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The transtheoretical model (TTM) has been applied to varying areas of physical health, e.g. diabetes. However, research into its applicability to psychotherapy is mixed. The TTM is applied through the University of Rhode Island Change Assessment (URICA). Investigating the utility of the URICA is needed to improve patient care and outcomes. This study aims to assess whether the URICA scores relate to patient outcomes; patient attendance; practitioner ratings of patient readiness, appropriateness, insight, motivation and potential for improvement; and to explore practitioner’s perspectives on the URICA.

Correlational methods were used to assess the relationship between the URICA and therapeutic outcome, attendance and practitioner-rated areas. Content analysis was used to analyse practitioner qualitative data.

The URICA did not correlate with either therapeutic outcome or attendance. A significant negative correlation was found between the URICA and practitioner-rated appropriateness of the referral. This means practitioners perceived individuals with lower URICA scores to be a more appropriate referral, despite the score indicating a reduced readiness to change. Qualitative categories included positive views, negative views, ambivalence and changes to measure and process. To conclude, the URICA does not explain a patient’s outcome or attendance. The URICA may not be appropriate to use in its current format in mental health services; therefore, assessing the TTM verbally may be more helpful.

This study provides research into suitability of using the URICA to assess the TTM and its applicability to attendance and outcome in psychological therapies.

This study aims to address the noted gap in comprehensive overviews detailing the developmental trajectory of Islamic finance (IF) as an interdisciplinary academic field.

The study introduces a unique approach using the combined methodologies of co-word analysis and main path analysis (MPA) by examining a broad collection of IF research articles.

The investigation identifies dominant themes and foundational works that have influenced the IF discipline. The data reveals prominent areas such as Shariah governance, financial resilience, ethical dimensions and customer-centric frameworks. The MPA offers detailed insights, narrating a journey from the foundational principles of IF to its current challenges and opportunities. This journey covers harmonizing religious beliefs with contemporary financial models, changes in regulatory landscapes and the continuous effort to align with broader socioeconomic aspirations. Emerging areas of interest include using new technologies in IF, standardizing global Islamic banking and assessing its socioeconomic effects on broader populations.

This study represents a pioneering effort to map out and deepen the understanding of the IF field, highlighting its dynamic evolution and suggesting potential avenues for future academic exploration.

Through the lens of resource-based view (RBV), knowledge-based view (KBV) and DCV, this paper aims to investigate the relationship of entrepreneurial leadership (EL) on the project success (PS) and further examines the mediating effect of knowledge infrastructure capability (KIC), knowledge-based dynamic capability (KBDC) and Big data analytic capability (BDAC).

The data were collected from 467 employees working on project in software companies. The data were evaluated using SMART-PLS, a structural equation modeling (SEM) tool.

The study revealed a significant impact of EL on the PS, the study also found the significant mediation role of KIC, KBDC and BDAC on the EL and PS relationship.

The research gives valuable insight into the effective role of EL as a contemporary leadership style in project-based firms. Also, this research is one of the first to examine knowledge-oriented dynamic capabilities (DC) as a knowledge fulcrum in project execution. These DC have been empirically proven to facilitate EL in achieving PS and support the firm in competing in an uncertain environment.

This research investigates the critical role of data governance (DG) in shaping a data-driven culture (DDC) within organizations, recognizing the transformative potential of data utilization for efficiency, opportunities, and productivity. The study delves into the influence of DG on DDC, emphasizing the mediating effect of data literacy (DL).

The study empirically assesses 125 experienced managers in Indonesian public service sector organizations using a quantitative approach. Structural Equation Modeling (SEM) analysis was chosen to examine the impact of DG on DDC and the mediating effects of DL on this relationship.

The findings highlight that both DG and DL serve as antecedents to DDC, with DL identified as a crucial mediator, explaining a significant portion of the effects between DG and DDC.

Beyond unveiling these relationships, the study discusses practical implications for organizational leaders and managers, emphasizing the need for effective policies and strategies in data-driven decision-making.

This research fills an important research gap by introducing an original model and providing empirical evidence on the dynamic interplay between DG, DL, and DDC, contributing to the evolving landscape of data-driven organizational cultures.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.

This paper aims to investigate the influence of health beliefs and trust by senior adults as associated with the perceived ease of use and perceived usefulness, for the acceptance of smart technology with a focus on smartwatch technology.

Structural equation modeling is used to conceptualize the model using survey data collected from 243 randomly selected senior adults 60+ years of age.

This paper presents that perceived usefulness, perceived ease of use, trust and health belief are direct and indirect predictors of senior adults’ technology acceptance and intention to use smartwatch technology.

The study reveals the moderator effect of social influence on relation between perceived usefulness, perceived ease of use and intention to use. The authors highlight the effect of health belief and trust on perceived usefulness and perceived ease of use and the role of intention to use smartwatch technology.

The authors contribute bridging developers of health technologists and senior adults as end-user perspectives. For marketing of health-care technology products, specifically smartwatch, to seniors, a focus on health beliefs and trust is essential to build, maintain and improve perceived usefulness and perceived ease of use.

The present study contributes empirical evidence to the literature on factors affecting the acceptance of the smartwatch technology by senior adults.

This study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.

This study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.

The findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.

This study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.

This study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.

The use of innovative technologies by firm employees is a key factor in ensuring the competitiveness of firms. However, researchers and practitioners have been concerned about the willingness of technology end users to use innovative technologies. This study, therefore, aims to determine the factors affecting the intention to use marketing analytics technology.

This study surveyed 213 firm employees. The quantitative data collected was analysed using partial least squares structural equation modelling.

The results reveal that performance expectancy, facilitating conditions, attitudes and perceived trust have a positive and significant effect on intentions to use marketing analytics. Effort expectancy, social influence and personal innovativeness in information technology were found not to predict intentions to use marketing analytics.

This study has practical implications for firms seeking to enhance the use of marketing analytics technology in developing countries.

This study contributes to the use of UTAUT, perceived trust, personal innovativeness and user attitude in predicting the intentions to use marketing analytics technology.

In the prior literature, the motivation to adopt wearable fitness technology (WFT) has been linked with either intrinsic or extrinsic. However, how the subcategories of extrinsic motivations (identified, introjected and external) affect the consumers’ WFT adoption decision remains sparse. Furthermore, do regulatory focus (prevention vs promotion) and gender differences the effects of different motivations on WFT adoption is almost unknown in the health-care marketing literature. This study aims to fill the above-mentioned gap and to unfold the WFT adoption beyond the traditional motivation by incorporating the organismic integration theory (part of self-determined theory) and regulatory focus theory.

This study used a questionnaire-based survey. Using the “AMOS” survey, questionnaire responses of 641 respondents were analyzed and validated by using structural equation modeling. All the variables were adopted from the literature.

The results show that intrinsic, identified and external motivations have the greatest impact on consumers’ decisions, while introjected motivation was not significant directly. The moderation effects of regulatory focus are significant in such a way that prevention focus influences the introjected motivation and promotion focus affects the external motivation and WFT adoption decision. Furthermore, the findings on gender moderation suggest that women are more intrinsically motivated, and men are more externally motivated for WFT adoption.

The new insights and contributions of this study provide a better understanding of WFT adoption and help sellers develop more effective marketing strategies.

This study incorporates subcategories of extrinsic motivations to provide a deeper understanding of consumers’ behavior. Furthermore, this study applies a unique framework of organismic integration theory to consumers’ WFT adoption. It is also among very few research that investigate regulatory focus and gender impact on consumers’ WFT adoption.

The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens.

This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data.

Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness.

Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.