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How to reduce uncertainty in clothing online purchasing has become a primary concern for consumers. This study aims to investigate the effects of quality perception and multisensory perception on consumer online clothing purchase intention and to explore the mediating effect of trust.

This study was conducted with consumers who have online clothing shopping experiences. A total of 539 valid samples were analyzed by bootstrap and structural equation model.

The result of this research revealed that perceived information quality, perceived service quality, perceived haptic imagery and perceived visual-audio cues have a significant influence on purchase intention. Additionally, consumers’ trust is positively correlated with purchase intention. Trust mediates the relationship between the perceptions (perceived information quality, perceived service quality, perceived haptic imagery and perceived visual-audio cues) and purchase intention, irrespectively.

To investigate the effects of multisensory perception on purchase intention, perceived haptic imagery is adopted as a variable in this study. This study investigates the mediating role of trust between multisensory perception and purchase intention. The findings of this study can enrich Stimulus-Organism-Response (S-O-R) framework and Information System (IS) success model in the field of clothing online shopping.

The point of divergence for the authors’ analysis is the observation that research on the development of professional skills did not provide empirical support to a possible positive relationship between innovative culture and development of professional skills. The author believes that the injection of intervening variables has the potential to do just that. The purpose of this paper is to understand such contingencies through a developed moderated mediation model, which jointly examines supportive leadership as the mediating mechanism and individual power distance orientation as a moderator and to increase the theoretical validity and precision of investigating the development of professional skills.

The survey data were collected from 317 information technology (IT) professional technical engineers and their supervisors from high-tech sectors. The authors tested the hypotheses by hierarchical regression and followed Baron and Kenny (1986) instruction to examine our moderated mediation model. The authors used a series of confirmatory factor analyses (CFAs) to verify the constructs’ distinctiveness before testing the hypotheses was performed. Meanwhile, in order to test the mediating effect, the three-equation approach to testing mediation, as recommended by Baron and Kenny (1986), was used.

The strong support for schema theory in this study suggests that the development of professional skills can be notably promoted through a moderated mediation model which integrates the link between innovative culture and professional skills through the mediating effect of supportive leadership and the direct effects are mitigated by the moderating effect of individual power distance orientation. It highlights the importance of appropriately matching innovative culture and supportive leadership with the power distance orientation of employees. This universalistic organizational behavior approach has worked effectively in an Asian sample.

This study provides a better understanding of work motivation by showing that an employee uses schemas to interpret the relationships among perceived innovative culture, individual power distance orientation, supportive leadership and development of professional skills. This paper also illustrates how perceived innovative culture can act as an positive motivator to inspire IT technical engineers’ development of professional skills, and how individually held power distance orientation may positively or negatively influence the relationship between perceived innovative culture and supportive leadership. Hence, this study has extended the schema theory in organizations and informed the literature on supportive leadership.

To improve the apparel e-customized system and meet consumers’ personalized requirement in the e-customization, the purpose of this paper is to develop an e-customized co-design system on garment design (ECS-GD) that enables users to co-design garment and communicate with stakeholders.

The e-customized co-design system mainly consists of function and modular structure, communication and evaluation modular. Based on the evolutionary algorithm and fuzzy theory, the e-customized system model is proposed with a presentation of garment interface. Based on the parameters of skirts, technical sketches, fabrics, color, patterns, comments and scores, the decision is made by consumers.

For the system to be effective, the system was conducted by multi-individuals co-work (consumers, designers, manufacturers and experts). The data flow was congruent to the design knowledge. The expert evaluation and communication were involved in the proposed system.

The limitation of this study is that the system is not tested in experiments. The model, main function and data flow are proposed in this study, which is important in the e-customized co-design system development.

Compared to the e-shopping and garment recommendation, the proposed ECS-GD is a good approach to improve the existing e-customized system and a well solution to help consumers with design knowledge recommending, professional suggestions and evaluations. Besides, the sketches and knowledge recommendation are provided to consumers which is a learning process.

