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In most economies, there are rules from the market regulators or government to sell at an equal wholesale price (EWP). But when one upstream channel is facing a negative demand disruption and another positive, EWP can create extra pressure on the disadvantageous supply chain partner, which faces negative disruption. The purpose of this study is to analyse the impact of EWP and the scope of the discriminatory wholesale price (DWP) during disruptions.

For the study, the authors used a dual-channel supply chain consisting of a manufacturer, online retailer (OR) and traditional brick-and-mortar (BM) retailer. Stackelberg game is used to model the interaction between the upstream and downstream channel partners, and the horizontal Nash game to analyse the interaction within downstream channel partners. For modelling asymmetric disruption, the authors took instances from the lock-down and post-lock-down periods of the COVID-19 pandemic, where consumers flow from BM retailer to OR store.

By analysing the disruption period, the authors found that this asymmetric disruption is detrimental to the BM channel, favourable to OR and has no impact on the manufacturer. But with DWP, the authors found that the profit of the BM channel and manufacturer can be increased during disruption. Though the profit of the OR decreased, it was found to be higher than in the pre-disruption period. Under DWP, the consumer surplus increased during disruption, making it favourable for the customers also. Thus, DWP can aid in creating a win-win strategy for all the supply chain partners during asymmetric disruption. Later as an extension to the study, the authors analysed the impact of the consumer transfer factor and found that it plays a crucial role in the optimal decisions of the channel partner during DWP.

Very scant literature analyses the intersection of DWP and disruptions. To the best of the authors’ knowledge, this study, for the first time uses DWP as a tool to help the disadvantageous supply chain partner during asymmetric disruptions. The study findings will assist the government, market regulators and manufacturers in revamping the wholesale pricing policies and strategies to help the disadvantageous supply chain partner during asymmetric disruption.

This research examines the drivers of electric vehicle (EV) acceptance in the Kingdom of Saudi Arabia (KSA) by applying the unified theory of acceptance and use of technology (UTAUT) model, contextualized for the EV setting. The study aims to provide insights supporting the transition to sustainable transportation and identifying consumer perceptions and behavioral intentions toward EV adoption.

Based on survey data from a convenience sample collected from undergraduate and MBA students in a major university of KSA, the authors use seemingly unrelated regressions to provide novel insights on electric vehicle acceptance.

The study shows UTAUT constructs influence purchase intentions and attitudinal outcomes. Results indicate that perceived EV sustainability plays an important role in the relationship between UTAUT constructs and purchase intention alongside attitudes toward EV technology. Technological innovativeness enhances the impact of EV attitude and weakens the effect of perceived EV sustainability on purchase intention.

The study benefits researchers on sustainable technology acceptance and stakeholders facilitating sustainable transportation shifts. The insights guide the promotion of eco-friendly transportation solutions.

The research contextualizes and extends the UTAUT model constructs to understand drivers of EV acceptance. The study contributes to understanding sustainable innovation acceptance, considering the mediating role of perceptions of EV sustainability and the moderating role of technological innovativeness in driving purchase intentions.

The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy.

The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model.

The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2.

This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.

This research aims to find which components of Decent Work are associated with Subjective Well-Being.

With data from 2021 from the states of Mexico, econometric models are carried out.

It is found that disposable income and satisfaction with leisure time have a significant positive relationship with employees' Subjective Well-Being. Likewise, the rate of critical occupancy conditions and informality rate have a significant negative relationship with Subjective Well-Being. The research suggests that influencing the Decent Work conditions of the population in Mexico could favour their Subjective Well-Being.

Share guidelines that enable employers and governments to establish strategies and policies that promote Decent Work to increase the Subjective Well-being of employees.

This article evaluates different variables that make up the Decent Work construct in their level of influence on Subjective Well-being. These relationships and variables considered have not been identified in previous studies as a whole.

This study aims to assess the moderating and mediating role of supportive culture (SC) in the relationship between participation in decision-making (PDM) and job satisfaction (JS) and the dimensions of commitment, such as affective commitment (AC), normative commitment (NC) and continuance commitment (CC).

Data were collected from 712 employees working in different public sector undertakings (PSUs) across India. Necessary condition analysis and partial least square analysis were used to test the proposed hypotheses.

The findings of the present study indicated that SC is partially mediating the relationship between PDM and JS; PDM and AC. However, SC did not mediate the relationship between PDM and NC; PDM and CC. PDM was positively and significantly related to SC, JS, AC, NC and CC. JS had a significant impact on AC, NC and CC. It is highly desirable for organizations to retain their employees ranging from line managers to top management levels and provide opportunities for everyone to actively use their experience and expertise.

The findings have implications for managers, as well as employees in PSUs, as they demonstrate how several work-related factors can be emphasized to maintain employees' commitment and motivation. Until now, India has paid scant attention to the role of SC as a mediator and moderator between PDM, JS and multiple commitments. This study cautiously collected responses from unbiased employees working in a variety of organizational functional units.

Significant developments in the service sector have been brought about by Industry 4.0. Automated digital technologies make it possible to upgrade existing services and develop modern industrial services. This study prioritizes critical factors for adopting Industry 4.0 in the Indian service industries.

The author identified four criteria and fifteen significant factors from the relevant literature that have been corroborated by industry experts. Models are then developed by the analytical hierarchy process (AHP) and analytical network process (ANP) approach to ascertain the significant factors for adopting Industry 4.0 in service industries. Further, sensitivity analysis has been conducted to determine the sensitivities of the rank of criteria and sub-factors to corroborate the results.

