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The best value initiative has been designated as the key framework for improving service quality and effectiveness in UK local government. In support of the framework a number of measures have been introduced to measure effectiveness. However, there are problems with using comparative performance measures within the diverse grouping of local government services. Aims to measure the effectiveness of best value in a similar group of building control services by using a clustered benchmarking approach. The mainly qualitative research involves applying a strategic benchmarking approach to the 26 units or councils within the cluster and then comparing the cluster with the best practice UK measures for building control services. To facilitate the process a benchmarking measurement framework for building control was developed. The results indicate that the clustering approach is more beneficial than comparing the service of a single unit with best practice. Practices comparable with best practice for key measures can be identified for individual units or councils within the cluster. Moreover, there are more opportunities for generalising the findings and developing measurement frameworks within clusters. The cluster can internalise the benchmarked findings through mutually supportive programmes.

The purpose of this paper is to differentiate traditional industrial clusters from emerging consumption‐oriented and destination‐based regional service clusters (e.g. entertainment, service‐based tourism, and convention destination); to develop an analytical framework to examine the impacts of both cluster and network effects in service clusters; and to offer implications for managing service innovation to enhance regional development and global competitiveness.

By applying industrial cluster, strategic network, and value creation theories, this research develops a framework to study value creation in service clusters.

The research concludes that firms' decisions to enter or exit a service cluster depends on the net strategic effects, which is the sum of agglomeration economies (i.e. cluster effects) and the economies of network (i.e. network effects).

Further empirical research is needed to determine the significance of both cluster and network effects that determine firms' decisions to enter the service clusters.

First, firms need to incorporate cluster‐specific elements, such as experience economy, value networking, and service innovation to maximize the consumer's use value or perceived customer benefits. Second, in order to increase consumers' willingness to pay a higher price, firms located inside the service clusters need to adopt strategies, such as product or service differentiation, that increase the perceived benefits or use value of customers. Third, service cluster firms' other objectives should be to minimize location‐specific cost differentials by capitalizing on the positive effects of both agglomeration economies and economies of network. Fourth, public policy implications need to include laws and regulations that encourage and promote innovation and new ventures creation, competition in the marketplace, and capital investment to maximize value creation. Finally, policy makers need to promote high value‐added economic activities in the service clusters in order to maximize consumer welfare.

A growing number of policymakers and scholars have recognized the significant economic contributions from the consumption‐oriented regional clusters. This paper is the first effort to develop an analytical framework to study value creation in service clusters for effective managerial and policy decisions.

This study profiles and segments potential tourists on the basis of their various attitudes toward artificial intelligence (AI) services. Furthermore, this study distinguishes descriptors among the different clusters, such as preference for using diverse AI services, overall image of AI services, willingness to use AI services (WUAI), willingness to pay more for AI services (WPAI) in tourism and hospitality, and characteristics of respondents.

An online survey was conducted in South Korea. Data on 758 potential tourists were used for K-means cluster analysis.

This study identified three distinct tourist segments with differentiated attitudes toward AI services: the group aspiring to use or fantasizing about AI services (Cluster 1), the group being knowledgeable and supportive of AI services (Cluster 2), and the group having low interest about AI services (Cluster 3).

Members of Cluster 2 were the most marketable as this segment exhibited the greatest knowledge of and support for AI services, while Cluster 1 would be an ideal segment to launch and test novel AI services.

This study extends the authors’ knowledge of AI scholarship by unpacking the existing market segments, which could be tapped to sustain AI penetration in the tourism industry. Hence, this study contributes to existing debates on AI scholarship, which is predominated by conceptual reflections and issues of AI services in the tourism and hospitality field.

Journal of services marketing (JSM) is a leading journal that has published cutting-edge research in services marketing over the past 34 years. The main objective of this paper is to provide a retrospective of the thematic structure of papers published in JSM over its publication history.

This study uses bibliometric methods to present a retrospective overview of JSM themes between 1987 and 2019. Using keywords co-occurrence analysis, this paper unveils the thematic structure of JSM’s most prolific themes. Bibliographic coupling analysis uncovers the research trends of the journal.

