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The research aims to examine the influence of perceived value (PV) dimensions on brand loyalty of luxury car owners and to examine the mediating role of attitudinal loyalty (AL) between PV dimensions and behavioral loyalty (BL).

Primary data for the study were gathered from the luxury car owners in Kerala, India. The construct measurements have been adopted from previous research studies. Structural equation modeling with the partial least square (PLS) technique was used to analyze the measurements and conceptual model.

The findings show that out of four PV dimensions among luxury car owners, the hedonic value (HV) significantly influences their AL. Economic value influences BL, and social values have an impact on AL as well as BL, but the relationship of functional value with any is not supported by the results. AL is a strong predictor of BL, and it actively mediates the relationship of HV and symbolic value with BL.

The manufactures of luxury cars provide more importance to hedonic and symbolic elements while launching new models and consider the price perceptions of the targeted customers while making decisions related to brand attachment and brand loyalty.

This study contributes to the decision-making of the rapidly growing vehicle market by examining the perceptions and by providing the effects of perceived values among luxury car owners. Also, it extends the literature by developing a framework for PV dimensions on AL and BL and also incorporated the mediating role of AL.

University-industry collaboration studies have placed greater emphasis on intrinsic motivators that drive academic researchers to pursue collaboration. This paper explores the relationship between spiritual motivation and collaboration intentions mediated the antecedents of university-level collaboration through theories of self-determination and planned behaviour. This study was conducted to validate the proposed relationship between spirituality and academic researchers in the field of engineering affiliated with higher educational institutions in India.

This study surveyed 242 participants and utilised structural equation modelling. Research has found that the beneficial relationship between spiritual motivation and collaboration intentions is mediated by attitudes and perceived behavioural control. This study represents a quantitative investigation within the realm of university-industry collaboration, which aims to document the ways in which spiritual motivation can augment collaboration with industry. The study employs self-determination theory and the theory of planned behaviour to elucidate the underlying mechanism to support entrepreneurial debate.

This study identified attitude and perceived behavioural control as mediators in the relationship between spiritual motivation and collaboration intentions.

The results of this study provide additional support for existing theories and present a diverse perspective on the intrinsic motivation of academic researchers to adopt UIC.

This study aims to comprehensively analyze the intersection of technology management and innovation management amidst the fourth industrial revolution, uncovering evolving trends and influential contributors.

Using the Bibliometrix R-package, this pioneering research conducts a bibliometric analysis to delve into innovation and technology management literature, quantifying scholarly output and identifying thematic breakthroughs.

The study reveals quantitative insights into the progression of innovation and technology management research, offering guidance on evolving trends, thematic breakthroughs and influential contributors.

The findings offer valuable insights for practitioners and managers, guiding them through emerging trends and recommending a dual focus on fundamental principles and emerging areas for strategic decision-making.

By fostering active engagement with evolving trends, this research contributes to the ongoing technology and innovation management discourse, potentially leading to societal benefits and advancements.

This study pioneers an in-depth bibliometric analysis at the intersection of innovation and technology management, offering unique insights and quantitative assessments of scholarly output and thematic trends, thus adding significant value to the existing literature.

This study aims to understand the lapse behavior of life insurance policyholders. Despite being accessible for nearly two centuries, only a small fraction of individuals purchase such policies and many of those who do let them lapse. The belief hypothesis model (BHM) is introduced to elucidate the correlation between policyholders' beliefs and their decisions regarding life insurance lapses.

BHM establishes a comprehensive linkage between core beliefs, external data and the lapse behavior exhibited by policyholders. To derive policyholders’ core beliefs about life insurance lapses, the authors conducted a semistructured, in-depth interview with 42 policyholders and 11 insurance advisors, using a grounded theory approach with zero-order, first-order and second-order coding.

The study's findings reveal that policy lapsation is influenced by various factors such as policyholders' beliefs about life insurance, process-related attitudes, trust in insurers and advisors and personal financial viewpoints. Policyholders who consider life insurance unnecessary or misunderstand its purpose are likelier to lapse their policies. Cumbersome documentation processes and technical issues also contribute to policy lapsation, emphasizing the significance of simplified procedures. Trust in insurers and advisors, personal financial literacy and payment preferences influence policy lapsation.

The findings of this research can be practically applied by companies to improve customer retention and by regulatory bodies to encourage policyholders to honor their insurance commitments.

Distinguishing itself from conventional hypotheses-driven and factor-centric models, BHM integrates consumer beliefs, thus enriching comprehension and decision-making insights.

This paper aims to analyze the frictional performance of the phosphated CTRBs under dry sliding conditions. Through friction and wear experiments and characterization analysis, this paper investigated the wear mechanism and anti-wear mechanism of the composite phosphating coatings.

This paper used a method of incorporating graphite particles and polytetrafluoroethylene (PTFE) particles into manganese-based phosphating solution to prepare composite phosphating coatings applied on cylindrical thrust roller bearings (CTRBs).

The results are as follows: the inclusion of functionalized graphite particles affects the characterization of the composite phosphating coatings, whereas PTFE particles serve a physical filling role and have no influence on the surface features of the composite phosphating coatings. The surface roughness of the composite phosphating coatings is the main factor affecting the frictional behavior of phosphated CRTBs. Roughness affects the amount of PTFE particles adsorbed on the surface of the phosphate crystals. PTFE particles can form a solid lubricating film at the friction interface, and under the same surface roughness conditions, phosphated CTRBs with added PTFE particles can improve its friction performance.

