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This study aims to explore Augmented Language Models (ALMs) for synthetic data generation in services marketing and research. It evaluates ALMs' potential in mirroring human responses and behaviors in service scenarios through comparative analysis with five empirical studies.

The study uses ALM-based agents to conduct a comparative analysis, leveraging SurveyLM (Bickley et al., 2023) to generate synthetic responses to the scenario-based experiment in Söderlund and Oikarinen (2018) and four more recent studies from the Journal of Services Marketing. The main focus was to assess the alignment of ALM responses with original study manipulations and hypotheses.

Overall, our comparative analysis reveals both strengths and limitations of using synthetic agents to mimic human-based participants in services research. Specifically, the model struggled with scenarios requiring high levels of visual context, such as those involving images or physical settings, as in the Dootson et al. (2023) and Srivastava et al. (2022) studies. Conversely, studies like Tariq et al. (2023) showed better alignment, highlighting the model's effectiveness in more textually driven scenarios.

To the best of the authors’ knowledge, this research is among the first to systematically use ALMs in services marketing, providing new methods and insights for using synthetic data in service research. It underscores the challenges and potential of interpreting ALM versus human responses, marking a significant step in exploring AI capabilities in empirical research.

This study aims to investigate the relationship between Airbnb and long-term residential rents, using Santa Monica, California, as a case study. In 2015, Santa Monica adopted the home sharing ordinance (HSO), a stringent regulation aimed at restricting short-term rentals (STR). This research examines the implications of this ordinance on the local housing market.

The synthetic control method (SCM) is applied to a panel data set comprising Airbnb listings and residential rents from multiple cities in Los Angeles County. This approach is used to estimate the causal effects of Santa Monica’s HSO on two outcomes: Airbnb listings and residential rents.

The empirical results show a 60% reduction in Airbnb listings in Santa Monica within two years of implementing the ordinance. Despite this significant decrease, the effect of the regulation on rents was not significant. Suggestive evidence indicates that the ordinance’s ineffectiveness in increasing the number of houses allocated to long-term tenants may have contributed to its negligible impact on rental rates.

To the best of the author’s knowledge, this research is the first to use the SCM for evaluating the impact of STR regulations. It offers crucial insights to policymakers on regulating platforms like Airbnb. The study reveals a scenario where a marked decrease in Airbnb activity did not lower residential rents, highlighting the need for context-specific evaluations in understanding the housing market’s dynamics. Additionally, these findings are valuable for investors considering the implications of regulatory changes in the STR sector.

This paper investigates the relationship between commitment to ESG practices and firm performance using a synthetic index based on ESG disclosure and ESG performance scores.

Using the Mazziotta-Pareto aggregation method, we develop a novel synthetic index of ESG engagement based on ESG rating and disclosure. This index is employed in a dynamic panel regression, implemented using the Arellano-Bond estimator, to explain profitability in a sample of 146 listed Canadian firms over the period spanning from 2014 to 2021.

ESG practices may either foster or hinder firm performance. In particular, a synergy emerges between the social and environmental dimensions of ESG practices, shedding light on the relevance of high standards in terms of environmental and social activities.

The study emphasizes the significance of acknowledging the various facets of ESG engagement and the necessity of transcending the current constraints of accessible ESG data and ratings. Synthetic indices combining different types of ESG information may contribute to mitigating the problems created by strategic disclosure on the part of firms, which typically results in undesirable practices such as greenwashing and social washing.

This is the first study that applies the Mazziotta-Pareto method to develop a synthetic index of ESG engagement, tackling each pillar separately. Moreover, when investigating the effect of ESG engagement on profitability, we allow for cross-pillar synergies and/or trade-offs.

This research assesses the economic impact of biomass plant installations on Brazilian municipalities, focusing on (1) labor income, (2) sectoral labor income and (3) income inequality.

Municipal data from the Annual Social Information Report, the National Electric Energy Agency and the National Institute of Meteorology spanning 2002 to 2020 are utilized. The Synthetic Difference-in-Differences methodology is employed for empirical analysis, and robustness checks are conducted using the Doubly Robust Difference in Differences and the Double/Debiased Machine Learning methods.