Leading authors, leading institutions, authors’ affiliated countries and critically, the dominant themes of JSM are identified. As its founding, JSM has published approximately 40 papers each year, with 2019 being its most productive year. On average, lead JSM authors to collaborate with 1.30 others. Keywords co-occurrence analysis identifies nine prominent thematic clusters, namely, “marketing to service”, “quality, satisfaction and delivery systems”, “service industries”, “relationship marketing”, “service failure, complaining and recovery”, “service dominant logic”, “technology, innovation and design”, “wellbeing” and “service encounters”. Bibliographic coupling analysis groups JSM papers into four clusters, namely, “brand & customer engagement behaviour”, “service co-creation”, “service encounters & service recovery” and “social networking”.

This study is the first to analyse the thematic structure of JSM themes over its history. The themes are analysed across time periods and then compared to dominant themes identified in contemporary service research agendas. Recommendations are made based on the gaps found. This retrospective review will be useful to numerous key stakeholders including the editorial board and both existing and aspiring JSM contributors. The selection of literature is confined to Scopus.

JSM’s retrospection is likely to attract readership to the journal. The study’s recommendations regarding which areas have matured and which are still ripe for future contributions will offer useful guidelines for all stakeholders.

This study aims to extract determinants of customers' choice of dining-related services (CDS) to understand their attitudes and habits for dining-related services. Moreover, it classifies restaurant customers into several clusters based on their CDS factors and further analyzes the differences in the preferences and habits for dining-related services among various clusters.

This study extracts the CDS factors using principal component analysis and identifies construct validity of the CDS factors through the results of confirmatory factor analysis. Then, it divides the restaurant customers in Taipei City into different clusters through cluster analysis to explore the habits and preferences for dining-related services in each cluster.

The study results show that the extracted six CDS factors, including service commitment, dietary preferences, design styles, additional value, delicate information collection, and dining environments. Dietary preferences, service commitment and additional value factor are the most important CDS factors for restaurant customers in Taipei City. This study divides 1,029 restaurant customers in Taipei City into three clusters based on the CDS factors through cluster analysis. The differences in the preferences and habits for dining services among three clusters have been discussed in the context in detail. Finally, this study develops eight service strategies and determines the order of execution of the eight service strategies for each cluster of customers.

The findings will help restaurant operators segment the market, target market customers, carry out service positioning and develop marketing strategies to respond to the highly competitive restaurant market and achieve sustainable restaurant operations.

The purpose of this paper is to explore the emergence of innovation in smart service systems to conceptualize how actor’s relationships through technology-enabled interactions can give birth to novel technologies, processes, strategies and value. The objectives of the study are: to detect the different enablers that activate innovation in smart service systems; and to explore how these can lead dynamically to the emergence of different innovation patterns.

The empirical research adopts an approach based on constructivist grounded theory, performed through observation and semi-structured interviews to investigate the development of innovation in the Italian CTNA (Italian acronym of National Cluster for Aerospace Technology).

The identification and re-elaboration of the novelties that emerged from the analysis of the Cluster allow the elaboration of a diagram that classifies five different shades of innovation, introduced through some related theoretical propositions: technological; process; business model and data-driven; social and eco-sustainable; and practice-based.

The paper embraces a synthesis view that detects the enabling structural and systems dimensions for innovation (the “what”) and the way in which these can be combined to create new technologies, resources, values and social rules (the “how” dimension). The classification of five different kinds of innovation can contribute to enrich extant research on value co-creation and innovation and can shed light on how given technologies and relational strategies can produce varied innovation outcomes according to the diverse stakeholders engaged.

The purpose of this study is to utilize a configuration approach to examine the relationships between multiple contingent variables and management control systems (MCS) in service organizations from various industries. The contingent variables include service process type, cost leadership and differentiation strategies, environmental unpredictability, change and complexity and organizational life cycle stage.

Questionnaires were administered to the financial controllers of service organizations operating in Australia. Cluster analysis is utilized to reveal the MCSs fit.

The data reveals that high-performing service organizations are those firms that place high emphasis on a differentiation strategy. The lower performers, on the other hand, are firms with a lack of emphasis on both cost leadership and differentiation strategies. Overall, the data demonstrates that strategic orientation plays an important role in high-performing organizations and the ability to incorporate various contingent situations determines the effectiveness of an organization.

