The complex interactions of industrial Policy, structural transformation, economic growth, and competitive strategy within regional industries are examined in this research. Using a dynamic capabilities framework, the study examines the mediating roles of organizational innovation and adaptability in the link between competitiveness and macroeconomic variables. A two-way fixed effects model is used in this study to examine the influence of structural transformation (ST) on Industrial Policy (IP). Using regional data covering the years 2010 to 2022, the research undertaken in this paper explores the dynamics of the Indonesian economy by empirically assessing the consequences of structural change on industrial Policy. In order to establish a comprehensive model that clarifies the mechanisms through which industrial policies and structural shifts impact the development of dynamic capabilities, ultimately influencing competitiveness strategies, this research draws on a large amount of empirical data and integrates insights from seminal works. Our research adds to our knowledge of strategic management in regional industries by providing detailed information on how economic development and policy interventions influence businesses’ ability to adapt and gain a competitive edge. In addition to advancing scholarly discourse, this study offers business executives and politicians valuable insights for managing the intricacies of global economic processes.

This study employed the theory of planned behavior to examine how green urban spaces influence walking behaviors, with a focus on Chongqing’s Jiefangbei Pedestrian Street. Using structural equation modelling to analyse survey data from 401 respondents, this study assessed the relationships between attitudes, subjective norms, perceived behavioral control, walking intentions, and actions. The results revealed that attitudes toward walking (β = 0.335, p < 0.001) and subjective norms (β = 0.221, p < 0.001) significantly predict walking intentions, which strongly determine actual walking behavior (β = 0.379, p < 0.001). Moreover, perceived behavioral control exerts a direct significant impact on walking actions (β = 0.332, p < 0.001), illustrating that both environmental and social factors are crucial in promoting pedestrian activity. These findings suggest that enhancing the appeal and accessibility of urban green spaces can significantly encourage walking, providing valuable insights for urban planning and public health policy. This study can guide city planners and health professionals in creating more walkable and health-conducive urban environments.

Technology development in the agricultural sector is important in the development of Thailand’s economy. The purpose of this research was to study the approach of guidelines for future agricultural technology development to increase productivity in the Agricultural sector in order to develop a structural equation model. The research applied mixed-methodology. Qualitative research by in depth interview from 9 experts and focus group with 11 successful businesspersons for approve this model. The quantitative data gather from firm, in the 500 of agricultural sector by using questionnaire, using statistical tests of descriptive analysis, inferential analysis, and multivariate analysis. The research found guidelines for future agricultural technology development to increase productivity in the Agricultural sector composed of 4 latent. The most important item of each latent were as following: 1) Agrobiology Technology (= 4.41), in important item as choose seeds that for disease resistance and tolerate the environment to suit the cultivation area, 2) Environmental Assessment (= 4.37),, in important item as survey of cultivated areas according to topography with geographic information system, 3) Agricultural Innovation (= 4.30), in important item as technology reduces operational procedures, reduce the workforce and can reduce operating costs, and 4) Modern Management Systems (= 4.13), in important item as grouping and manage as a cooperative to mega farms. In addition, the hypothesis test found that the difference in manufacturing firm sizes. Medium and Small size and large size revealed overall aspects that were significantly different at the level of 0.05. The analysis of the developed structural equation model found that there was in accordance and fit with the empirical data and passed the evaluation criteria. Its Chi-square probability level, relative Chi-square, the goodness of fit index, and root mean square error of approximation were 0.062, 1.165, 0.961, and 0.018, respectively.

This investigation extends into the intricate fabric of customer-based corporate reputation within the banking industry, applying advanced analytics to decipher the nuances of customer perceptions. By integrating structural equation modeling, particularly through SmartPLS4, we thoroughly examine the interrelations of perceived quality, competence, likeability, and trust, and how they culminate in customer satisfaction and loyalty. Our comprehensive dataset is drawn from a varied demographic of banking consumers, ensuring a holistic view of the sector’s reputation dynamics. The research reveals the profound influence of these constructs on customer decision-making, with likeability emerging as a critical driver of satisfaction and allegiance to the bank. We also rigorously test our model’s internal consistency and convergent validity, establishing its reliability and robustness. While the direct involvement of Business Intelligence (BI) tools in the research design may not be overtly articulated, the analytical techniques and data-driven approach at the core of our methodology are synonymous with BI’s capabilities. The insights garnered from our analysis have direct implications for data-driven decision-making in banking. They inform strategies that could include enhancing service personalization, refining reputation management, and improving customer retention efforts. We acknowledge the need to more explicitly detail the role of BI within the research process. BI’s latent presence is inherent in the analytical processes employed to interpret complex data and generate actionable insights, which are crucial for crafting targeted marketing strategies. In summary, our research not only contributes to academic discourse on marketing and customer perception but also implicitly demonstrates the value that BI methodologies bring to understanding and influencing consumer behavior in the banking sector. It is this blend of analytics and marketing intelligence that equips banks with the strategic leverage necessary to thrive in today’s competitive financial landscape.

This study investigates the influence of Environmental, Social, and Governance Disclosures (ESGD) on the profitability of firms, using a sample of 385 publicly listed companies on the Thai Stock Exchange. Data from 2018 to 2022 is sourced from the Bloomberg database, focusing on ESGD scores as indicators of companies’ ESG commitments. The study utilizes a structural equation model to examine the relationships between independent variables; ESGD, Earnings Per Share (EPS), Debt to Assets ratio (DA), Return on Investment Capital (ROIC), Total Assets (TA), and dependent variables Tobin’s Q (TBQ) and Return on Assets (ROA). The analysis reveals a positive relationship between ESGD and TBQ, but not with ROA. Further exploration is conducted to determine if different ESGD levels (high, medium, low) yield consistent effects on TBQ. The findings indicate discrepancies: high and medium ESGD levels are associated with a negative impact on TBQ when EPS increased, whereas low ESGD levels correlate with an increase in TBQ with rising EPS. This nuanced approach challenges the conventional uniform treatment of ESGD in previous research and provides a deeper understanding of how varying commitments to ESG practices affect a firm’s market valuation and profitability. These insights are crucial for firm management, highlighting the importance of ESGD in relation to other financial variables and their effects on market value. This study offers a new perspective on ESGD’s impact, emphasizing the need for differentiated strategies based on ESG commitment levels.

This study investigates the impact of perceived innovative leadership on team innovation performance, with innovation climate acting as a mediating variable. A quantitative research approach, including a survey of team members across various industries, was used to collect data. Analysis through Structural Equation Modeling (SEM) reveals that perceived innovative leadership significantly positively influences team innovation performance, with innovation climate partially mediating this relationship. The findings emphasize the critical role of innovative leadership and a positive innovation climate in fostering organizational innovation, offering valuable insights for management practices. This paper also discusses the study’s limitations and provides directions for future research.