The findings reveal that biomass plant installations lead to an average annual increase of approximately R$688.00 in formal workers' wages and reduce formal income inequality, with notable benefits observed for workers in the industry and agriculture sectors. The robustness tests support and validate the primary results, highlighting the positive implications of renewable energy integration on economic development in the studied municipalities.

This article represents a groundbreaking contribution to the existing literature as it pioneers the identification of the impact of biomass plant installation on formal employment income and local economic development in Brazil. To the best of our knowledge, this study is the first to uncover such effects. Moreover, the authors comprehensively examine sectoral implications and formal income inequality.

This study aims to analyze the factors influencing households housing tenure choices in Togo.

The authors applied a rigorous econometric approach, using Harmonized Household Living Conditions Survey (EHCVM) data from 2018 and 2021 to construct a longitudinal panel, and Unified Basic Welfare Indicators Questionnaire (QUIBB) data from 2006, 2011 and 2015 to construct a pseudo-panel.

The study reveals that a household’s life-cycle variables like age of the household head, marital status, household size and place of residence, have a significant influence on homeownership. In addition, households in the highest wealth quartiles and used heads of household are more likely to own their home.

Housing policies focused on improving the financial sustainability of low-income households and reducing the transaction costs associated with property acquisition are essential to promoting homeownership.

As part of the implementation of the Government Roadmap 2020–2025, the government has committed to providing 20,000 affordable social housing units, aiming to significantly boost the supply of decent housing. However, the findings of this study highlight the need for targeted subsidy programs for low-income households, particularly for female-headed households and those living in urban areas. These subsidies could cover part of the cost of purchasing homes. For middle-income households, it is crucial to develop suitable financing mechanisms, such as low-interest mortgages and loan guarantees. Given demographic pressures and the high cost of public housing programs, promoting self-build remains essential. This support should be accompanied by the provision of low-cost building materials and technical training in innovative, sustainable construction methods. Additionally, improved access to employment and land regularization are essential prerequisites for the success of these initiatives.

Research on the determinants of tenure choice is relatively limited in sub-Saharan Africa due to the unavailability of housing survey data. This paper proposes a case study of Togo, whose housing market characteristics correspond to most sub-Saharan African countries. Furthermore, this study applied two methodological approaches commonly used in dynamic analyses, thereby enhancing the robustness of the findings.

The purpose of this paper is to construct a grey incidence model for panel data that can reflect the incidence direction and degree between indicators.

Firstly, this paper introduces the concept of a negative matrix and preprocesses the data of each indicator matrix to eliminate differences in dimensions and magnitudes between indicators. Then a model is constructed to measure the incidence direction and degree between indicators, and the properties of the model are studied. Finally, the model is applied to a practical problem.

The grey-directed incidence degree is 1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a positive linear relationship. This degree is −1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a negative linear relationship.

The example shows the number of days with good air quality is negatively correlated with the annual average concentration of each pollutant index. PM_2.5, PM₁₀ and O₃ are the main pollutants affecting air quality in northern Henan.

This paper introduces the negative matrix and constructs a model from the holistic perspective to measure the incidence direction and level between indicators. This model can effectively measure the incidence between the feature indicator and factor indicator by integrating information from the point, row, column and matrix.

The purpose of this paper is to explore a new grey relational analysis model to measure the coupling relationship between the indicators for the water environment status assessment. Meanwhile, the model deals with the problem that the changing of indicator order may result in the changing of the degree of grey relation.

The binary index submatrix of the sample matrix is given first. Then the product of the matrix and its own transpose is used to measure the characteristics of the index and the coupling relationship between the indicators. Thirdly, the grey relational coefficient is defined based on the matrix norm, and a grey coupling relational analysis model is proposed.

The paper provides a novel grey relational analysis model based on the norm of matrix. The properties, normalization, symmetry, relational order invariance to the multiplicative, are studied. The paper also shows that the model performs very well on the water environment status assessment in the eight cities along the Yangtze River.

