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The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Women make a major contribution to the agricultural sector, especially in developing countries. Despite this, women still face many obstacles in carrying out their agricultural activities. These obstacles have a negative impact on their productivity and create a gender gap. This paper analyses the difference in agricultural productivity between male-headed and female-headed households in Burkina Faso.

Using data from the Permanent Agricultural Survey (EPA), we applied the Blinder-Oaxaca decomposition method to determine the size of the gender gap and identify the variables explaining this gap. In this study, we used the value of production per farm worker as a measure of productivity.

The results indicate a gender gap of 43.8 percentage points in favor of male-headed households. Around 131% of this difference is explained by differences in observable household characteristics. The factor that most explains this difference in productivity is the difference in the total area of land available to households.

This finding calls for women’s access to land to be considered in the design and implementation of agricultural development policies.

One of the main contributions of this article in relation to previous studies lies in the unit of analysis. Rather than focusing on individual producers, as in previous studies, we have instead considered the household as the unit of analysis, since in developing countries such as Burkina Faso, production decisions are taken at household level. It contributes to inform economic policy decisions by providing decision-makers with the factors on which they can act to bring about an increase in agricultural productivity by reducing the gap between male-headed households and female-headed households.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2023-0923

The banking industry, which is one of the most significant industries when taking into account both deposit sizes and employment statistics in Turkey, is one of the country's primary economic drivers. In this regard, it is highly important to evaluate banks as it is necessary to present to what extent they use their resources efficiently. The main purpose of the study is to analyze the efficiencies of Turkish banks by the two-stage data envelopment analysis (DEA) and Malmquist productivity index (MPI).

The authors aim to analyze both the efficiency and productivity of Turkish banks by two-stage DEA and the MPI, which enable decomposing into sub-sections of production processes. Hence, more detailed insight into the Turkish banking system can be presented through two-stage efficiency and production approaches.

DEA results indicate that two out of three state-owned banks achieved resource efficiency while none of the investigated banks performed profit efficiency throughout the investigated period. Besides, average resource efficiency is found higher than average profit efficiency in Turkish banks. MPI results reveal that both technological and technical improvement prospects exist for Turkish banks.

The original contribution of this paper is to employ two-stage DEA and the MPI, which reflect both the static and dynamic performance of the Turkish banking sector. In this regard, this study aims to be a pioneer by both reflecting the static and dynamic performance analysis of Turkish banks.

Taiwan’s hotel industry was adversely impacted by the COVID-19 pandemic. This study aims to examine the effect of strategic choices by Taiwanese international tourist hotels before and during the pandemic environments.

A data envelopment analysis (DEA)-based Malmquist methodology is used in this study to provide a mechanism to assess Taiwanese hotel strategy performance. Changes in the productivity and performance of Taiwanese international tourist hotels were analyzed in the periods before and during the pandemic to uncover insights useful should a similar crisis occur in the future. Panel data were obtained from the annual report of international tourist hotels published by the Taiwan Tourism Bureau from 2017–2020. Two groups of hotels were analyzed in this study: city hotels and scenic hotels.

The findings of this study reveal that chain hotels tended to perform better than independent hotels in both city and scenic areas during the global pandemic. Specifically, the crisis caused a substantial decline in productivity and profitability for international tourist hotels in Taipei City during the COVID-19 period. Compared to city hotels, findings also indicate that most international tourist hotels in scenic areas were able to maintain better productivity, including larger-sized scenic hotels.

The DEA-based analysis provides unique and valuable insights for hotel firm leaders on how to better identify and make strategic choices when responding to future crises.

This study aims to explain the market-oriented transformation dilemma of Chinese urban investment and development companies (UIDCs; also known as local government investment and financing companies) and objectively evaluate their transformation efficiency from both static and dynamic perspectives. The results of the research provide methodological bases for improving the transformation efficiency of UIDCs, thus pointing out the direction for the rational planning of their transformation path.

This study takes Chinese UIDCs in market transformation during 2015–2019 as the research object and uses principal component analysis to screen the index system for measuring the efficiency of market transformation. It then uses a three-stage data envelopment analysis model and the Malmquist productivity index to evaluate the market transformation efficiency of these companies during 2015–2019 and comprehensively analyzes the influence of external environmental factors on the market transformation of Chinese UIDCs.

Research results show that the transformation efficiency of Chinese UIDCs is low and slow overall and that large spatial and temporal differences exist. The transformation efficiency of UIDCs located in eastern China is higher than that of UIDCs in central and western China. The higher the external environmental factors of regional GDP, local debt service pressure and credit rating, the more likely they are to cause input redundancy in the transformation process of Chinese UIDCs, which is not conducive to their market-oriented transformation. In addition, the higher the urbanization rate, the more effective it is to improve the efficiency of market-oriented transformation of UIDCs. If the influence of environmental factors is stripped away, both the overall efficiency value and pure technical efficiency value of market-oriented transformation of Chinese UIDCs will increase while the scale efficiency value becomes smaller.

