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Drawing upon the conservation of resources (COR) theory, this study examined the relationship between organizational developmental human resource (HR) practices and employees' commitment to the organizational career (COC), while considering the mediating role of psychological availability and the moderating effects of links and fit.

A hierarchical linear modeling analysis was conducted to analyze the data from a 3-wave time-lagged study with 394 employees and 65 supervisors in ten Chinese firms.

The study's findings suggest that organizational developmental HR practices have a positive and indirect impact on employees' COC, with psychological availability serving as a mediator. The indirect effects were stronger for employees with stronger links and better fit.

This study provides managers with guidelines to improve employees' COC. Managers should also focus on supporting employees' psychological resources. Furthermore, organizations should be aware that employees with varying levels of links and fit may respond differently to psychological availability.

The study offers a new understanding of how and under what conditions organizational developmental HR practices can increase employees' COC. It also highlights the role of psychological availability as the psychological mechanism mediating this relationship and adds to the limited literature on the potential positive impact of strong links and fit.

This study aims to examine whether and how corporate sustainability capability influences energy efficiency through competitive intensity and slack resource availability.

The authors applied a two-wave research design and administered a survey questionnaire to senior-level managers of 78 ISO-14001 and ISO-50001 certified manufacturing companies. The authors use a multi-method approach for data analysis. AMOS 23 software was applied for covariance-based structural equation modeling. In addition, SPSS 25 software was applied for hierarchical regression analysis to examine the causal relationships in the model.

The finding reveals that corporate sustainability capabilities, which include energy-saving opportunities, seizing energy-saving opportunities and resource reconfiguration, significantly improve firms’ energy efficiency. In addition, competitive intensity and slack resource availability positively moderated the relationship between corporate sustainability capability and energy efficiency.

To the best of the authors’ knowledge, this study is the first to examine the link between corporate sustainability capability and energy efficiency in developing countries such as Pakistan. Although the influence of various corporate sustainability capabilities on sustainable performance has been widely examined in the literature, the role of corporate sustainability capability has been limitedly explored with energy efficiency. This study extends the literature by adding to the knowledge of corporate sustainability capability that enhances boundary conditions in developing countries.

The purpose of this study was to improve the overall equipment effectiveness (OEE) of the production process of the shredder operation of ABC company, an industrial waste management company which supplies pre-processed industrial waste as alternative fuel to a cement plant.

This case study investigated all possible availability and performance losses that caused the shredder system’s OEE and various problem-solving techniques, such as root cause analysis and Pareto analysis, were used to find the root cause of the reduced OEE.

After analysing this case study, three significant loss factors were identified from all the availability and performance losses, which caused the shredder system’s OEE losses. Practical solutions were found for the effect of those loss factors to improve the machine’s OEE and productivity.

This case study has been concentrated on only analysing of losses and improvement of OEE in the production process and not about cost analysis between loss and improvements.

This paper shows how to improve the OEE of a production process through various problem-solving techniques by identifying its losses and how to achieve the best solutions for those losses in a practical manner.

The purpose of this study is to examine how the tourism economy affects local food availability, access, utilization and stability in dessert-prone agricultural heritage sites. Specifically, the study aims to explore the relationship between the tourism industry and local agricultural practices and how this connection influences food security in the Siwa Oasis, located in the Western Desert of Egypt.

The study employs a qualitative exploratory research design using in-depth interviews and focus groups to investigate the impact of the tourism economy on food security and identify potential benefits and limitations for food security in the region.

The research reveals that the tourism economy in Siwa Oasis has only a marginal contribution to food security. The study highlights a lack of a strong connection between the tourism industry and local agricultural practices within the heritage site. As a result, the potential benefits and synergies that could be achieved between tourism and agriculture have not been fully realized, leading to a limited impact on food stability.

This study primarily relies on qualitative data from Siwa Oasis, Egypt, which may limit the generalizability of findings beyond this specific context. Additionally, while the study provides valuable insights into the complex relationship between tourism and food security, it does not quantitatively measure the magnitude of tourism's impact. Future research could incorporate quantitative methods for a more comprehensive understanding of this relationship in diverse desert-prone regions. Finally, the study highlights the need for more integrated approaches to enhance food security through tourism, but the specific strategies and policy recommendations require further investigation and adaptation to local contexts.

This study underscores the need for tourism development strategies that prioritize food security in desert-prone areas like Siwa Oasis. Policymakers and stakeholders should promote sustainable tourism practices that enhance local agriculture, create diversified income sources and foster equitable benefits for communities. Moreover, recognizing the seasonal nature of tourism, interventions to address food shortages during off-peak periods are crucial. Efforts should also focus on skill development and gender-inclusive opportunities within the tourism sector to ensure broader community participation. Additionally, collaborations between tourism and agriculture should be encouraged to optimize food availability and stability while preserving cultural food traditions.

This study adds original insights by examining the specific impact of the tourism economy on food security in dessert-prone agricultural heritage sites. The study's originality lies in its exploration of the untapped potential for synergy between the tourism and agricultural sectors and the implications for local food security. This research contributes to understanding how tourism can improve food security in specific contexts and provides valuable insights into sustainable development in heritage sites.