The purpose of this paper evaluates the effects of the Great Western Development (GWD) policy on agricultural intensification, land use, agricultural production and rural poverty in western China.

The authors collect county-level data on land use, input application, grain crop production, income, poverty and geophysical characteristics for 1996–2005 and use a quasi-natural experimental design of difference-in-differences (DD) in the empirical analysis.

Results suggest that the GWD policy significantly increased the grain crop production in western China. This increase resulted from higher yield, with increased fertilizer use and agricultural electricity consumption per hectare, and more land allocated to grow grain crops. The policy also increased land-use concentration, reduced crop diversity and alleviated rural poverty in western China.

This paper makes three contributions. First, the authors add to the growing literature on the GWD policy by evaluating its effects on farm household decisions and exploring the mechanisms and broad socioeconomic impacts in western China. Second, the authors take advantage of a quasi-natural experimental design to improve the identification strategy where input use, land allocation, production and off-farm labor participation are all endogenous in a farm household. Third, the authors explore a long list of variables within one integrated dataset to present a comprehensive picture of the impact of the GWD policy.

As an automatic welding process may experience some disturbances caused by, for example, splashes and/or welding fumes, misalignments/poor positioning, thermally induced deformations, strong arc lights and diversified welding joints/grooves, precisely identifying the welding seam has a great influence on the welding quality. This paper aims to propose a robust method for identifying this seam based on cross-modal perception.

First, after a welding image obtained from a structured-light vision sensor (here laser and vision are integrated into a cross-modal perception sensor) is filtered, in a sufficiently small area, the extended Kalman filter is used to prevent possible disturbances to search for its laser stripe. Second, to realize the extraction of the profile of welding seam, the least square method is used to fit a sequence of centroids determined by the scanning result of columns displayed on the tracking window. Third, this profile is then qualitatively described and matched using a proposed character string method.

It is demonstrated that it maintains real time and is clearly superior in terms of accuracy and robustness, though its real-time performance is not the best.

This paper proposes a robust method for automatically identifying and tracking a welding seam.

In this paper, Yale model was adopted to understand how the characteristics of a green message (goal framing, argument volume and argument consistency), its source (source credibility) and its receivers (environmental consciousness) influence the communication process regarding the receivers' perceptions and attitudes (information value, persuasiveness and communication effectiveness) and their intention to spread the green message.

Eight scenarios of factorial design were developed to test the effects of a message on receivers' perceptions and attitudes. 1,157 valid questionnaires were used to conduct AMOS-SEM and ANOVA analyses.

The analytical results showed significant differences between the persuasive effects of Scenarios 1 and 8. A high level of environmental consciousness significantly affected the relationships between the message characteristics, source credibility and information value, leading to a higher intention to share the green messages.

The Yale model was applied to online persuasion to examine the receivers' perceptions of and attitudes toward a green message and their intention to share it on social networking sites (SNSs). Being environmentally conscious can strengthen the perception that the receivers of an online green message have of the source credibility and information value. The Yale model proved to represent the online green message-sharing context and explain how to persuade online users more effectively.

This study suggests that green message providers should present content emphasizing the losses associated with inaction, thus increasing the receivers' acceptance of the message's persuasiveness. Additionally, with the assistance of supportive measures, such as national education programs, government policies and actions showing corporate social responsibility, the environmental awareness of individuals can encourage them to share green message posts on SNSs and engage in green practices.

A significant effect of the message characteristics (goal framing × argument volume × argument consistency) was observed on SNS users' perceived source credibility and information value. Therefore, key opinion leaders, governments, and organizations who want to promote green ideas and attract public attention are encouraged to provide messages emphasizing the costs of inaction and contain arguments with a sufficient number of responses strongly supporting the original message. This could arouse the concern for green issues among online communities.

