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The principal research objective entails examining the nexus between green supplier selection and green performance while scrutinizing the moderating role of governance mechanisms, specifically process control and outcome control, in shaping this association.

To assess our hypotheses, this study obtained data from Chinese manufacturing sectors and utilized regression analysis on a dataset consisting of 295 samples.

This study enriches the sustainable supply chain management literature by emphasizing the influence of green supplier selection on a firm’s green performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.

This study enriches the sustainable supply chain management literature by emphasizing the influence of supplier selection on a firm’s environmental performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.

Artificial intelligence (AI) has a significant impact on value co-creation (VCC). However, a study providing a comprehensive summary of the current state of the art and common ground of the two fields is missing. The current study aims to fill this gap by conceptualizing the role of AI in VCC and customer decision-making.

The study reviews literature on VCC and AI together, including a total of 108 articles. To bring the literature together, the authors adopted the antecedents-mediators-outcomes framework and narrative approach that helped them develop a framework by integrating the antecedents, mediators and outcomes of AI-facilitated VCC. Furthermore, the authors also operationalized existing literature to facilitate an understanding of the role of AI in customer decision-making.

The study, in addition to identifying the common theoretical grounds of VCC and AI (human behavior, cognition and social interactions), operationalizes AI functionality, its characteristics and customer characteristics as the antecedents of AI-facilitated VCC. Moreover, based on literature, on the continuum of low-to-high involvement, four types of decision-making were identified as mediator of the relationship between AI characteristics, customer characteristics and VCC. Additionally, the authors found different categorizations of AI in literature as archetypes to support various forms of VCC.

The study contributes to the literature of VCC and AI by construing a comprehensive framework for analyzing AI's impact on VCC, envisioning customer–AI interaction as continual exchange of advantages in which characteristics of AI and customers play a critical role in customer decision-making and shaping VCC.

The aim of the work is to investigate structural behaviour of reinforced concrete (RF) beam retrofitted with basalt fibre (BF) fabric. The incorporation of BF showed enhancement in bending strength, to increase confinement and to repair damages caused by cracking. In the early decades, using BF for composite materials shaped BF as an excellent physical substance with necessary mechanical properties, highlighting the significant procedures ability.

Specimens were casted with U-wrapped BF and then evaluated based on flexural tests. In the test carried over for flexural fortifying assessment, BF reinforcements demonstrated a definitive quality improvement in the case of the subjected control sample; ultimately, the end impacts depend upon the applied test parameters. From the outcomes introduced in this comparison, for the double-wrapped sample, the modifications improved by 12% than that of the single-wrapped beam, which is identified to subsist for a better strengthening of new-age retrofitting designs.

The current research deals with the retrofitting of RC beam by conducting a comparative experiment on wrapping of BF (single or double BF wrapping) in improving the mechanical behavior of concrete.

It can be shown from the experimental results that increasing the number of layers has significant effect on basalt strengthened beams.

The purpose of this study is to test the impact of time and price sensitivity on consumer satisfaction and purchase intention on online-to-offline (O2O) takeout platforms and explore the moderating effect of purchase preference on time sensitivity and satisfaction, as well as price sensitivity and satisfaction, in order to guide market pricing.

A structural equation model (SEM) of customer purchase intention was constructed, and the relationships between the variables (time sensitivity, price sensitivity, satisfaction and purchase intention) were examined. The completed questionnaires of 349 respondents were collected from the Questionnaire Star platform in China. The research model and hypotheses were then tested. Analytic hierarchy procedure was used to determine the moderating effect of purchase preference. Finally, the study proposes a pricing strategy for customer-active selective services.

Satisfaction positively influences purchase intention, and price sensitivity significantly increases satisfaction and further increases purchase intention; however, time sensitivity negatively affects satisfaction. Specifically, purchase preference has strongly moderated the relationship between time, price sensitivity and satisfaction. In addition, the findings show that when purchase preference is high, the effect of price sensitivity on satisfaction is stronger, suggesting the importance of purchase preference in strengthening purchase intentions. The research work recommends a pricing strategy involving value-added pricing primarily for time-sensitive customers, which can help build a high-end brand image and reduce price competition. Reduced pricing is mainly for price-sensitive customers, which is conducive to stimulating consumption within a specific time. This pricing strategy is important for adjusting market sensitivity and flexibility.

This research provides new ideas for related disciplines and guidance for the differentiated pricing and promotion of takeout platforms, as well as a theoretical basis for the diversified development of takeout platforms, improvement of personalized service quality and enhancement of customer stickiness. This study fills gaps in the existing literature on the moderating effect of purchase preference on time sensitivity and satisfaction and price sensitivity and satisfaction.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.

Digital technology plays a vital role in empowering omnichannel integration. Research on digital technology has recently attracted attention and rapidly developed. However, a comprehensive assessment of the research status and potential gaps is yet to be conducted. Thus, this study investigated the current research status of digital technology-empowered omnichannel integration, and future research directions are proposed.

A three-stage bibliometric analysis was conducted on 764 articles published from 2000 to 2023, cited in the Web of Science database. Furthermore, performance and thematic analyses were performed.

The most productive contributors and influential articles in this field were identified, and four themes of focus were discovered: service quality, o2o commerce, omnichannel retailing, and digital transformation.

To the best of our knowledge, this work is the first attempt to enable researchers to understand the vast body of published scholarship on digital technology-empowered omnichannel integration.