The model in this paper has supplemented and improved the grey relational analysis theory for panel data.

Logistics sector is recognized as one of the core enablers of the economic development of a nation. However, inefficiency in logistics operations impedes the achievement of intended targets by increasing the cost of doing business. Also, it is difficult to improve the efficiency of a country’s logistics operations without a metric for evaluating and understanding logistics capabilities and efficiency. Therefore, the present study has developed In-country Logistics Performance Index (ILP Index) to propose a benchmarking tool to measure the in-country logistics competitiveness, particularly in the setting of emerging economies, i.e. India.

This study has developed a unified index using principal component analysis and quintile approach. In addition, the proposed index relies on several dimensions that are developed and illustrated using quantitative secondary panel data.

The findings of this study reveal that the quality of infrastructure, economy, and telecommunications are the three most important dimensions that may significantly support the growth of the transportation and logistics sector. The results reveal that Gujarat, Tamil Nadu, and Maharashtra are the top performers whereas, Bihar, Jharkhand, and Jammu and Kashmir scores the least due to the insufficient logistics infrastructure as compared to other Indian states.

Given the extensive focus on international-level logistics index (like World Bank’s LPI) in the existing literature, this study intends to develop in-country logistics index to evaluate the logistics capabilities at the regional and state level. In addition, unlike prior studies, this study utilizes quantitative secondary data to eliminate cognitive and opinion bias. Moreover, this benchmarking tool would assist decision-makers in idealizing standard practices toward sustainable logistics operations. Additionally, the ILP index could serve the international investors in crucial decision-making, as it provides valuable insights into a country’s logistics readiness, influencing their investment choices and trade preferences. Finally, the proposed approach is adaptable to measuring the overall performance of any other industry/economy.

This study aims to examine the links between carbon emissions, electric vehicles, economic growth, energy use, and urbanisation in 15 countries from 2010 to 2020.

This study adopts seminal panel methods of moments quantile regression with fixed effects to trace the distributional aspect of the relationship. The reliability of methods is confirmed via fully modified ordinary least squares coefficients.

This study reveals that fossil fuel use, economic activity, and urbanisation negatively impact environmental quality, whereas renewable energy sources have a significant positive long-term effect on environmental quality in the selected panel of countries.

The main limitation of this study is the generalisability of the findings, as the study is confined to a limited number of countries, and focuses on non-renewable and renewable energy sources.

Finally, this study proposes several policy recommendations for decision-makers and policymakers in the 15 nations to address climate change, boost sales of electric vehicles, and increase the use of renewable energy sources.

This study calls for a comprehensive transition towards green energy in the transportation sector, enhancing economic growth, fostering employment opportunities, and improving environmental quality.

The present study aims to identify significant barriers to adopting prefabricated construction (PFC) in developing economies using a study in Sri Lanka and develop an integrated strategy framework to mitigate and overcome the obstacles.

The research process included a comprehensive literature review, a pilot study, a questionnaire survey for data collection, statistical analysis and a qualitative content analysis.

Ranking method revealed that all 23 barriers were significant. Top significant barriers include challenges in prefabricated component transportation, high capital investment costs and lack of awareness of the benefits of PFC among owners/developers. Factor analysis clustered six barrier categories (BCs) that fit the barrier factors, explaining 71.22% of the cumulative variance. Fuzzy synthetic evaluation revealed that all BCs significantly influence PFC adoption in Sri Lanka. Finally, the proposed mitigation strategies were mapped with barriers to complete the integrated framework.

The study outcomes are relevant to construction industry stakeholders of Sri Lanka, who are keen to enhance construction efficiencies. The implications can also benefit construction industry stakeholders and policymakers to formulate policies and regulations and identify mitigation solutions.

The study provides deeper insights into the challenges to adopting prefabrication in South Asian countries such as Sri Lanka. Furthermore, the integrated framework is a novel contribution that can be used to derive actions to mitigate barriers in developing economies.