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This paper examines the moderating effects of online reviews on the relationship between country image, product image, and purchase intention of products from two developed countries in Vietnam.

This current research used a cross-sectional design. Data was collected via questionnaires, and 305 responses were left after refining. The collected data were analyzed using exploratory factor analysis, confirmatory factor analysis, structural equation modeling, and multi-group analysis methods.

Affective country images do not directly affect purchase intention when online review quality and positivity are high. Cognitive country images still directly affect purchase intention when online review positiveness is low. However, online review quantity does not moderate the effects of country images on product images and purchase intention.

Cognitive country image consistently affects purchase intention through the central route independent of online reviews. In contrast, the affective country image will likely affect purchase intention through the peripheral route when online reviews are insufficient for customers.

Firms can mitigate the adverse effects of country image, especially cognitive country image, in foreign markets by improving online review quality and positiveness.

Our study extended existing literature by providing a better understanding of the nature of country image and the roles of country image dimensions in shaping product image and purchase intention in the context of the increasing popularity of online reviews.

3D body scanning technology, when applied using smartphone applications, enhances interactions between consumers and companies using this technology by offering accurate measurements, personalized recommendations, virtual fitting and social interactions. Despite the worldwide 3D scanning technology market continuing to grow through these advantages, many consumers remain unaware of or do not use this technology. Thus, this study aims to examine how consumers perceive the consumption values of smartphone-based 3D body scanning technology and analyze their impact on usage intention.

Nettelo, a smartphone-based 3D body scanning application, was selected as the experimental platform. An online survey of US females aged 18 and above yielded 390 valid responses for the final analysis.

The findings reveal that users perceive the functional value as high, thereby influencing usage intention through trust and satisfaction. Social and epistemic values directly impact usage intention, while emotional and conditional values significantly affect trust and satisfaction.

The paper demonstrates the application of the theory of consumption values to smartphone-based 3D body scanning technology in the decision-making process and suggests ways for fashion companies to bridge the gap with consumers and innovatively enhance their experiences.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.

Demand planning (DP) is a key element of supply chain management (SCM) and is widely regarded as an important catalyst for improving supply chain performance. Regarding the availability of technology to process large amounts of data, artificial intelligence (AI) has received increasing attention in the DP literature in recent years, but there are no reviews of studies on the application of AI in supply chain DP. Given the importance and value of this research area, we aimed to review the current body of knowledge on the application of AI in DP to improve SCM performance.

Using a systematic literature review approach, we identified 141 peer-reviewed articles and conducted content analysis to examine the body of knowledge on AI in DP in the academic literature published from 2012 to 2023.

We found that AI in DP is still in its early stages of development. The literature is dominated by modelling studies. We identified three knowledge clusters for AI in DP: AI tools and techniques, AI applications for supply chain functions and the impact of AI on digital SCM. The three knowledge domains are conceptualised in a framework to demonstrate how AI can be deployed in DP to improve SCM performance. However, challenges remain. We identify gaps in the literature that make suggestions for further research in this area.

This study makes a theoretical contribution by identifying the key elements in applying AI in DP for SCM. The proposed conceptual framework can be used to help guide further empirical research and can help companies to implement AI in DP.

This paper provides empirical evidence on the impact of societal trust in three dimensions of banking activity: the quantity and quality of credit granted by banks and their economic performance.

The paper uses a sample of 1,366 European banks from 36 countries between 2014 and 2020. In our estimations, we control for bank-specific and country-specific determinants of banking activity. Moreover, we perform several robustness tests, including estimation by instrumental variables and hierarchical linear methods, variable definitions, and subsamples.

Societal trust increases the weight of credit on the balance sheets of banks and decreases the weight of nonperforming loans and net interest margins. Moreover, the results suggest a smaller (higher) impact of trust on loans and net interest margins (nonperforming loans) in the European Monetary Union (EMU) countries. Also, on the importance of loans and net interest margins, our results suggest a substitution effect between trust and the credit-favorable nature of the legal environment.

The symmetrical sign of the effect of societal trust on the importance of loans – positive – and nonperforming loans and net interest margins – negative – suggests that potential asymmetric information biases may be less severe or better accounted for by the credit risk evaluation in highly trustworthy environments. Also, bank managers should recognize risk factors related to trust levels and benefit from targeted risk mitigation measures and from implementing strategies aimed at enhancing profitability in different societal trust scenarios.

This paper provides empirical evidence on the impact of societal trust on banking activity from the perspective of the lending activity, risk and performance of banks. It also analyses the effects of EMU membership and the credit-favorable nature of the legal environment.

Given the substantial presence of the Bangladeshi apparel industry on the international stage, it is imperative to examine the sector’s technology adoption (TA) behavior. This study aims to understand the TA-related factors perceived by top apparel industry professionals in Bangladesh.

Based on the technology, organization and environment (TOE) framework, this study used a two-step analytical sampling approach and conducted semi-structured in-depth interviews with eight top-level industry professionals from Bangladeshi apparel firms.

