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This study aims to investigate the integration of blockchain technology into the food supply chain within the restaurant industry. It focuses on how blockchain can be applied to enhance transparency and trust in tracking food sources, ultimately impacting customer satisfaction.

A service design workshop (Study 1) and three between-subjects experiments (Studies 2–4) were conducted.

Results indicate that blockchain adoption significantly improves traceability and trust in the food supply chain. This improvement in turn enhances customer satisfaction through perceived improvements in food safety, quality and naturalness. This study also notes that the effects of blockchain technology vary depending on the type of restaurant (casual or fine dining) and its location (tourist destinations or residential areas).

The findings offer practical insights for restaurant owners, technology developers and policymakers. Emphasizing the benefits of blockchain adoption, this study guides decision-making regarding technology investments for enhancing customer service and satisfaction in the hospitality sector.

This research contributes novel insights to the field of technology innovation in the hospitality industry. It extends the understanding of signaling theory by exploring how blockchain technology can serve as a tool for signal transmission in restaurant food supply chains.

Against the backdrop of the global reshaping of supply chains, supply chain ecosystems have emerged as a critical force in ensuring the high-quality development of enterprises and fostering stable economic growth. However, a systematic theoretical understanding of how to construct these supply chain ecosystems remains nascent. This study aims to explore the mechanism of the process of building supply chain ecosystems between digital innovation platform enterprises and digital trading platform enterprises from the perspective of dynamic capabilities.

An explanatory case study is conducted based on a theoretical framework grounded on dynamic capabilities view. Two preeminent digital platform enterprises in China (Haier and JD.com) are studied. The authors primarily conducted this research by collecting a large volume of these Chinese public materials.

First, the construction processes of supply chain ecosystems in both digital platform enterprises can be delineated into three stages: embryonic, development and maturity. Second, digital innovation platform enterprises’ construction process is primarily influenced by factors such as production and operational collaboration, consumer demand and research and development. This influence is exerted through interactions on digital platforms and within sub-ecosystems. Meanwhile, digital trading platform enterprises’ construction process is influenced by factors such as infrastructure development, consumer demand and financial support, driving dynamic capability formation through multi-party cooperation and ecological interactions based on conceptual identity.

In the establishment of supply chain ecosystems, digital platform enterprises should prioritize the cultivation of opportunity expansion, resource integration and symbiotic relationship capabilities. Furthermore, this study shows that digital platform enterprises need to actively adjust their interactive relationships with cooperating enterprises based on changes in the market, industry, policies and their own developmental stages.

This study addresses prior deficiencies in understanding the comprehensive construction of supply chain ecosystems and provides significant insights to enhance the theoretical foundation of supply chain ecosystem studies. Additionally, this paper uncovers the dynamic capability development behaviors and contextual features inherent in the construction process of supply chain ecosystems by digital platform enterprises.

The purpose of this study is to investigate the effects of different bullet screen types (functional vs social) on the continuous watching intention of consumers, as well as the influence mechanism. In addition, this study analyses the moderating role of consumer motivational orientation on the above effects.

First, objective data were obtained through the crawler to test the proposed hypotheses. An objective data analysis (417 group sample) was conducted to analyse the relationship between the percentage of social bullet screens and consumers sustained growth ratio to indirectly test the primary effect. Second, a questionnaire survey was conducted to test (176 questionnaires) the mediating role of perceived social crowding. Finally, a simulated online contextual experiment (340 participants) is conducted to explore the moderating role of consumer motivational orientation.

First, functional bullet screens produce higher continuous watching intention and lower perceived social crowding than social bullet screens. Second, perceived social crowding mediates the relationship between bullet screen type and continuous watching intention. Third, consumers' motivational orientation type (task-motivated vs recreation-motivated) moderates the relationship among bullet screen type, perceived social crowding and continuous watching intention.