The potentially broad framework offered in this study allows managers from various service industries to relate the variations in the combination of situations to those of their firms. The service managers’ experience with certain type of combinations can assist them to identify the strategic position of their respective organizations. Consequently, service managers can estimate the prospect for further development to enhance the firm performance.

This study offers a holistic view of the multiple and simultaneous relationships between contingent factors and MCS design in service organizations using a configuration approach. This paper contributes to the literature on organizational configurations in particular by studying the systematic nature of effective MCSs in service organizations when adapting to the contextual settings of the organizations.

Needs of people following acquired brain injury vary over their life-course presenting challenges for community services, especially for those with “hidden” neuropsychological needs. Characterisation of subtypes of rehabilitation service user may help improve service design towards optimal targeting of resources. This paper aims to characterise a neuropsychologically complex group of service users.

Preliminary data from 35 participants accepted for a holistic neuropsychological rehabilitation day programme were subject to cluster analysis using self-ratings of mood, executive function and brain injury symptomatology.

Analysis identified three clusters significantly differentiated in terms of symptom severity (Cluster 1 least and Cluster 2 most severe), self-esteem (Clusters 2 and 3 low self-esteem) and mood (Cluster 2 more anxious and depressed). The three clusters were then compared on characteristics including age at injury, type of injury, chronicity of problems, presence of pre-injury problems and completion of rehabilitation. Cluster 2 were significantly younger at time of injury, and all had head injury.

Results suggest different subgroups of neuropsychological rehabilitation service user, highlighting the importance of early identification and provision of rehabilitation to prevent deterioration, especially for those injured when young. Implications for design of, and research into, community rehabilitation service design for those with “hidden disability” are considered.

The paper findings suggests that innovative conceptual frameworks for understanding potentially complex longer term outcomes are required to enable development of tools for triaging and efficient allocation of community service resources.

The purpose of this paper is to provide a clustering approach to segment supply chain partners in the automobile industry and prioritize services offered by third party logistics service (3PL) providers.

In total, 98 automobile and auto‐parts manufacturers are surveyed to identify service needs, preferences, and outsourcing commitments. By applying a two‐stage clustering approach combined with Ward's minimum‐variance method and the K‐means algorithm, the logistics companies prioritize their services to better satisfy groups of customers with specific preferences.

Four distinctive groups of manufacturers are identified using the two‐stage clustering approach. The clusters separate logistic preferences and outsourcing patterns of after market parts suppliers, original equipment service parts suppliers, original equipment manufacturer parts suppliers, and tier one car makers. The paper finds that distribution and delivery services hold the highest percentage of services outsourced among the manufacturers.

This paper models logistic services as customizable services and develops a data system methodology to define the profiles of automobile manufacturers and their preferred logistic services. Through the analysis of service preferences and clustering, the paper identifies the key logistic services that can be customized for members of the automobile supply chain. A case is provided which demonstrates how a logistics company can provide customized service designs for specific target markets and customers.

This research aims to comprehend the factors that impact the emerging inclination of consumers toward mobile finance technology (fintech) services over banking institutions. The study focuses on users' demographics and psychographics to delineate their unique segments and profiles.

The study proposes a segmentation and profiling framework that includes variance analysis, two-step cluster analysis and pairwise statistical tests. This framework is applied to a dataset of customers using a range of mobile fintech services, specifically robo-investment, peer-to-peer (P2P) payments, robo-advisory and digital savings. The analysis creates distinct customer profile clusters, which are later validated using pairwise statistical tests based on segmentation output.

Empirical results reveal that P2P payment service users exhibit a higher frequency of usage, proficiency and intention to continue using the service compared to users of robo-investment or digital savings platforms. In contrast, individuals utilizing robo-advisory services are identified to have a significantly greater familiarity and intention to sustain engagement with the service compared to digital savings users.

The findings provide financial institutions, especially traditional banks with actionable insights into their customer base. This information enables them to identify specific customer needs and preferences, thereby allowing them to tailor products and services accordingly. Ultimately, this understanding may strategically position traditional banks to maintain competitiveness amidst the increasing prominence of fintech enterprises.

This research provides an in-depth examination of customer segments and profiles within the mobile fintech services sphere, thus giving a nuanced understanding of customer behavior and preferences and generating practical recommendations for banks and other financial institutions. This study thereby sets the stage for further research and paves the way for developing personalized products and services in the evolving fintech landscape.