Introduction: According to the existing research, one of the key determinants of a company’s survival and market development is its ability to get bank loans or other external sources of finance for business expansion.

Purpose: The study aims to explore the factors affecting access to finance and their effects on the development of medium- and small-sized businesses. These factors include business size and age, profitability, the length of a company’s association with a commercial bank, and banking sector characteristics.

Need for the study: It is particularly crucial for small- and medium-sized businesses since they often have trouble getting funding from banks because they don’t supply the banks with the information they need to assess their loan application prospects, however, when a company’s economic and financial situation improves, banks get access to more information about the firms, and financing is thus more readily available.

Methodology: This research is based on qualitative methods, focus on an elaborative study of the existing literature, and provide suggestions based on the same.

Findings: The findings show that small- and medium-sized businesses, like those in other European nations, have less access to finance than large businesses. It revealed that the company’s size, liquidity, profitability, and banking industry state significantly influence the availability of bank loans.

This research intends to investigate the determinants that affect consumers’ purchase intention of electric vehicles (EVs) in Malaysia using an extended theory of planned behaviour (TPB).

Survey data were collected with a sample size of 306. The research used SmartPLS 4.0 structural equation modelling tool to analyse the data. Reliability and validity tests (discriminant and convergent validity) were used and subsequently assessed the measurement and structural models. Mediation analysis was conducted to identify the role of the latent constructs.

The findings indicated that a green purchase attitude plays a complete mediation role in the effect of environmental knowledge on the purchase intention of EVs. In the same notion, the effect of price perception and availability of charging facilities on the purchase intention of EVs passes completely through perceived behavioural control. However, the subjective norm was an insignificant mediator of the impact between government support and EV purchase intention.

This paper helps to examine the latent constructs that impact purchase intention using environmental knowledge, government support, price perception and the availability of charging facilities. Successful green marketing and a sustainable consumerism framework are seen as a booster to promote the usage of EVs in Malaysia.

An extended TPB model has been employed in this research to study the effects of the above-mentioned constructs. The results show that most of the extended constructs are significant in explaining the purchase intention. The empirical results address the gap in the consumer green attitude and provide insight into this area of study.

The purpose of this study is to understand the investment decision-making of real estate investors in housing, highlighting the interplay between rational and irrational factors. In this study, investment satisfaction was a mediator, while reinvestment intention was the dependent variable.

A quantitative, cross-sectional and descriptive research design was used, gathering data from a sample of 550 residential real estate investors using a multi-stage stratified sampling technique. The partial least squares structural equation modelling disjoint two-stage approach was used for data analysis. This methodological approach allowed for an in-depth examination of the relationship between rational factors such as location, profitability, financial viability, environmental considerations and legal aspects alongside irrational factors including various biases like overconfidence, availability, anchoring, representative and information cascade.

This study strongly supports the adaptive market hypothesis, showing that residential real estate investor behaviour is dynamic, combining rational and irrational elements influenced by evolutionary psychology. This challenges traditional views of investment decision-making. It also establishes that behavioural biases, key to adapting to market changes, are crucial in shaping residential property market efficiency. Essentially, the study uncovers an evolving real estate investment landscape driven by evolutionary behavioural patterns.

This research redefines rationality in behavioural finance by illustrating psychological biases as adaptive tools within the residential property market, urging a holistic integration of these insights into real estate investment theories.

The study reshapes property valuation models by blending economic and psychological perspectives, enhancing investor understanding and market efficiency. These interdisciplinary insights offer a blueprint for improved regulatory policies, investor education and targeted real estate marketing, fundamentally transforming the sector’s dynamics.

Unlike previous studies, the research uniquely integrates human cognitive behaviour theories from psychology and business studies, specifically in the context of residential property investment. This interdisciplinary approach offers a more nuanced understanding of investor behaviour.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

This study aims to identify the major factors influencing the consumers to prefer milk products and also to analyze the awareness level of the Indian consumers.

In this study, the data is obtained through a structured questionnaire from Indian consumers considering convenience sampling under the nonprobability sampling technique. The consumer preference is explained using a multiple-regression model followed by analysis of variance (ANOVA), which shed insight on the significant differences between the variables that influence consumer preference for dairy products.

Investigation is done to analyze the factors influencing the consumers' buying behavior toward milk and its products. The results showed that quality, health consciousness, price and availability are the most influencing factors to buy milk products. Quantity of milk showed a significant relationship between age, monthly income and family size.

This study helps marketing managers to frame the marketing strategies based on consumer preference, quality, health consciousness, price and availability. The research outcome will not only be advantageous for the entrepreneurial perspective but also takes care of consumer likeliness. Though the research reveals the opinion of Indian consumers, it limits the likeliness of the western world. Because of the scarcity of resources, several dairy products are unexplored, which could pave the future scope of research.

The novelty of this study is to identify the quality, health consciousness, price and availability are the most influencing factors to buy milk products considering ANOVA and the multiple regression model.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.