through the following dimensions: the pre-experience, experience and post-experience. This model is focused on human–AI coworking (HAIC) in museums and cultural institutions.

Healthcare policies around the globe are aimed at achieving patient-centeredness. The patient is understood as a prosumer of healthcare, wherein healthcare service co-production and value co-creation take center stage. The article endeavors to unpack the state of the literature on the innovations promoting the transition toward patient-centeredness, informing policy and management interventions fostering the reconceptualization of the patient as a prosumer of healthcare services.

A hybrid review methodology consisting of a bibliometric-interpretive review following the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol is used. The bibliometric component enabled us to objectively map the extant scientific knowledge into research streams, whereas the interpretive component facilitated the critical analysis of research streams.

Patient-centeredness relies on a bundle of innovations that are enacted through a cycle of patients' activation, empowerment, involvement and engagement, wherein the omission of any steps arrests the transition toward service co-production and value co-creation. Institutional, organizational and cognitive barriers should be overcome to boost the transition of patients from consumers to prosumers in a patient-centered model of healthcare.

The article delivers the state of the art of the scientific literature in the field of innovations aimed at sustaining the transition toward patient-centeredness and provides some food for thoughts to scholars and practitioners who wish to push forward service co-production and value co-creation in healthcare.

This paper aims to study the node modeling, multi-agent architecture and addressing method for the material conscious information network (MCIN), which is a large-scaled, open-styled, self-organized and ecological intelligent network of supply–demand relationships.

This study models the MCIN by node model definition, multi-agent architecture design and addressing method presentation.

The prototype of novel E-commerce platform based on the MCIN shows the effectiveness and soundness of the MCIN modeling. By comparing to current internet, the authors also find that the MCIN has the advantages of socialization, information integration, collective intelligence, traceability, high robustness, unification of producing and consuming, high scalability and decentralization.

Leveraging the dimensions of structure, character, knowledge and experience, a modeling approach of the basic information can fit all kinds of the MCIN nodes. With the double chain structure for both basic and supply–demand information, the MCIN nodes can be modeled comprehensively. The anima-desire-intention-based multi-agent architecture makes the federated agents of the MCIN nodes self-organized and intelligent. The MCIN nodes can be efficiently addressed by the supply–demand-oriented method. However, the implementation of the MCIN is still in process.

This paper lays the theoretical foundation for the future networked system of supply–demand relationship and the novel E-commerce platform.

The authors believe that the MCIN, first proposed in this paper, is a transformational innovation which facilitates the infrastructure of the future networked system of supply–demand relationship.

Currently, China’s economy is in the critical phase of transforming economic development patterns and replacing old growth drivers with new ones. Whether it can successfully overcome the “middle-income trap” has become a significant issue attracting wide attention.

Driven by underlying digital technologies such as artificial intelligence, blockchain, cloud computing and big data, the fourth industrial revolution featuring the booming digital economy has provided significant opportunities for China’s economy to “overtake” and overcome the “middle-income trap”. The transformation of economic development pattern, the optimization of industrial structure, and the change of growth drivers, brought by the deep integration of digital and real economies are the keys to leaping over the “middle-income trap”.

From the supply side, the digital economy can improve the quality and efficiency of the supply side and promote the supply-side structural reform and economic growth from the following three aspects: First, promote the quality, efficiency and diversification of the supply system; second, promote networking, opening-up and synergy in the innovation system and third, promote the socialization, modularization and flexibility of production pattern. From the demand side, the digital economy can boost the new drivers of the “troika” of economic growth consisting of consumption, exports and investment by changing the market investment direction, promoting consumption upgrade and fostering export strengths. However, once these two attributes interact with each other, especially when data is combined with capital, the most adhesive factor in the market economy, a series of new social relations will then be produced based on the technical attribute, resulting in significant adjustments in social relations, involving both positive and negative externalities.

To overcome the “middle-income trap”, it is necessary to adapt to the laws of economic evolution and promote a fundamental change in economic growth drivers; boost the high-quality development of the digital economy by strengthening the support role of data in the digital economy; and accelerate digital industrialization and industrial digitalization to realize the integration of digital and real economies.

This paper aims to propose an interpretive framework to understand how machine learning (ML) affects the way companies interact with their ecosystem and how the introduction of digital technologies affects the value co-creation (VCC) process.

This study bases on configuration theory, which entails two main methodological phases. In the first phase the authors define the theoretically-derived interpretive framework through a literature review. In the second phase the authors adopt a case study methodology to inductively analyze the theoretically-derived domains and their relationships within a configuration.

ML enables multi-directional knowledge flows among value co-creators and expands the scope of VCC beyond the boundaries of the firm-client relationship. However, it determines a substantive imbalance in knowledge management power among the actors involved in VCC. ML positively impacts value co-creators’ performance but also requires significant organizational changes. To benefit from VCC via ML, value co-creators must be aligned in terms of digital maturity.

The paper answers the call for more theoretical and empirical research on the impact of the introduction of Industry 4.0 technology in companies and their ecosystem. It intends to improve the understanding of how ML technology affects the determinants and the process of VCC by providing both a static and dynamic analysis of the topic.

The purpose of this paper is to better understand the future of hotel chains by exploring the evolving expectations of guests, potential innovations, emerging opportunities, and likely future scenarios.

A literature review consisting of current events, industry reports, and recent trends is utilized to summarize and categorize the challenges and opportunities facing hotel chains.

The future of hotel chains will be driven by the convergence of an increasingly competitive landscape, along with a diverse evolving customer base seeking out unique and individualized experiences. In order to survive in the future hotel chains will have to: listen to and learn from guests incorporating big data insights, go beyond segments to provide personalized services, continuously develop the brand through signature experiences, utilize collaboration and open innovation to maintain an edge in technology and service, and through total revenue management generate ancillary revenues and maximize guest spend.

This paper provides a comprehensive set of recommendations to hotel chains highlighting opportunities related to: financing, revenue generation, personalization, and co-creation.