This research measures the transformation efficiency of Chinese UIDCs and comprehensively analyzes the influence of external environmental factors on their market-oriented transformation. The goal is to enrich the study of the market-oriented transformation efficiency evaluation index system of Chinese UIDCs at the theoretical level and provide important reference values for improving the efficiency of market-oriented transformation of Chinese UIDCs at the practical level.

The purpose of this study was to uncover representative emergent areas and to examine the research area of marketing, tourism and big data (BD) to assess how these thematic areas have developed over a 27-year time period from 1996 to 2022. This study analyzed 1,152 studies to identify the principal thematic areas and emergent topics, principal theories used, predominant forms of analysis and the most productive authors in terms of research.

The articles for this research were all selected from the Web of Science database. A systematic and quantitative literature review was performed. This study used SciMAT software to extract indicators. Specifically, this study analyzed productivity and produced a science map.

The findings suggest that interest in this area has increased gradually. The outputs also reveal the innovative effort of industry in new technologies for developing models for tourism marketing. Ten research areas were identified: “destination marketing,” “mobility patterns,” “co-creation,” “gastronomy,” “sustainability,” “tourist behavior,” “market segmentation,” “artificial neural networks,” “pricing” and “tourist satisfaction.”

This work is unique in proposing an agenda for future research into tourism marketing research with new technologies such as BD and artificial intelligence techniques. In addition, the results presented here fill the current gap in the research since while there have been literature reviews covering tourism with BD or marketing, these areas have not been studied as a whole.

El objetivo de esta investigación fue descubrir nichos representativos de áreas emergentes y examinar el área de Marketing, Turismo y Big Data, evaluando cómo han evolucionado estas áreas temáticas durante un período de 27 años desde 1996–2022. Analizamos 1.152 investigaciones para identificar las principales áreas temáticas y temas emergentes, las principales teorías utilizadas, las formas de análisis predominantes y los autores más productivos en términos de investigación.

Todos los artículos para esta investigación fueron seleccionados de la base de datos Web of Science. Realizamos una revisión sistemática y cuantitativa de la literatura. Utilizamos el software SciMAT para extraer indicadores. Específicamente, analizamos la productividad y elaboramos un mapeo científico.

Los hallazgos sugieren que el interés en esta área ha aumentado gradualmente. Los resultados también revelan el esfuerzo innovador de la industria en nuevas tecnologías para desarrollar modelos de marketing turístico. Se identificaron diez áreas de investigación (“marketing de destinos”, “patrones de movilidad”, “co-creación”, “gastronomía”, “sostenibilidad”, “comportamiento turístico”, “segmentación de mercado”, “redes neuronales artificiales”, “precios”, y “satisfacción del turista”).

Este trabajo es único al proponer una agenda para futuras investigaciones en investigación de Marketing Turístico con nuevas tecnologías como Big Data y técnicas de Inteligencia Artificial. Además, los resultados presentados aquí llenan el vacío actual en la investigación ya que si bien se han realizado revisiones de literatura que cubren Turismo con Big Data o Marketing, estas áreas no se han estudiado como un conjunto.

这一特定研究领域的目标是发现具有代表性的新兴领域, 并考察市场营销、旅游和大数据研究领域, 以评估这些主题领域在1996年至2022年的27年间是如何发展的。我们分析了1152项研究, 以确定主要专题领域和新兴主题、使用的主要理论、主要的分析形式以及在研究方面最有成效的作者。

本研究的文章都是从Web of Science数据库中选出的。我们进行了系统化的定量文献审查, 并使用SciMAT软件来提取指标。具体来说, 我们分析了生产力并制作了一个科学研究地图。

研究结果表明, 人们对这一领域的兴趣已经逐渐增加。本文也揭示了工业界在开发旅游营销模式的新技术方面的创新努力。研究确定了十个研究领域：“目的地营销”、“流动模式”、“共同创造”、“美食”、“可持续性”、“游客行为”、“市场细分”、“人工神经网络”、“定价 “和游客满意度”。

这项研究的独特之处在于提出了未来利用大数据和人工智能技术等新技术进行旅游营销研究的议程。此外, 本文的结果填补了目前的研究空白, 因为虽然有文献综述涉及旅游与大数据或市场营销, 但这些领域还没有被作为一个整体来研究。

This literature review aims to portray the thematic landscape of the Pacific Accounting Review (PAR) from 2013 to 2023. This paper also synthesises the special issues in PAR and identifies the main research streams that facilitate contemplating the dialogic interactions between PAR and real-world challenges. Furthermore, this paper aligns these streams with the emerging concerns in Sustainable Development Goals (SDGs) and technological disruptions to propose impactful future directions for publications in PAR.