The findings indicate that the technological factors include perceived benefits, technical compatibility, investment cost, and availability of training and technical support. Firm size, business scope, and financial viability were found as organizational factors, whereas buyer pressure, local and global competition, government policies, and sustainability were found as environmental factors of TA decisions.

This study provides practical and novel insights into the role of top management in TA within the TOE framework. By shedding light on TA in Bangladesh’s apparel industry, this research contributes to the literature.

This paper aims to examine how the number of short videos posted and the number of influencers employed, two important strategies in short video marketing, affect consumer behavior and how price discounts moderate the effects of influencer endorsement on consumer browsing and purchasing behavior.

Drawing on the literature on influencer endorsement, this study used an ordinary least square model to empirically examine the two effects of endorsement strategies in increasing product traffic and sales for consumers at a short video app, Douyin (TikTok).

The results show that the number of short video ads produces the classic inverted U-shape for traffic and sales, and both effects were strengthened under a high discount condition. Whereas the number of influencers has a positive effect on traffic but produces an inverted U-shape for sales, both effects were undermined under a high discount condition.

This study is the first to explore the two distinct effects (repetition effect and diffusion effect) of influencer endorsement on browsing and purchasing behavior and theorize about the moderate effects of discounts on these effects.

This study aims to investigate how the supply chain (SC) experts from the UK manufacturing organisations (MO) interpret the drivers and barriers to adopting Industry 4.0 technologies from a technological, organisational and environmental (TOE) point of view. Furthermore, this study evaluates how adoption drivers and barriers influence innovative practices that support Industry 4.0 adoption.

A qualitative narrative inquiry strategy, involving nine semi-structured interviews with leading SC experts including group executives, global directors and vice presidents was adopted.

The people factor across the SC is found critical for successful Industry 4.0 adoption. It was also found that the firm size is an insignificant factor; rather, Industry 4.0 readiness in small and medium enterprises (SMEs) is driven by flexibility and agility, while larger organisations are driven by resource availability and scalability. Evidently, the culture and attitude of the MO tend to facilitate or hinder the Industry 4.0 adoption, therefore, innovative practices in building an expert team, establishing a systematic change management process, actively involving suppliers in the adoption process and continuously monitoring the adoption process were introduced.

By addressing the relationships between the drivers, barriers and innovative practices from a business perspective, a detailed Industry 4.0 adoption framework for the UK Manufacturing Supply Chains (MSC) was developed as a unique theoretical and practical contribution. This study also highlights the lessons learnt from applying Industry 4.0 in the UK context. The findings can also be informed in applying Industry 4.0 technologies in emerging market contexts.

Linear addition is commonly used to generate ensemble forecasts for decomposition ensemble models but traditionally treats individual modes with equal weights for simplicity. Using Taiwan air passenger flow as an empirical case, this study examines whether incorporating weighting for individual single-mode forecasts assessed by grey relational analysis into linear addition can improve the accuracy of the decomposition ensemble models used to forecast air passenger demand.

Data series are decomposed into several single modes by empirical mode decomposition, and then different artificial intelligence methods are applied to individually forecast these decomposed modes. By incorporating the correlation between each forecasted mode series and the original time series into linear addition for ensemble learning, a genetic algorithm is applied to optimally synthesize individual single-mode forecasts to obtain the ensemble forecasts.

The empirical results in terms of level and directional forecasting accuracy showed that the proposed decomposition ensemble models with linear addition using grey relational analysis improved the forecasting accuracy of air passenger demand for different forecasting horizons.

Accurately forecasting air passenger demand is beneficial for both policymakers and practitioners in the aviation industry when making operational plans.

In light of the significance of improving the accuracy of decomposition ensemble models for forecasting air passenger demand, this research contributes to the development of a weighting scheme using grey relational analysis to generate ensemble forecasts.

Body language is an integral part of interpersonal communication and exchange, which can convey rich emotions, intentions and information. However, how anchor’s body language works in live-streaming e-commerce (LSE) has yet to receive adequate attention. Based on dual systems theory of decision-making, this paper aims to explore the impact of anchor’s body language on the performance of LSE from the perspective of customer engagement behavior and to examine the moderating role of anchor’s relational social interaction.

The authors confirmed the theoretical model through empirical analysis of structured data from 1,415 actual livestreaming rooms from Douyin, as well as unstructured data of 418,939 min of video and audio, 1,985,473 words of text and 423,302 keyframe images.

The study found that anchor’s body language has a significant positive effect on the performance of LSE, and customer engagement behavior plays a partially mediating role. The moderating effect suggests that anchor’s relational social interaction and body language have substitution effects in enhancing customer engagement behavior and the performance of LSE, which reveals the substitution relationship between anchor’s verbal and nonverbal interactions in LSE.

This study is one of the earlier literature focusing on anchor’s body language, and the findings provide practical references for enhancing customer engagement behavior and achieving performance growth in LSE.