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The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.

A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.

The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.

This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.

This paper studies the Digital Service Innovation (DSI) concept by systematically reviewing earlier studies from various scholarly communities. This study aims to recognize how recent advances in DSI literature from different research streams complement and can be incorporated into the growing digital servitization literature to define better and understand DSI.

After systematically identifying 123 relevant articles, this study employed complementary methods, such as author bibliographic coupling, linguistic text mining/textual analysis and qualitative content analyses.

This paper first maps the intellectual structure and boundaries of the DSI-related communities and qualitatively assesses their characteristics. These communities are (1) Innovation for digital servitization, (2) Service innovation in the digital age and (3) Adoption of novel e-services enabled by information system development. Next, the composition of the DSI concept is examined and depicted to comprehend the notion's critical dimensions. The findings discuss the range of theories and methods in the existing research, including antecedents, processes and outcomes of DSI.

This study reviews, extends the understanding of origins and critically evaluates DSI-related research. Moreover, the paper redefines and clarifies the structure and boundaries of the DSI-concept. In doing so, it elaborates on the substance of DSI and identifies the essential themes for its understanding and conceptualization. Thus, the study helps the future development of the concept and allows knowledge accumulation by bridging adjacent research communities. It helps researchers and managers navigate the foggy emerging research landscape.

The purpose of this paper is to develop a systematic literature review on the sunk cost effect from consumers’ perspectives. By applying a comprehensive approach, this paper aims to synthesise and discuss the impact of financial and behavioural sunk costs on consumers’ decisions, judgements and behaviour before and after purchasing. This study also identifies potential research avenues to inspire further studies.

Following a search in the Scopus and Web of Science databases, a systematic literature review was conducted by identifying and analysing 56 peer-reviewed articles published between 1985 and 2022 (November). Descriptive and content analysis was implemented based on the selected papers to examine and synthesise the effect of sunk costs on consumers’ choices, evaluations and actions in a comprehensive approach; uncover research gaps; and recommend paths for future research.

The research results found in the literature are discussed according to five related themes: factors affecting the sunk cost effect; the impact of past investments on purchasing decisions; consumers’ post-purchasing evaluation, behaviour and choices; the mental amortisation of price; and the sunk cost effect on loyalty and switching.

The originality of this study lies in the comprehensive approach to the sunk cost effect from consumers’ perspectives. This review paper synthesises and discusses the research results found in the literature related to financial and behavioural sunk costs that can influence consumers’ decisions, judgements and behaviour before and after paying for a good or service.

This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic.

In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling.

The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic; the largest ones are smart analytics, marketing strategies and sustainability.

To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis.

Employing a service-profit chain (S-PC) framework, this manuscript investigates the relationship between employee engagement (EE) and customer engagement (CE) within service contexts and explores how a mediating mechanism, service employee work performance (SEWP), links EE with CE.

Meta-analytic procedures ascertain the magnitude of the relationship between EE and SEWP (k = 102, ρ^ = 0.45) and between SEWP and three dimensions of CE: customer purchases (k = 42, ρ^ = 0.47), customer knowledge (k = 4, ρ^ = 0.33) and customer influence (k = 7, ρ^ = 0.42). The current meta-analysis reports an effect size for the EE-overall SEWP relationship nearly 1.50 times greater than related extant meta-analyses.

Results suggest SEWP, consisting of service employee task performance and contextual performance, serves as an important intervening mechanism between EE and CE by considering nine dimensions of SEWP. Such findings suggest that to maximize SEWP, service employees must go beyond simply being satisfied in their work roles; instead, service employees must feel energized, find fulfillment and meaning and be engrossed in their work to maximize the service they provide to customers.

This research extends previous meta-analytic efforts, bridges the multi-disciplinary gap between EE and CE research, provides an empirical link allowing for informed decision-making for managers and stakeholders, underscores the importance of service employees surpassing required job responsibilities to meet and exceed customer needs and suggests an agenda for future service research integrating EE and CE.