A Yale model research framework was developed to investigate the effect of message characteristics on the intention to share green messages online. Receivers' environmental consciousness played a vital role in this message-sharing process.

This study aims to assess carbon emissions in urban aged residential buildings in Qingdao, Shandong Province, constructed prior to 2000, and to evaluate retrofitting and rebuilding strategies for potential carbon reduction.

Field investigations and literature reviews were conducted to identify key factors influencing carbon emissions, such as shape coefficient, window-to-wall ratio and envelope structure. A combination of generalization and mathematical statistical methods was used to classify buildings based on construction year, form, structural type and energy-saving goals. Cluster analysis was employed to extract six typical building models.

Results demonstrate that building form complexity positively correlates with carbon emissions per unit area, while longer lifespans reduce emission intensity. Retrofitting exhibits shorter carbon payback periods (1.62–3.92 years) than rebuilding (18.7–49.94 years), indicating superior environmental performance. Pre-1986 buildings are advised for demolition/rebuilding due to limited retrofit benefits. For 1986–1995 buildings, retrofitting is recommended if structurally viable. Post-1996 buildings favor retrofitting over new construction for its shorter payback and lower emissions, enhancing long-term carbon reduction.

This study contributes to the understanding of carbon emissions in urban aged residential buildings by considering various factors and providing specific recommendations for retrofitting and rebuilding strategies tailored to different construction periods. Additionally, it highlights the importance of building form complexity and remaining lifespan in determining carbon emissions, offering insights for sustainable urban development and carbon reduction initiatives.

This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues.

The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques.

The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction.

This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.

Over the last few decades, management has witnessed a proliferation of research on mergers and acquisitions (M&As) and strategic alliances (SAs). Although both fields have been widely studied, the relationship between the two bodies of literature has not been sufficiently explored. Despite the enormous commonality between both phenomena in terms of the drivers behind them and of the critical success factors associated with the M&A and alliance process management, scholars from the two fields have rarely exchanged findings and insights, even though they may be highly relevant to each other. M&A and SA research remain mostly separated from each other, thus minimizing the ability for more mutually beneficial complementary and synergetic knowledge sharing effects.

This chapter synthesizes and compares existing theoretical perspectives from the M&A and SA literatures and identifies opportunities for future research and knowledge cross fertilization between the two fields. Building upon previous review studies about M&A and SA literatures, the authors develop a comparative longitudinal review of both literatures published in top management journals over a 27-year period. For that purpose, the authors resort to machine learning algorithms to discover thematic patterns that may have gone unnoticed by using traditional review methods.

By highlighting some of the shortcomings that limit the authors’ theoretical and practical understandings, they challenge scholars from both fields (M&A and SA) to go beyond what they think they know from compartmentalized received theory, and draw upon novel and meaningful ideas, concepts, and theoretical approaches from “the other side of the fence.” The authors believe that such a dialog will facilitate further theoretical exploration and empirical investigation of both phenomena and produce insights that will influence the practical management of M&A and SAs.

A new method for forecasting wind turbine capacity of China is proposed through grey modelling technique.

First of all, the concepts of discrete grey model are introduced into the NGBM(1,1) model to reduce the discretization error from the differential equation to its discrete forms. Then incorporating the conformable fractional accumulation into the discrete NGBM(1,1) model is carried out to further improve the predictive performance. Finally, in order to effectively seek the emerging coefficients, namely, fractional order and nonlinear coefficient, the whale optimization algorithm (WOA) is employed to determine the emerging coefficients.

The empirical results show that the newly proposed model has a better prediction performance compared to benchmark models; the wind turbine capacity from 2019 to 2021 is expected to reach 275954.42 Megawatts in 2021. According to the forecasts, policy suggestions are provided for policy-makers.

By combing the fractional accumulation and the concepts of discrete grey model, a new method to improve the prediction performance of the NGBM(1,1) model is proposed. The newly proposed model is firstly applied to predict wind turbine capacity of China.