The characterization parameters of the composite phosphating coatings were controlled by adding functionalized graphite particles to the phosphating solution. The effects of adding only graphite particles and adding both graphite particles and PTFE particles on the surface characteristics of the phosphating coatings were discussed separately. The tribological performance of phosphating CTRBs was analyzed through friction and wear testing, and the influence of surface characteristics on wear mechanisms and anti-wear mechanisms was discussed.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0405/

This quantitative study aims to examine the mediating role of perceived corporate social responsibility (CSR) between organisational practices and affective commitment. Code of ethics, ethics training, transformational leadership and ethical climate were investigated as factors of perceived CSR.

This research model was evaluated using structural equation modelling and survey data from 184 employees of Malaysian pharmaceutical multinationals.

Perceived CSR mediated the relationship between code of ethics, transformational leadership, ethical climate and affective commitment.

The CSR investments aimed at developing an effective ethics programme, transformational leadership practices and ethical climate could improve organisational competitiveness by strengthening employees’ perceived CSR and affective commitment.

The systematic investigation of various organisational controls that establish a corporate environment of ethics and social responsibility sets a precedent for past piecemeal approaches. The existing body of knowledge is based on Western countries, which revealed insights unique to pharmaceutical multinationals in a transitional Asian economy.

Based on a quantitative investigation, this paper endeavors to examine Industry 4.0 (I4.0) adoption process by studying the impacts of absorptive capacity (AC) and innovative ambidexterity (exploration, exploitation), while also considering the moderating influence of learning capability (LC).

Data has been gathered through administering questionnaire to 468 managers representing 175 manufacturing firms. Subsequently, PLS-SEM technique has been employed to verify the research hypotheses.

Study findings reveal that AC is significantly associated with I4.0 adoption and innovation ambidexterity. However, innovation ambidexterity demonstrates partial (only exploration) significant association with the adoption of I4.0. Similarly, the findings indicate that LC acts as a partial moderator between innovation ambidexterity (exploration) and I4.0 adoption.

The study presents significant insights into I4.0 adoption process. The findings may support managers of manufacturing firms to understand and assess the influence of integrating contextual factors facilitating successful adoption of I4.0. The study emphasizes necessity of managers’ awareness regarding the importance of firm’s AC to transform smoothly to I4.0 technologies. In addition to, encouraging the innovation ambidexterity along with LC to enhance the adoption of I4.0.

While researchers demonstrate increasing interest in applying I4.0, concrete evidence to support the I4.0 adoption process is, still, insufficient due to ongoing challenges in digital transformation. Consequently, further research is needed, particularly in exploring how a firm’s ability to realize knowledge and foster innovation contributes to implementing I4.0. This paper seeks to tackle this lack of research by examining the connection between AC, innovation ambidexterity, and LC and the adoption of I4.0 in an emerging economy.

The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0.

This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0.

In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections.

Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.

This paper aims to explore the numerical study of the steady two-dimensional MHD hybrid Cu-Fe₃O₄/EG nanofluid flows over an inclined porous plate with an inclined magnetic effect. Iron oxide (Fe₃O₄) and copper (Cu) are hybrid nanoparticles, with ethylene glycol as the base fluid. The effects of several physical characteristics, such as the inclination angle, magnetic parameter, thermal radiation, viscous propagation, heat absorption and convective heat transfer, are revealed by this exploration.

Temperature and velocity descriptions, along with the skin friction coefficient and Nusselt number, are studied to see how they change depending on the parameters. Using compatible similarity transformations, the controlling equations, including those describing the momentum and energy descriptions, are turned into a set of non-linear ordinary differential equations. The streamlined mathematical model is then solved numerically by using the shooting approach and the Runge–Kutta method up to the fourth order. The numerical findings of skin friction and Nusselt number are compared and discussed with prior published data by Nur Syahirah Wahid.

The graphical representation of the velocity and temperature profiles within the frontier is exhibited and discussed. The various output values related to skin friction and the Nusselt number are shown in the table.

The new results are compared to past research and discovered to agree significantly with those authors’ published works.

The existing traditional recommendations based on content-based filtering (CBF), collaborative filtering (CF) and hybrid approaches are inadequate for recommending practice challenges in programming online judge (POJ). These systems only consider the preferences of the target users or similar users to recommend items. In the learning environment, recommender systems should consider the learning path, knowledge level and ability of the learner. Another major problem in POJ is the learners don't give ratings to practice challenges like e-commerce and video streaming portals. This purpose of the proposed approach is to overcome the abovementioned shortcomings.

To achieve the context-aware practice challenge recommendation, the data preparation techniques including implicit rating extraction, data preprocessing to remove outliers, sequence-based learner clustering and utility sequence pattern mining approaches are used in the proposed approach. The approach ensures that the recommender system considers the knowledge level, learning path and learning goals of the learner to recommend practice challenges.

Experiments on practice challenge recommendations conducted using real-world POJ dataset show that the proposed system outperforms other traditional approaches. The experiment also demonstrates that the proposed system is recommending challenges based on the learner's current context. The implicit rating extracted using the proposed approach works accurately in the recommender system.

The proposed system contains the following novel approaches to address the lack of rating and context-aware recommendations. The mathematical model was used to extract ratings from learner submissions. The statistical approach was used in data preprocessing. The sequence similarity-based learner clustering was used in transition matrix. Utilizing the rating as a utility in the USPAN algorithm provides useful insights into learner–challenge relationships.