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This study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context.

Drawing on the elaboration likelihood model, this study establishes a research model to reveal the antecedents and internal mechanisms of customers' adoption of AI chatbot recommendations. The authors tested the model with survey data from 530 AI chatbot users.

The results show that in the AI chatbot recommendation adoption process, central and peripheral cues significantly affected a customer's intention to adopt an AI chatbot's recommendation, and a customer's cognitive and emotional trust in the AI chatbot mediated the relationships. Moreover, a customer's mind perception of the AI chatbot, including perceived agency and perceived experience, moderated the central and peripheral paths, respectively.

This study has theoretical and practical implications for AI chatbot designers and provides management insights for practitioners to enhance a customer's intention to adopt an AI chatbot's recommendation.

1. The study investigates customers' adoption of AI chatbots' recommendation.

2. The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

3. The central and peripheral cues are generalized according to cooperative principle theory.

4. Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

5. Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

6. Customers' mind perception positively moderates the central and peripheral paths.

The study investigates customers' adoption of AI chatbots' recommendation.

The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

The central and peripheral cues are generalized according to cooperative principle theory.

Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

Customers' mind perception positively moderates the central and peripheral paths.

Stewardship behavior is an important embodiment of the spirit of employee ownership, which is critical to the sustainability of companies, especially under the influence of the COVID-19 epidemic. Most previous studies have focused on how to motivate employees’ stewardship behavior, but little is known about how stewardship behavior affects employees themselves. The purpose of this study is to explore how employee stewardship behavior affects their work-family interface based on the conservation of resources (COR) theory.

In this study, structural equation modeling was conducted using two-wave survey data from 323 employees through three internet companies in Southern China.

Results reveal that engaging in stewardship behavior is positively correlated with both positive emotion and emotional exhaustion. Positive emotion and emotional exhaustion, in turn, mediate the effects of stewardship behavior on work–home interface. Family motivation influences the strength of the relationships between positive emotion or emotional exhaustion and work–family interface, that is, high family motivation strengthens the positive association between positive emotion and work–family enrichment and weakens the positive association between emotional exhaustion and work–family conflict.

This study suggests that managers should give employees more support and care to ease the worries of engaging in stewardship behavior. Also, organizations should recruit employees with high family motivation, which can reduce the negative effects of stewardship behavior on work–-family interface.

Based on an actor’s perspective, this study examines both the positive and negative effects of stewardship behavior on employees themselves, thereby increasing understanding of the dual effect of stewardship behavior. In addition, this study further elucidates the mechanisms that moderate the positive and negative effects of individual family motivation on their engagement in stewardship behavior within the COR theory.

Existing studies have paid less attention to the impact of knowledge accumulation on digital transformation and its boundary conditions. Hence, this study aims to investigate the effects of ambidextrous knowledge accumulation on manufacturing digital transformation under the moderation of dynamic capability.

This study divides knowledge accumulation into exploratory and exploitative knowledge accumulation and divides dynamic capability into alliance management capability and new product development capability. To clarify the relationship among ambidextrous knowledge accumulation, dynamic capability and manufacturing digital transformation, the authors collect data from 421 Chinese listed manufacturing enterprises from 2016 to 2020 and perform analysis by multiple hierarchical regression method, heterogeneity test and robustness analysis.

The empirical results show that both exploratory and exploitative knowledge accumulation can significantly promote manufacturing digital transformation. Keeping ambidextrous knowledge accumulation in parallel is more conducive than keeping single-dimensional knowledge accumulation. Besides, dynamic capability positively moderates the relationship between ambidextrous knowledge accumulation and manufacturing digital transformation. Moreover, the heterogeneity test shows that the impact of ambidextrous knowledge accumulation and dynamic capabilities on manufacturing digital transformation varies widely across different industry segments or different regions.

First, this paper shifts attention to the role of ambidextrous knowledge accumulation in manufacturing digital transformation and expands the connotation and extension of knowledge accumulation. Second, this study reveals that dynamic capability is a vital driver of digital transformation, which corroborates the previous findings of dynamic capability as an important driver and contributes to enriching the knowledge management literature. Third, this paper provides a comprehensive micro measurement of ambidextrous knowledge accumulation and digital transformation based on the development characteristics of the digital economy era, which provides a theoretical basis for subsequent research.

In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.

The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.

Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.

While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.

Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.

Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.

The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

This paper examines how firms respond to local government’s environment initiatives through textual analysis of government work reports (GWRs). This study aims to provide insights into how firms strategically respond to government’s environmental initiatives through their disclosure and investment practices.

This study uses a textual analysis of GWRs from China’s provinces. The frequency and change rate of environmental keywords in these reports are used as a measure of the government’s environmental initiatives.

This study finds that environmental disclosure scores in environmental, social and governance (ESG) reports increase with the frequency or change rate of environmental keywords in provincial GWRs. This effect is more pronounced for non-state-owned enterprises, firms in highly marketized provinces or those listed in a single capital market. However, there is no significant relationship between firms’ environmental investments and government initiatives, except for cross-listed firms in provinces with consistently high frequency of environmental keywords in their GWRs.

The findings indicate that government environmental initiatives can shape firms’ disclosure behaviors, yet have limited influence on investment decisions, suggesting that environmental disclosure could potentially be opportunistic. This underscores the need for more effective strategies to stimulate firms’ environmental investments.

