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This study aims to analyze how restaurants' collaboration with mobile food delivery applications (MFDAs) affects product development efficiency and argues that technological capabilities moderate relational ties impact the joint decision-making and development efficiency of restaurant products.

A product development efficiency model was formulated using a resource-based view and real options theory. In all, 472 samples were collected from restaurants collaborating with MFDAs, and partial least squares structural equation modeling was applied to the proposed model.

The findings of this study indicate three factors are critical to the product development efficiency between restaurants and MFDAs; restaurants must develop a strong connection with the latter to ensure meals are consistently served promptly. Developers of MFDAs should use artificial intelligence analysis, such as order records of different genders and ages or various consumption attributes, to collaborate with restaurants.

To the best of the authors’ knowledge, this study is one of the few that considers the role of MFDAs as a product strategy for restaurant operations, and the factors the authors found can enhance restaurants’ product development efficiency. Second, as strategic artificial intelligence adaptation changes, collaborating firms and restaurants use such applications for product development to help consumers identify products.

The social media communication of luxury service firms remains largely unexplored. This study explores the influence of firm-created social media communication (FCSMC) on predicting brand evangelism (BEM) via perceived values, including functional value (FV), emotional value (EV) and social value (SV), by embedding the direct and moderating influence of customer experience (CX) on brand evangelism in the luxury hotel sector.

This study recruited 405 regular travelers to participate in an online survey. Following meticulous data curation, the empirical analysis was performed on 363 responses using structural equation modeling.

The findings revealed that FCSMC substantially impacted perceived values, including FV, EV and SV, as well as BEM. Likewise, perceived values, including FV and EV, were positively associated with BEM. In addition, this study revealed that CX exhibited significant predictive capability with its direct and moderating effects on BEM in the luxury hotel sector.

This original research advances the uses and gratifications theory and attribution theory. It provides novel theoretical insights and practical recommendations for the luxury hotel sector.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

Because family businesses are highly complex enterprises, researchers need appropriate theoretical and methodological tools to study them. The neoconfigurational perspective and its accompanying method, qualitative comparative analysis, are particularly well suited to phenomena characterized by complex causality, but their uptake in family business research has been slow and fragmented. To remedy this, the authors highlight their unique ability to address research questions for which other approaches are not well suited and discuss how they might be applied to family business phenomena.

The authors introduce the core tenets of the neoconfigurational perspective and how its set-theoretic epistemology differs from traditional approaches to theorizing and analysis. The authors then use a dataset of family firms to present a primer on conducting qualitative comparative analysis and interpreting the results.

The authors find that family firm resources can be combined in multiple ways to affect business survival, suggesting that resources are substitutable and complementary. The authors discuss how the unique features of the neoconfigurational approach, namely equifinality, conjunctural causation and causal asymmetry, can be fruitfully applied to break new ground in scholarly understanding of family businesses.

This article allows family business researchers to apply the neoconfigurational approach without first having to consult multiple and disparate sources often written for other disciplines. This article explicates how to leverage the theoretical and empirical advantages of the neoconfigurational approach in the context of family businesses, supporting a more widespread adoption of the neoconfigurational perspective in family business research.

Introduction: India has the 15th-largest domestic natural gas consumption (NGC), critical to sustainable economic growth. Promoting natural gas will have a crucial impact on production in all industries.

Purpose: This research gives an overview of NGC and gross domestic product (GDP) in India from 1990 to 2021 and investigates the association and nature of causality between NGC and GDP in India.

Methodology: For the years 1990 through 2021, we used annual statistics from the NGC and the GDP of India. Both research variables data have been taken from the World Bank Indicator.

Findings: There is no causality and correlation between natural gas and GDP in India.

Practical Implications: Based on the research, the Government of India can create different policies for substituting natural gas for other energy sources to have a healthier impact on a sustainable environment in the short and long term. In the future, researchers can work on environmental degradation and GDP.

Although the published studies have noted that ambidextrous marketing capabilities (AMCs) could improve firm performance, they seem to ignore the differences between mature corporations and new ventures (NVs). Generally, it is impossible for NVs to simultaneously possess two types of marketing capabilities such as marketing exploitation and marketing exploration. They have to make a trade-off between the present market and the future market. This paper seeks to investigate the causal relationship between AMCs (exploitation-dominated AMCs and exploration-dominated AMCs) and entrepreneurial performance in the context of NVs. Furthermore, this paper attempts to explore the internal interaction of entrepreneurial orientation and the external interaction of competitive intensity.

This paper develops a theoretical framework according to configuration theory and investigates the causal relationship between AMCs (exploration-dominated AMCs and exploitation-dominated AMCs) and entrepreneurial performance as well as the moderating roles played by entrepreneurial orientation and competitive intensity based on the survey data collected from 257 Chinese NVs.

The results show that both the exploration-dominated AMCs and the exploitation-dominated AMCs have significant positive effects on entrepreneurial performance. However, the moderating roles played by entrepreneurial orientation and competitive intensity in the causal relationships are different and complicated. Specifically, entrepreneurial orientation negatively moderates the relationship between exploitation-dominated AMCs and entrepreneurial performance and positively moderates the relationship between exploration-dominated AMCs and entrepreneurial performance; competitive intensity positively moderates the relationship between exploitation-dominated AMCs and entrepreneurial performance and negatively moderates the relationship between exploration-dominated AMCs and entrepreneurial performance.

This paper plays a pioneering role in enriching the theoretical connotation of AMCs, improving the theoretical framework of AMCs and expanding the theoretical application of AMCs by analyzing and confirming the causal relationships between AMCs and entrepreneurial performance in the context of NVs, which is different from the previous studies. In addition, this paper also makes a valuable contribution to management practices, such as leading NVs, to match different types of AMCs with internal and external conditions for improving entrepreneurial performance.

This study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.

This study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.

The ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.

To the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

This study aims to modify the human factors analysis and classification system (HFACS) to make it suitable for collapse accident analysis in construction. Based upon the modified HFACS, distribution patterns of causal factors across multiple levels were discerned among causal factors of various stakeholders at construction sites. It explored the correlations between two causal factors from different levels and further determined causation paths from two perspectives of level and stakeholder.

The main research framework consisted of data collection, coding and analysis. Collapse accident reports were collected with adequate causation information. The modified HFACS was utilized for coding causal factors across all five levels in each case. A hybrid approach with two perspectives of level and stakeholder was proposed for frequency analysis, correlation analysis and path identification between causal factors.

Eight causal factors from external organizations at the fifth level were added to the original HFACS. Level-based correlation analyses and path identification provided safety managers with a holistic view of inter-connected causal factors across five levels. Stakeholder-based correlation analyses between causal factors from the fifth level and its non-adjacent levels were implemented based on client, government and third parties. These identified paths were useful for different stakeholders to develop specific safety plans for avoiding construction collapse accidents.

This paper fulfils an identified need to modify and utilize the HFACS model for correlation analysis and path identification of causal factors resulting in collapse accidents, which can provide opportunities for tailoring preventive and protective measures at construction sites.

This study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.

This paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.

The results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.

The interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.