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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.

The study aimed at analyzing operations managers’ perception of the use of electric vehicles (EVs) in business processes and its impact on overall business process cost (BPC) and sustainable development (SD).

The present study adopts the triangulation approach which is a combination of quantitative and qualitative methods. The data was collected using structured and scientifically tested questionnaires from the industrial managers working in the industries in the Mysore region of Karnataka. Descriptive statistics, factor analysis and structural equation models were employed to analyze and interpret the data.

The findings revealed that the usage of EVs in Business Processes significantly impacts the BPC (b = 0.851, t = 8.037, p < 0.01) and it is also the usage of EVs in business processes can significantly impact SD (b = 0.889, t = 7.923, p < 0.01). Thus, the adoption of EVs in the business process offers many benefits to business organizations such as minimized operational costs, an eco-friendly business model, more tax incentives, less BPCs, a low-emission footprint and a contribution towards SD at large.

Many business organizations operating in the present time show interest in employing EVs in their business processes. Hence, before introducing EVs in industries on a large scale, it becomes imperative to obtain the perception of industrial managers who have already experienced its impact. This study may help industrial organizations to understand the impact of EV on various aspects of the business and to design a business model which would help in achieving SD goals.

The use of EVs in the daily life of human beings and business activities is gaining importance because of the various positive impacts. Therefore, it is necessary to understand industrial managers’ opinions regarding the use of EV in business activities.

The study seeks to understand the possible opportunities that Industry 5.0 might offer for various aspects of inclusive sustainability. The study aims to discuss existing perspectives on the classification of Industry 5.0 technologies and their underlying role in materializing the sustainability values of this agenda.

The study systematically reviewed Industry 5.0 literature based on the PRISMA protocol. The study further employed a detailed content-centric review of eligible documents and conducted evidence mapping to fulfill the research objectives.

The advancement of Industry 5.0 is currently underway, with noteworthy initial contributions enriching its knowledge base. Although a unanimous definition remains lacking, diverse viewpoints emerge concerning the recognition of fundamental technologies and the potential for yielding sustainable outcomes. The expected contribution of Industry 5.0 to sustainability varies significantly depending on the context and the nature of underlying technologies.

Industry 5.0 holds the potential for advancing sustainability at both the firm and supply chain levels. It is envisioned to contribute proportionately to the three sustainability dimensions. However, the current discourse primarily dwells in theoretical and conceptual domains, lacking empirical exploration of its practical implications.

This study comprehensively explores diverse perspectives on Industry 5.0 technologies and their potential contributions to economic, environmental and social sustainability. Despite its promise, the practical evidence supporting the effectiveness of Industry 5.0 remains limited. Certain conditions are necessary to realize the benefits of Industry 5.0 fully, yet the mechanisms behind these conditions require further investigation. In this regard, the study suggests several potential areas for future research.

This research sought to identify the best strategy for avoiding corporate amnesia in the context of the Industry 5.0 and an aging society.

To achieve this goal, a multi-phase methodology based on analytic network process was proposed and tested in one of the biggest companies in the bakery industry.

The results highlight that online communities of practice and storytelling are the best way to avoid corporate amnesia. The most important factors are commitment, work satisfaction and organizational culture. Commitment and work satisfaction also enhance the use of online communities of practice, while work satisfaction and organizational culture foster the use of storytelling.

This article proposes a nexus between knowledge management and operations management. This research also presents a decision-making tool that can help managers determine the most appropriate strategy for avoiding corporate amnesia.

The purpose of this study is to explore the financial sources and evaluate the credit facilities available to Tibetan entrepreneurs especially residing in the vicinity of the Karnataka district, India. The most significant problem is that lending rates are extremely high and there is a lack of professional skill to manage their operations. Availability of financial support is still a major barrier for established and potential Tibetan entrepreneurs in the growth of their enterprises.

A sample size of 115 respondents, belonging to the urban and rural districts of Karnataka were interviewed to collect the information as primary data. Correlation analysis, cluster analysis, one-way ANOVA and percent test have been applied for statistical analysis. The interest rate, bank loan, credit, savings, friends and relatives, corporate, retained profits and trade credit are the variables used for the research.

Personal savings, bank credit and bank loans are the most important variables reflecting the credit activities and are clustered having a total of 3.710. Corporate, trade credit and retained profits form minimal sources of credit having a total of 1.194. Hence, there is an important relationship between the variables and the credit facilities availed by the entrepreneurs.

The research emphasis on their credit facility, financial growth, availability of capital are some of the challenges encountered by the entrepreneurs hindering the growth of the new business. Hence the researcher has focused on understanding and exploring the various challenges faced by these entrepreneurs.

This research mainly intends to ascertain the stimulus of investor investment tendencies on the amount of capital investment in the share market.

Utilizing a sample of 477 individual investors who actively trade on the Bangladesh capital market, this empirical study was conducted. The objective of this examination is to ascertain the investment trading behavior of retail investors in the Bangladesh capital market using multiple regression, hypothesis testing and correlation analysis.

The coefficients of market categories, preferred share price ranges and investment source reveal negative predictor correlations; all predictors are statistically significant, with the exception of investment source. Positive predictive correlations exist between investor category, financial literacy degree, investment duration, emotional tolerance level, risk consideration, investment monitoring activities, internal sentiment and correct investment selection. Except for risk consideration and investment monitoring activities, all components have statistically significant predictions. The quantity of capital invested in the stock market is heavily influenced by the investment duration, preferred share price ranges, investor type, emotional toleration level and decision-making accuracy level.