This study provides valuable insights into the differential impacts of government environmental initiatives on firms’ disclosure and investment behaviors, contributing to the understanding of corporate environmental responsibility in the context of government initiatives.

The purpose of this study is to examine the relationship between the corporate social responsibility (CSR) performance of job-hopping executives at their former and subsequent firms.

We conduct regression analyses using a sample of firms listed on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2020 to examine whether CSR performance is similar from one firm to the next as executives switch jobs.

We find a positive relationship between the CSR performance of former and subsequent firms under job-hopping executives. This relationship is the strongest in the year of the job switch; it weakens in the second year and eventually disappears in the third year. In addition, we show that this relationship benefits different CSR stakeholder groups and is contingent on executive and subsequent firm attributes and job-hopping characteristics. Furthermore, we demonstrate that firms that hire a new chief executive officer from a firm with a strong track record in CSR, the new firm experiences a significant surge in CSR performance compared with firms that do not experience such a shock.

This study has implications for executive hiring decisions.

This study extends the understanding of CSR determinants through the lens of inter-organisational ties associated with job-hopping executives.

This study aims to test the relationship between feedback-seeking behavior (FSB) and knowledge sharing. The study also proposes the moderating role of gender in the relationship between FSB and knowledge sharing. In this study, the author draws on the social exchange theory to propose FSB as a driver of knowledge sharing. Ultimately, the study seeks to contribute to a better understanding of the role that FSB plays in triggering knowledge sharing and the ways in which gender can influence this dynamic.

A survey was conducted with 290 knowledge workers from the manufacturing and service sectors of India. FSB and knowledge sharing were assessed with a purposive sample (n = 290). The hypotheses were tested using confirmatory factor analysis (CFA), structural equation modeling (SEM) and multi-group moderation analysis.

The study found that FSB is positively related to knowledge sharing and gender moderates the relationship between FSB and knowledge sharing.

This study adds to the literature by investigating the potential interplay between FSB, knowledge sharing and gender. By uncovering the ways in which gender differences can impact FSB and knowledge sharing, this study provides valuable insights for organizations seeking to promote knowledge sharing and improve communication and collaboration among employees.

Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.

This research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.

To detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.

The novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.

Intelligent new energy vehicles (INEVs) are becoming the competitive hotspot for the automobile industry. The major purpose of this study is to determine how to increase innovation efficiency through knowledge sharing and technology spill between new energy vehicle (NEV) enterprises and technology enterprises. This will help to improve the core competence of the automobile industry in China. Also, it serves as a guide for the growth of other strategic.

The authors construct a tripartite evolutionary game model to study the cross-border cooperative innovation problem. Firstly, the payment matrix of NEV enterprise, technology enterprise and government is established, and the expected revenue of each participant is determined. Then, the replication dynamic equations and evolutionary stability strategies are analyzed. Finally, the theoretical research is validated through numerical simulation.

Results showed that: (1) An optimal range of revenue distribution coefficient exists in the cross-border cooperation. (2) Factors like research and development (R&D) success rate, subsidies, resource and technology complementarity, and vehicles intelligence positively influence the evolution towards cooperative strategies. (3) Factors like technology spillover risk cost inhibit the evolution towards cooperative strategies. To be specific, when the technology spillover risk cost is greater than 2.5, two enterprises are inclined to choose independent R&D, and the government chooses to provide subsidy.

The research perspective and theoretical analysis are helpful to further explore the cross-border cooperation of the intelligent automobile industry. The findings suggest that the government can optimize the subsidy policy according to the R&D capability and resource allocation of automobile industry. Moreover, measures are needed to reduce the risk of technology spillovers to encourage enterprise to collaborate and innovate. The results can provide reference for enterprises’ strategic choice and government’s policy making.

The INEV industry has become an important development direction of the global automobile industry. However, there is limited research on cross-border cooperation of INEV industry. Hence, authors construct a tripartite evolutionary game model involving NEV enterprise, technology enterprise and the government, and explore the relationship of cooperation and competition among players in the INEV industry, which provides a new perspective for the development of the INEV industry.

This study aims to investigate the relationship between political connections, financing decisions and cash holding.

Based on historical data from 181 active non-financial firms listed on Gulf Cooperation Council (GCC) Stock Exchange Markets during the period of 2009–2016, this study uses ordinary least squares and dynamic system-generalized method of moments to test the research hypotheses. The final data set comprises a total of 1,448 firm-year observations from ten major non-financial industry classifications.

This study finds a positive relationship between political connections and each of internal financing proxied by retained earnings ratio and external financing proxied by short- and long-term debt to total asset. The findings also show a positive relationship between political connections and cash holding.

The findings of the study provide a better understanding of the role of politically connected directors in financing decisions and cash holding in the GCC. Investors can consider the presence of royal family members in the board of directors when making investment decision. Policymakers are encouraged to develop more effective policies that encourage listed firms to provide information on the political positions of the board of directors, managers and major shareholders/owners of companies.

This study contributes to the literature by providing empirical evidence on the relationship between political connections and financing decisions by focusing on the GCC region. This study also highlights that boards in connected firms in the GCC have lower monitoring role owing to political interventions, and that connected firms face higher agency problems as they have weak governance and boards compared with non-connected firms.