The results of this study shed light on the differential impact of different types of bullet screens (functional and social) on consumers' continuous watching intentions, which makes up for the lack of research on the content of bullet screens in the field of livestreaming. Meanwhile, compared with the previous positive psychological research perspective, this study explores the intermediate mechanism of bullet screen type on consumers' continuous watching intention through a negative psychological perspective, which helps e-commerce companies and streamers better understand the differential impact of different bullet screens. Finally, this study explores the joint influence effect of bullet screen and consumer motivation type, which fills the theoretical research gap of consumer motivation orientation type in the category of live-streaming.

The purpose of this paper is to enhance the performance of robots in peg-in-hole assembly tasks, enabling them to swiftly and robustly accomplish the task. It also focuses on the robot’s ability to generalize across assemblies with different hole sizes.

Human behavior in peg-in-hole assembly serves as inspiration, where individuals visually locate the hole firstly and then continuously adjust the peg pose based on force/torque feedback during the insertion process. This paper proposes a novel framework that integrate visual servo and adjustment based on force/torque feedback, the authors use deep neural network (DNN) and image processing techniques to determine the pose of hole, then an incremental learning approach based on a broad learning system (BLS) is used to simulate human learning ability, the number of adjustments required for insertion process is continuously reduced.

The author conducted experiments on visual servo, adjustment based on force/torque feedback, and the proposed framework. Visual servo inferred the pixel position and orientation of the target hole in only about 0.12 s, and the robot achieved peg insertion with 1–3 adjustments based on force/torque feedback. The success rate for peg-in-hole assembly using the proposed framework was 100%. These results proved the effectiveness of the proposed framework.

This paper proposes a framework for peg-in-hole assembly that combines visual servo and adjustment based on force/torque feedback. The assembly tasks are accomplished using DNN, image processing and BLS. To the best of the authors’ knowledge, no similar methods were found in other people’s work. Therefore, the authors believe that this work is original.

The corona virus outbreak has affected the entrepreneurial ecosystem adversely. This in particular has impacted on females. This study aims to identify the factors affecting the entrepreneurial intention (EI) of females under post-COVID adverse conditions.

A total of 340 articles were screened applying the preferred reporting items for systematic reviews and meta-analyses method. The EI of individuals undertaking different professional courses from three top National Institute Ranking Framework (NIRF) government institutes were then analyzed. In the expert analysis, the nominal group technique (NGT) and analytic hierarchy process (AHP) were used to identify and rank the crucial factors. Subsequently, in the exploratory analysis, a 19-item questionnaire was framed. The data was analyzed using SmartPLS 3.

Resilience, entrepreneurial education, self-concept and self-efficacy, social influence and opportunity perception were identified as critical indicators. Resilience was identified as the most significant factor. The partial least square structural equation modeling (PLS-SEM) revealed that all the factors except social influence had significant effect on the EI of females.

This study focuses primarily on factors affecting females in India. As ecosystems and support vary by region and country, the authors suggest that this study be replicated in different regions/countries in the future.

The potential entrepreneurs can use this study's reference to identify the abilities they need. The government and academic institutions can have skill/training programs to enhance the effect of important factors identified in the study.

While there is growing research of entrepreneurship and entrepreneurial marketing post-pandemic, there are a lack of Indian studies and female entrepreneurship studies.

The entrepreneurial process often cannot be explained by a single entrepreneurial theory. Instead, it is more likely the result of the interaction between various entrepreneurial behavior patterns and different environmental conditions. However, existing research has frequently overlooked the complexity inherent in the entrepreneurial phenomenon. Building on a configurational perspective, this study aims to examine how new ventures can use different behavioral configurations to achieve high performance amid various uncertain environments.

Based on the survey data from 143 new start-ups in China’s software industry, this study uses fuzzy-set qualitative comparative analysis (fsQCA).

This study jointly considers multiple entrepreneurial behaviors − causation, effectuation and entrepreneurial bricolage and different types of environmental uncertainty − state uncertainty, effect uncertainty and response uncertainty. The findings reveal three behavioral configurations for high/nonhigh new venture performance.

This study expands previous insights into the relationship between entrepreneurial behaviors and new venture performance from the perspective of configurational theory. Moreover, it offers new insights into the types of uncertainty, further refining our understanding of the uncertainties inherent in entrepreneurial activities.