This investigation was conducted exclusively with Bangladeshi individual stockholders. Therefore, the existing study can be extended to institutional investors and conceivably to other divisions. It is possible to conduct this similar study internationally. And the query can enlarge with more sample size and use a more sophisticated econometric model. Despite that the outcomes of this study help the regulatory authorities to arrange more informative seminars and consciousness programs.

The conclusions have practical implications since they empower investors to modify their portfolios based on elements including share price ranges, investment horizons and emotional stability. To improve chances of success and reach financial objectives, they stress the significance of bettering financial understanding, active monitoring and risk analysis. Results can also be enhanced by distributing ownership over a number of market sectors and price points. The results highlight the value of patience and giving potential returns enough time.

This study on the trading behavior of investors in Bangladesh is unique and based on field study, and the findings of this study will deliver information to the stakeholders of the capital market regarding the investors’ trading behavior belonging to different categories, financial literacy level, investment duration, emotional tolerance level and internal feeling.

The purpose of this paper is to identify the “selling points” for Open University Malaysia (OUM) to be used in its marketing activities and the “critical points” that OUM should focus on for further improvements in providing its services to its students. These selling and critical points are derived from the analysis of the importance and satisfaction data collected from OUM’s postgraduate students.

This study employs a two-dimensional, i.e., Importance-Satisfaction Survey which consists of 47 items, categorized under eight dimensions. Items are phrased as positive statements and students are asked to indicate how important it is to them using a seven-point Likert scale ranging from not at all important (1) to very important (7). They are then asked to rate their level of satisfaction, using the same scale from very dissatisfied (1) to very satisfied (7). A total of 709 postgraduate students responses were used in this study. A multiple regression analysis was conducted to explain the relationship between the dependent variable, overall satisfaction and eight independent variables. The “selling points” and “critical points” are determined by combining the quadrant and gap analyses. The “selling point” items are the high-importance-high-satisfaction (HIHS) items with relatively small gap scores while the “critical points” are those in the high-importance-low-satisfaction and HIHS quadrants with relatively large gap scores.

The overall results of the Importance-Satisfaction Survey showed that the postgraduate students are generally satisfied with OUM’s programmes and services. The multiple regression analysis of all dimensions against overall satisfaction as the dependent variable showed that the five dimensions of facilitator, curriculum, faculty, support services and learning centre account for 75.7 per cent of the variation in overall satisfaction. The selling points include: the learning management system (MyVLE), online registration, course contents, modules and facilitators. The critical points include those related to facilitator interaction and feedback, students’ sense of connectedness with the faculty staff, timely responses to enquiries and complaints and accessibility to digital library and learning centre staff.

Importance-Satisfaction Surveys can be used to help an institution to identify the services and facilities that can be marketed and also those that need to be improved in order to better meet its students’ expectations.

While many similar studies had been conducted elsewhere, this study had identified the “selling points” and “critical points” which are unique to OUM. In addition, most previous studies were focused on conventional institutions, carried out in many different countries with differing learning environments and cultures.

Progress accomplished by the disabled entrepreneurs on the fronts of profits, turnover, return on investment (ROI), employees engaged, capital employed and diversification shall be studied and prevalence of gender differences in such progress shall be assessed.

The proposed research is descriptive in nature, based on primary data, collected by personally administering a well-structured interview schedule to 201 disabled entrepreneurs in Puducherry selected using a snowball sampling technique. Data collected has been analyzed using SPSS 21, using the tools of mean, one-way ANOVA, factorial ANOVA and chi-square (χ²) analysis.

The prevalence rate of entrepreneurship among female disabled is very low. Female disabled entrepreneurs manage higher turnover than their male counterparts and manage insignificantly higher progress in terms of capital employed, while male disabled entrepreneurs have managed insignificantly higher progress in terms of profits, diversification and ROI. Illiterate disabled, both men and women, struggle to manage decent turnover while the better educated manage better turnover.

This paper has highlighted the low prevalence rate of entrepreneurship among women disabled though the fewer women disabled entrepreneurs are performing better than their male counterparts in operating their business.

The findings of this paper may be taken as base for formulation of effective government policies in empowering disabled persons in general and women disabled in particular.

Recently, deep learning (DL) has been widely applied in various aspects of human endeavors. However, studies have shown that DL models may also be a primary cause of data leakage, which raises new data privacy concerns. Membership inference attacks (MIAs) are prominent threats to user privacy from DL model training data, as attackers investigate whether specific data samples exist in the training data of a target model. Therefore, the aim of this study is to develop a method for defending against MIAs and protecting data privacy.

One possible solution is to propose an MIA defense method that involves adjusting the model’s output by mapping the output to a distribution with equal probability density. This approach effectively preserves the accuracy of classification predictions while simultaneously preventing attackers from identifying the training data.

Experiments demonstrate that the proposed defense method is effective in reducing the classification accuracy of MIAs to below 50%. Because MIAs are viewed as a binary classification model, the proposed method effectively prevents privacy leakage and improves data privacy protection.

The method is only designed to defend against MIA in black-box classification models.

The proposed MIA defense method is effective and has a low cost. Therefore, the method enables us to protect data privacy without incurring significant additional expenses.

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.