This review adopts bibliometric analysis to establish the main research streams and objective measures for directing future publications. This paper acquires the data of 310 PAR articles from the Web of Science and ensure the data integrity before the analysis. Based on this technique, this paper also analyses PAR’s productivity, authorship and local and global impacts.

Our bibliometric analysis reveals three key research streams: (1) ESG practices and disclosures, (2) informal institutions in accounting and (3) accounting in transition. This finding affirms PAR’s relevance to real-world accounting challenges. Using a thematic map, this paper portrays the current state of PAR’s topics to identify potential directions for future publications. Further, this paper proposes three future paths for PAR: (1) the research agenda for non-financial reporting, (2) research relating to and from diverse countries considering both formal and informal contemporary contextual factors and (3) the future of the evolving accounting profession.

This study adds value to the existing PAR reviews by extending our knowledge with the latest publications, demonstrating an objective and replicable approach, and offering future directions for PAR publications.

The current study gives a quantitative analysis DT literature over the past ten years in domain of management and business where the field has witnessed a proliferation in studies. The study's primary areas of concentration were analyzing historical trends and identifying prospective future research opportunities in the field. Due of the way, it approaches innovation and problem-solving, design thinking has garnered a lot of interest from both academics and practitioners. Promoters and detractors, however, tend to have quite different perspectives on the program's core qualities, practicality and results.

The study adopts a bibliometric approach to a systematic literature review, where the analyses and visualizations are carried out using R Studio (Biblioshiny package) and VosViewer software. The study was conducted on 518 documents extracted from the Scopus database. To identify past research trends in the field, performance analysis based on productivity and/or impact of the research constituents was carried out to understand the intellectual structure of the field.

The study's findings indicated that few areas have received the most attention, which are presented as seven themes. While the emerging themes in the field include areas such as service design, service innovation, customer experience, innovation management, project management and 21st-century skills.

The studies are going deeper by breaking down concepts or processes and analyzing one aspect at a time, codesign and prototyping are such subareas within the realm of service or new product design and development.

This study is the first of its kind where a literature review has been conducted covering design thinking in the area of business management and accounting.

The study aims to enhance energy efficiency within the high-energy consuming construction industry. It explores the spatial-temporal dynamics and distribution patterns of total factor energy efficiency (TFEE) across China’s construction industry, aiming to inform targeted emission reduction policies at provincial and city levels.

Utilizing a three-stage super-efficiency SBM-DEA model that integrates carbon emissions, the TFEE in 30 Chinese provinces and cities from 2004 to 2019 is assessed. Through kernel density estimation and exploratory spatial data analysis, the dynamic evolution and spatial patterns of TFEE are examined.

Analysis reveals that environmental investments positively impact TFEE, whereas Gross Regional Product (GRP) exerts a negative influence. R&D expenditure intensity and marketization show mixed effects. Excluding environmental and random factors, TFEE averages declined, aligning more closely with actual development trends, showing a gradual decrease from east to west. TFEE exhibited fluctuating growth with a trend moving from inefficient clusters to a more even distribution. Spatially, TFEE demonstrated aggregation effects and characteristics of space-time transition.

This research employs the three-stage super-efficiency SBM-DEA model to measure the total factor energy efficiency of the construction industry, taking into account external environment, random disturbances, and multiple effective decision-making units. It also evaluates energy efficiency changes before and after removing disturbances and comprehensively examines regional and temporal differences from static and dynamic, overall and phased perspectives. Additionally, Moran scatter plots and LISA cluster maps are used to objectively analyze the spatial agglomeration and factors influencing energy efficiency.

This study investigates the impact of temporary employment on various forms of financial distress for firms during the COVID-19 pandemic.

The authors apply a logit model to evaluate the differences in the probabilities of experiencing financial distress for firms with or without temporary reemployment and for firms with different intensities of temporary workers. As an additional test, an ordinal logistic model is applied to reflect different degrees of financial distress.

Our main results indicate that firms with temporary employment are more likely to experience financial distress than firms without temporary employment, regardless of the severity of financial distress. Among firms with temporary employment, our analysis suggests that a firm’s likelihood of experiencing financial distress depends on its relative share (quantile) of temporary workers.

Our findings provide valuable insights for evaluating the impact of temporary employment on firms’ vulnerability during the COVID-19 crisis and suggest strategies for firms to enhance resilience to similar future crises.

Our study is the first one that explores the relationship between temporary employment and financial distress. Firms around the world have been pursuing flexible labor to improve resilience and firm performance. The pandemic may further ramify this trend, creating a future “new normal” regarding employment relationships, job segmentation and gender equality in the job market. This article adds a new dimension to the evaluation of the new normal, which may help firms evaluate the consequences of temporary employment, especially in times of crisis.