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Drawing on a mission approach, this study investigates the application of integrated reporting (IR). It also explores its role in shaping organisations' internal processes in Chinese state-owned enterprises (SOEs) and the China General Nuclear Power Corporation (CGN).

We employed a case study method and collected data by conducting semi-structured interviews with CGN managers and analysing their reports and appropriate documents.

Our findings reveal that the CGN is motivated by its mission and vision to adopt IR rather than by other common motivations, such as legitimacy, strategy and stakeholder pressure. IR practice contributes to implementing its mission and vision through direct and indirect methods, covering the changes in design archetypes and subsystems that meet the nature of reorientation changes that belong to first-order transition.

This study extends empirical evidence of IR at the firm level in China and provides in-depth insights into how IR is implemented in Chinese SOEs. Our findings may be helpful for policymakers to review and develop policies. For instance, the government might consider integrating IR frameworks into current reporting and mandate listed companies to adopt IR. However, our data are from one company, the only Mainland Chinese Company recognised by the IIRC database.

This study provides an innovative approach to analysing IR and offers managers insight into how IR practice benefits the mission’s implications.

Despite the increasingly salient role of smart manufacturing (SM) in revolutionizing operational processes, little research has explored the dynamics of corporate executives’ decision-making on SM projects after their initiation. To fill this research gap, this study examines the configurations of project complexities (i.e. technological breadth and organizational breadth) and industry conditions (i.e. industry growth and industry competition) that shape managerial decisions to complete or terminate SM projects.

Using secondary data of 125 SM projects implemented in 106 manufacturers during the 2011–2020 period, we conduct a fuzzy-set qualitative comparative analysis to empirically derive equifinal configurations that lead to the completion or termination of SM projects.

The results reveal that project complexities and industry conditions work together in configurations where manufacturers will complete or terminate SM projects. We employ prospect theory to elucidate the findings and offer propositions.

Our study extends the extant SM literature by revealing the configurations of project complexities and industry conditions that shape managerial decisions on the completion or termination of SM projects after their initiation. It contributes to the prospect theory literature by accounting for the roles of both decision content and decision context and providing empirical evidence on their joint effects on managerial risk-taking decisions that alter the subjective value and probability weight of decision outcomes.

Adding Pd element to Au wire can improve the reliability of Au-Al bonding, but the mechanism of Pd element has not been well revealed so far. The purpose of this study is to reveal in more detail the mechanism of the role of Pd elements in Au/Al bonding.

In this paper, the microstructure changes and tensile data of 99.99% (4N) gold wire and 99% (2N) gold wire with 1 at % Pd were compared through high-temperature thermal aging treatment of the specimens, so as to explore the influence mechanism of Pd element on Au-Al bonding reliability.

The addition of Pd element effectively reduces the thickness of intermetallic compounds (IMCs) layer and strengthens the fracture tension and reliability. Compared with the 4N specimen, the average thickness of the IMCs layer of the 2N specimen under the same conditions is reduced by 1 µm, and the tensile value of the 2N specimen is increased by 1−3 g. Stored at 200°C for 200 h, the failure rate of bond point of 4N specimen reached 94.64%, and that of 2N specimen was 20%, with a difference of 4.73 times.

Through comparative analysis of the data, this study found that the doping of Pd element in Au-Al IMCs in the early stage slowed down the growth rate of the IMCs, and the precipitation of Pd element in the late stage to form a better Pd-rich layer hindered the element mutual diffusion behavior between Au and Al.

The development of prefabricated buildings has become one of the primary solutions to transform the traditional construction industry around the world. Incentive policy is one of the important driving factors for the development of prefabricated building. The policy system in the field of prefabricated buildings needs to be improved urgently. However, there is still a dearth of research on how incentive policies exert impact on the development of prefabricated buildings. This paper aims to reveal the impact mechanisms of different types of policies on the development system of prefabricated buildings.

This study categorizes prefabricated building policies, constructs a system dynamics model of prefabricated building policies and conducts scenario simulations to examine the impact and sensitivity of different types of policies on the development system of prefabricated buildings.

The results show that compulsory policies play a greater role in the early stage of prefabricated building development and need to be withdrawn at the right time. Preferential and encouraging policies play an incentive role in the middle and later stages of prefabricated building development. Encouraging policies predominate in the later stage of prefabricated building development. Based on the research results, policy recommendations for prefabricated building development are put forward respectively from the government, developers and consumers.