The outcome reveals the top significant criteria as organizational criteria (0.5019) and innovation criteria (0.3081). This study prioritizes six significant factors information technology (IT) specialization, digital decentralization of all departments, organizational size, smart services through customer data, top management support and Industry 4.0 infrastructure in the transition toward Industry 4.0 in the service industries.

The potential factors identified in this study will assist managers in determining strategies to effectively manage the Industry 4.0 transition by concentrating on top priorities when leveraging Industry 4.0. The significance of organizational and innovation criteria given more weight will lay the groundwork for future Industry 4.0 implementation guidelines in service industries.

Our research is novel since, to our knowledge, no previous study has investigated the potential critical factors from organizational, environmental, innovation and cost dimensions. Thus, the potential critical factors identified are the contributions of this study.

This study describes a series of experiments investigating the upper hot layer temperature profile in a confined space under different ventilation conditions for porosity-controlled wood crib fires for pre-flashover conditions.

Full-scale compartment (4 m × 4 m × 4 m) experiments were carried out for four-door openings, i.e. 100%, 75%, 50% and 25% of the total vent area (2 m × 1 m) with the wood crib as a fuel load. The temperature of the upper hot smoke layers of the compartment was recorded with the help of four layers of thermocouples for varying vent areas.

The effect of ventilation on the properties, i.e. mass loss rate, enclosure temperature, heat release rate and carbon monoxide (CO) gas concentration, has been measured and analyzed. The effect of ventilation on heat flux and flame temperature has also been studied. Compartment gas temperature has been examined by five wood crib burning stages: Ignition, growth, steady burning, recess and collapse.

Findings demonstrate that the influence of vent openings varies for the burning parameters and upper layer temperature of the compartment. The current results are beneficial in analyzing thermal risks concerning compartment fire and fire safety engineering projects.

The main purpose of this paper is to determine the bias-corrected efficiencies and rankings of the selected hotels and restaurants (H&Rs) in India.

The data for the Indian H&R sector are collected from the Prowess database. The bootstrap data envelopment analysis (DEA) based on a constant return to scale (CRS), variable return to scale-input oriented (VRS-IP) and variable return to scale-output oriented (VRS-OP) are applied on H&Rs to obtain the bias-corrected efficiencies.

It is found that relative efficiencies using basic DEA methods of all the 45 H&Rs of India are overestimated. These efficiencies are corrected using bias correction through bootstrap DEA methods. The bounds for the efficiencies of each H&R are computed using all the adopted methods. All H&Rs are ranked using bias-corrected efficiencies, and the linear trend between ranks suggests that the H&Rs are ranked almost similarly by all the adopted methods.

To improve efficiency, Indian H&R companies must rethink their personnel needs by enhancing their workforce management capabilities. The government needs to extend more support to this sector by introducing a liberal legislation framework and supporting infrastructure policies.

There is a paucity of studies on H&Rs in India. The current study focused on measuring bias-corrected efficiencies of the selected H&Rs of India. This study is one of the few initiatives to explore bias-corrected efficiencies extensively using the bootstrap DEA method.

The purpose of this study is to investigate the interactive effects of a firm’s strategic sustainability orientation (SSO) and customer relationship capital (CRC) on sustainable product design practices (SPDP).

The authors examine the interactive effects of two firm resources, namely, SSO and CRC, as they relate to SPDP, using survey data from 132 manufacturers in Malaysia.

The findings show that the effect of SSO on SPDP is stronger when CRC is low, than otherwise. The authors also find that the interactive effects of the firm-level resources stated above are stronger when customer pressures (CPs) are higher than when they are low.

The authors contribute to the literature by studying the effects of firm-level strategic resources and their interactions as they relate to SPDP. An important limitation of this research is the usage of single-source data and single-period data.

Managerially, this research provides new insights into how organizations should respond to CPs while understanding the counterintuitive but disabling effects of customer relationship assets.

The findings are important to society because most of the environmental impact arising from the production, consumption and disposal of products is a direct consequence of decisions made at the product design stage and an unintended consequence of the marketing decision-making processes that underlie marketing strategy formulation that fails to recognize ecosystems impact as a standard decision influence.

To the best of the authors’ knowledge, this research is the first to study the effects of firm-level strategic resources and their interactions as they relate to SPDP.

Considering the high impacts caused by manufacturers on sustainability, this research aims to analyse how Brazilian manufacturing companies deal with sustainability issues. To do this, sustainability parameters are analysed to verify possible improvement opportunities.

This research uses Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and grey relational analysis (GRA) to analyse data from a survey with Brazilian professionals regarding aspects of sustainability in Brazilian manufacturers. The average score levels and the ranking of these aspects are evaluated.

Through the analysis performed, it was possible to verify that manufacturers in Brazil still have a long path to travel in the search for sustainability. Comparatively, it was observed that practices related to local communities received the lowest scores, on average. In contrast, on average, practices related to productivity and efficiency, occupational accidents and diseases, and compliance with environmental legislation received the highest scores.

The results presented in this paper show that there are several improvement opportunities to be sought by Brazilian manufacturing companies regarding sustainability aspects. Particular attention should be given to local community practices. Besides companies, policymakers can also use this analysis to guide their future actions, encouraging manufacturing companies to better support the local community. Researchers can use the instrument of analysis (TOPSIS and GRA) to analyse other realities and compare them with the findings presented.

The analysis of Brazilian manufacturing companies’ reality regarding sustainability practices and considering a model based on Global Reporting Initiative (GRI) and Brazilian Institute of Corporate Governance (IBGC) is novel in the literature. The use of TOPSIS and GRA, as well as comparing their findings, generated interesting insights for companies, policymakers and researchers. The analysis presented shows the need for more significant concern for local communities and can be used to support further debates and action plans to minimise this gap.