Owing to the finite nature of the boundary of the line (BOL), the conventional method, involving the strong matching of single-variety parts with storage locations at the periphery of the line, proves insufficient for mixed-model assembly lines (MMAL). Consequently, this paper aims to introduce a material distribution scheduling problem considering the shared storage area (MDSPSSA). To address the inherent trade-off requirement of achieving both just-in-time efficiency and energy savings, a mathematical model is developed with the bi-objectives of minimizing line-side inventory and energy consumption.

A nondominated and multipopulation multiobjective grasshopper optimization algorithm (NM-MOGOA) is proposed to address the medium-to-large-scale problem associated with MDSPSSA. This algorithm combines elements from the grasshopper optimization algorithm and the nondominated sorting genetic algorithm-II. The multipopulation and coevolutionary strategy, chaotic mapping and two further optimization operators are used to enhance the overall solution quality.

Finally, the algorithm performance is evaluated by comparing NM-MOGOA with multi-objective grey wolf optimizer, multiobjective equilibrium optimizer and multi-objective atomic orbital search. The experimental findings substantiate the efficacy of NM-MOGOA, demonstrating its promise as a robust solution when confronted with the challenges posed by the MDSPSSA in MMALs.

The material distribution system devised in this paper takes into account the establishment of shared material storage areas between adjacent workstations. It permits the undifferentiated storage of various part types in fixed BOL areas. Concurrently, the innovative NM-MOGOA algorithm serves as the core of the system, supporting the formulation of scheduling plans.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

A review of sustainability challenges of flame retardants (FRs) for textiles has been conducted. Specifically, the purpose of this paper is to identify and recommend solutions to sustainability challenges emanating from the raw material, processing technology and performance of the FRs used for textiles.

The approach used in preparing this paper was based on the review of various scholarly databases about the subject matter. The review approach is designed to inform the readers about the sustainability challenges of FRs for textiles. The science of burning and FRs for synthetic and cellulosic fibres were reviewed. Both synthetic and natural biodegradable FRs for textiles has been identified. The obtained literature was then synthesised to get information about sustainable challenges of non-halogenated FRs both synthetic and natural biodegradable. Finally, possible approaches for mitigating the identified challenges have been recommended.

The sustainability challenges of the FRs in terms of raw material, processing, affordability and performance have been identified. Synthetic FRs suffer from sustainability challenges in terms of raw materials, processing and non-renewability. Despite the environmental friendliness and sustainability in terms of being renewability, processability and biodegradability, natural biodegradable FRs have poor performance compared to synthetic ones. Moreover, natural biodegradable FRs depend on geographical condition and lack economic variability data. Potentially, the challenges of FRs can be mitigated through eco-friendly synthesis, chemical modification and sustainable methods of applications. Because of its renewability and environmental friendliness, biodegradable FRs have a potential to becoming sustainable if researched more.

In this review, a collection of literature about sustainability challenges of FRs and the approaches to overcome the challenges has been provided. The collected information was analysed and synthesised to bring understanding of the science of burning, types and application of FRs for textiles and biodegradable FRs. Sustainability challenges have been identified, and mitigation approaches are provided.

This paper aims to examine the financial outcome of reverse logistics among firms in a developing country. The authors draw on the organizational information processing theory to propose that analytics capability moderates the relationship between reverse logistics and financial performance.

The authors collected firm-level survey data from 200 manufacturing firms in Ghana, a developing country in sub-Saharan Africa. Partial least squares structural equations modeling is used to examine the proposed relationships, and the moderating effects are further probed using Hayes PROCESS.

The empirical results show that reverse logistics is negatively related to financial performance. However, analytics capability attenuates this negative relationship, such that firms with high analytics capability obtain a positive relationship between reverse logistics and financial performance.

Firms in developing countries should combine their reverse logistics strategies with developing analytics capabilities that help minimize uncertainties and increase the efficient collection and use of information to reduce the cost of reverse logistics.

This paper examines how reverse logistics relates to financial performance in low-resource contexts. Beyond the novelty of the context, it explores the information processing needs of reverse logistics systems and provides empirical data to support analytics capability. This has yet to be considered in prior studies.