The research results are expected to make up for the lack of clear policies paths in existing research and provide theoretical references for the formulation and optimization of future policies.

Interactivity is the key to developing digital branding. However, existing research on brand interactivity outcomes is inconsistent and fragmented, lacking a systematic empirical exploration of its effects on consumer responses in the digital context.

Drawing upon the cognition-affection-conation (CAC) framework as our theoretical compass, a meta-analysis was conducted to synthesize and analyze empirical evidence from 144 samples involving 57,952 participants to assess how and when digital brand interactivity influences consumers’ multilevel responses.

Our narrative unfolds with digital brand interactivity as the catalyst, fostering positive consumer behaviors – brand loyalty and purchase intention – through a sequential dance of cognitive mindset shifts (brand experience, engagement and attitude) and affective resonance (trust and emotional attachment). A moderation analysis adds depth, revealing stronger effects in B2C settings for lesser-known brands with hedonic interaction content and among individuals with a collectivist orientation.

Our findings serve as a roadmap for targeted digital marketing strategies, guiding brands, consumers and contextual aspects to optimize the performance of digital branding by harnessing the full potential of digital interactivity.

This study introduces a framework combining CAC and brand-consumer psychology to understand how interactivity affects consumer responses in digital contexts. It delves into dynamic shifts moderated by brand characteristics, consumer traits and contextual factors, offering a holistic view of digital branding’s impact on interactive marketing.

Workers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.

This study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.

A case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.

Existing studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.

Negative brand engagement represents a pervasive and persistent feature of interactivity in online contexts. Although existing research suggests that consumer negativity is potentially more impactful or detrimental to brands than its positive counterpart, few studies have examined negative brand-related cognitions, feelings and behaviours. Building on the concept of brand engagement, this study aims to operationalise negative online brand engagement.

This paper presents the results of nine studies that contributed to the development and validation of the proposed scale. Building on the concept of engagement, Studies 1–3 enhanced the construct conceptualisation and generated items. Study 4 involved validation with an academic expert panel. The process of measure operationalisation and validation with quantitative data was completed in Studies 5–8. Finally, the scale's nomological validity was assessed in Study 9.

The results confirm the multidimensional nature of negative online brand engagement. The validated instrument encompasses four dimensions (cognition, affection, online constructive behaviour and online destructive behaviour), captured by 17 items.

Progress in understanding and dealing with negative online brand engagement has been hampered by disagreements over conceptualisation and the absence of measures that capture the phenomenon. This work enhances managerial understanding of negativity fostering strategies that protect brand engagement and improve firm performance.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

The recent livestreaming commerce has magnified the role of influencer marketing, where the influencers are partnering with brands for product promotion. This study examines the impact of influencer attributes, interaction strategies and parasocial relationships on impulsive buying in livestreaming commerce.

A survey with 368 livestreaming commerce users was analyzed using the symmetric-thinking approach – partial least squares structural equation modeling (PLS-SEM) and asymmetric thinking approach – fuzzy set qualitative comparative analysis (fsQCA).

The results of PLS-SEM indicate that influencer trustworthiness, influencer interactivity and self-disclosure determine parasocial relationships, which in turn influence impulsive buying. The fsQCA finding returned three configurations with various combinations of the causal conditions (i.e. influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty) explaining the formation of impulsive buying.

These findings provide unique linear and nonlinear insights to explain the combinatory effects of influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty on impulsive buying in livestreaming commerce.

This study examined the impact of environmental factors on consumers’ intention to use buy online in-store returns (BORS) services. Specifically, it investigates how integrating environmental knowledge and consequence awareness into the theory of planned behavior (TPB) influences consumers’ intention to adopt BORS services.

Data were collected through a structured questionnaire and analyzed using statistical methods to explore the relationships between attitude, subjective norm, perceived behavioral control, environmental factors and intention to use BORS services.

The findings indicate that attitude, subjective norm and environmental knowledge significantly increase consumers’ intention to use BORS services. Additionally, the interaction of attitude and environmental knowledge further enhances consumers’ intention to use BORS services.

This study contributes to the limited literature on the drivers of consumers’ adoption of BORS services by integrating environmental factors with TPB. It provides new insights into how targeted environmental education and promotion activities can influence consumers’ behavior toward sustainable practices, providing valuable strategies for retailers to support sustainable development goals.