Industry 4.0 with artificial intelligence (AI), the Internet of Things, and automation are giving way to Industry 5.0 which places emphasis on workers' well-being, people, and society. The new era accentuates interaction between humans and smart machines to drive prosperity and induce sustainable contribution of industry to society. It must build a resilient economy to cope with future uncertainties or external shocks. Moreover, the sustainability effort of companies demanded by consumers, employees, shareholders, governments, and civil society requires sustainability transformation of business strategies. Applying sustainability principles in product design implies the consideration of a product's entire life cycle. Technologies accelerate sustainable production and support using natural resources efficiently taking into consideration planetary boundaries. This condition also makes for a novel perception of products, including financial products. Under the current circumstances of a volatile, uncertain, complex, and ambiguous environment, the focus on consumers and recognition of their dominance play a pivotal role in business models to maintain customer relationships. Thus, the digital, sustainable, human-centric industry is emerging. This chapter deals with digital products to enable twin transition and accomplish a sustainable business strategy simultaneously. It examines the reciprocal reinforcement of sustainability and digitalization and discusses the impact of digital sustainability on products. In this sense, this chapter also outlines the business contribution to sustainable development through digitalization. Finally, a holistic model for a hypothetical financial remittance product is offered to demonstrate how a digital product can contribute to sustainable development.

This study aims to elucidate the machining properties of low-cost expanded clay-reinforced syntactic foams by using different neural network models for the first time in the literature. The main goal of this endeavor is to create a casting machining-neural network modeling flow-line for real-time foam manufacturing in the industry.

Samples were manufactured via an industry-based die-casting technology. For the slot milling tests performed with different cutting speeds, depth of cut and lubrication conditions, a 3-axis computer numerical control (CNC) machine was used and the force data were collected through a digital dynamometer. These signals were used as input parameters in neural network modelings.

Among the algorithms, the scaled-conjugated-gradient (SCG) methodology was the weakest average results, whereas the Levenberg–Marquard (LM) approach was highly successful in foreseeing the cutting forces. As for the input variables, an increase in the depth of cut entailed the cutting forces, and this circumstance was more obvious at the higher cutting speeds.

The effect of milling parameters on the cutting forces of low-cost clay-filled metallic syntactics was examined, and the correct detection of these impacts is considerably prominent in this paper. On the other side, tool life and wear analyses can be studied in future investigations.

It was indicated that the milling forces of the clay-added AA7075 syntactic foams, depending on the cutting parameters, can be anticipated through artificial neural network modeling.

It is hoped that analyzing the influence of the cutting parameters using neural network models on the slot milling forces of metallic syntactic foams (MSFs) will be notably useful for research and development (R&D) researchers and design engineers.

This work is the first investigation that focuses on the estimation of slot milling forces of the expanded clay-added AA7075 syntactic foams by using different artificial neural network modeling approaches.

Supplier evaluation and selection is an essential (multi-criteria decision-making) MCDM process that considers qualitative and quantitative factors. This research work attempts to use a MCDM technique based on merging fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) and Quality Function Deployment (QFD) ideas. The study attempts to find the supplier's attributes (HOWs) to accomplish its goals after determining the product's characteristics to suit the company's needs (WHATs).

The proposed research methodology comprises the following four steps: Step 1: Determine the product purchase requirements (“WHATs”) and those pertinent to supplier evaluation (“HOWs”). In Step 2, the relative importance of the “WHAT-HOW” correlation scores is determined and also the resulting weights of “HOWs”. In Step 3, linguistic evaluations of possible suppliers in comparison to subjective criteria are given to the decision-makers. Step 4 combines the QFD and F-TOPSIS techniques to select suppliers.

A fuzzy MCDM method based on fusing and integrating fuzzy information and QFD is presented to solve the drawbacks of conventional decision-making strategies used in supplier selection. Using the F-TOPSIS method, fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS), the relative closeness coefficient values for all alternatives are computed. The suppliers are ranked by relating the closeness of coefficient values. This method permits the combination of ambiguous and subjective data expressed as fuzzy-defined integers or linguistic variables.

QFD and TOPSIS, two widely used approaches, are combined in this article to rank and evaluate suppliers based on the traits that the suppliers choose to prioritize. This study demonstrates that the method employed could address multiple-criteria decision-making scenarios in a computationally efficient manner. The effectiveness and applicability of the method are illustrated using an example.

Implementing sustainable practices in multi-tier supply chains (MTSCs) is a difficult task. This study aims to investigate why such endeavours fail and how MTSC partners can address them.

A single-case study of a global food retail company was used in this study. Semi-structured interviews with the case firm and its first- and second-tier suppliers were used to collect data, which were then qualitatively analysed using thematic analysis.

Major barriers impeding the implementation of sustainability in multi-tier food supply chains were revealed such as the cost of sustainability, knowledge gap, lack of infrastructure and supply chain complexity. Furthermore, the findings reveal five possible solutions such as multi-tier collaboration and partnership, diffusion of innovation along the chain, supply chain mapping, sustainability performance measurement and capacity building, all of which can aid in the improvement of sustainability practices.

Future research should investigate how specific barriers and drivers affect specific aspects of sustainability, pointing practitioners to specific links between the variables that can aid in tailoring sustainability oriented investment.

This research supports managerial comprehension of MTSC sustainability, pointing out ways to improve sustainability performance despite the complex multi-tier system of food supply chains.

The research on MTSC sustainability is still growing, and this research contributes to the debate about how MTSCs can become more sustainable from the perspective of the triple bottom line, particularly food supply chains which face significant sustainability challenges.