Prefabricated components sustainable supplier (PCSS) selection is critical to the success of prefabricated projects. However, limited studies have addressed the uncertainty and complexities during the selection process, particularly in multi-criterion group decision-making (MCGDM) circumstances. Hence, the research aims to develop a group decision-making model using a modified fuzzy MCGDM approach for PCSS selection under uncertain situation.

The proposed study develops a framework for sorting decisions in PCSS selection by using the hesitant fuzzy technique for order preference by similarity to ideal solution (HF-TOPSIS) method. The maximum consistency (MC) model is used to calculate the weights of decision makers (DMs) based on the cardinality and sequence of decision data.

The proposed framework has been successfully applied and illustrated in the case example of CB01 contract section in Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject. The results show various complicated decision-making scenarios can be addressed through the proposed approach. The MC model is able to calculate the weights of DMs based on the cardinality and sequence of decision data.

The research contributes to improving accuracy and reliability decision-making processes for PCSS selection, especially under hesitant and fuzzy situations in prefabricated megaprojects.

The multidimensional complexity of urban settlements is increasing and the problem of spaces and territories brought to the scale of smart cities is a critical global issue. This study aims to analyse the scientific production in the Web of Science (WoS) on the relationship between smart cities and the eight urban dimensions defined by the World Economic Forum (WEF) in the period 1990 to 2021, in order to establish which countries lead the knowledge related to the search for sustainable living conditions for people and how this knowledge contributes to improving stakeholders' decision-making.

The methodological steps followed in the study were: (1) Identification and selection of keywords. (2) Design and application of an algorithm to identify these selected keywords in titles, abstracts and keywords using WoS terms to contrast them. (3) Data processing was performed from Journal Citation Report (JCR) journals during the year 2022.

This study identified the authors, institutions and countries that publish the most globally on the topic of Smart Cities. The acceleration in the integration of new technologies and their impact on population conglomerates and their relationship with urban dimensions were also analysed. The evidence found indicates that the USA and China are leading in this field.

This bibliometric study was designed to analyse a knowledge space not addressed in the scientific literature referred to the relationship between the concept of smart cities and the urban dimensions established by the WEF, the identification of new technologies that are converging to promote developments of new ways of managing urban dimensions and propose new knowledge spaces.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

The purpose of this study is to investigate whether business growth intention (BGI) and entrepreneurial motivations enhance the explanatory power of the theory of planned behavior (TPB) to predict environmental intention (EI) among nascent entrepreneurs.

In the context of nascent entrepreneurship, the authors collected data from 193 nascent entrepreneurs in France. To test the hypotheses, stepwise multiple regression was performed.

The results show that BGI has a positive influence on EI. This indicates that it is possible for French nascent entrepreneurs to plan the simultaneous pursuit of business growth and environmental goals. However, entrepreneurial motivations have a mixed effect on EI. If necessity motivations negatively influence EI, opportunity motivations have no significant effect on the latter.

To the best of the authors’ knowledge, this research is among the first to extend the TBP model with additional factors, namely, BGI and necessity/opportunity motivations, to study EI. Moreover, the extended TBP model is validated in the under-research context of nascent entrepreneurship.

This study aims to reach a perception of the advance of research on deviant organisational behaviours.

This research has been done through the text mining method. By reviewing, the papers were selected 360 papers between 1984 and 2020. Based on the Davis–Boldin index, 11 optimal clusters were gained. Then the roots were ranked in any group, using the Simple Additive Weighting technique. Data were analysed by RapidMiner and MATLAB software.

According to the results obtained, clusters are included leadership styles, job attitudes, spirituality in the workplace, work psychology, personality characteristics, classification and management of deviant workplace behaviours, service and customer orientation, deviation in sales, psychological contracts, group dynamics and inappropriate supervision.

This study provides a landscape and roadmap for future investigation on deviant organisational behaviours.