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The purpose of this paper is to provide a comprehensive review of the respondents’ fraud phenomenon in online panel surveys, delineate data quality issues from surveys of broad and narrow populations, alert fellow researchers about higher incidence of respondents’ fraud in online panel surveys of narrow populations, such as logistics professionals and recommend ways to protect the quality of data received from such surveys.

This general review paper has two parts, namely, descriptive and instructional. The current state of online survey and panel data use in supply chain research is examined first through a survey method literature review. Then, a more focused understanding of the phenomenon of fraud in surveys is provided through an analysis of online panel industry literature and psychological academic literature. Common survey design and data cleaning recommendations are critically assessed for their applicability to narrow populations. A survey of warehouse professionals is used to illustrate fraud detection techniques and glean additional, supply chain specific data protection recommendations.

Surveys of narrow populations, such as those typically targeted by supply chain researchers, are much more prone to respondents’ fraud. To protect and clean survey data, supply chain researchers need to use many measures that are different from those commonly recommended in methodological survey literature.

For the first time, the need to distinguish between narrow and broad population surveys has been stated when it comes to data quality issues. The confusion and previously reported “mixed results” from literature reviews on the subject have been explained and a clear direction for future research is suggested: the two categories should be considered separately.

Specific fraud protection advice is provided to supply chain researchers on the strategic choices and specific aspects for all phases of surveying narrow populations, namely, survey preparation, administration and data cleaning.

This paper can greatly benefit researchers in several ways. It provides a comprehensive review and analysis of respondents’ fraud in online surveys, an issue poorly understood and rarely addressed in academic research. Drawing from literature from several fields, this paper, for the first time in literature, offers a systematic set of recommendations for narrow population surveys by clearly contrasting them with general population surveys.

This paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.

Because of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.

The results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.

The novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.

The proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

Empirical accounting research frequently makes use of data sets with a time‐series and a cross‐sectional dimension ‐ a panel of data. The literature review indicates that South African researchers infrequently allow for heterogeneity between firms when using panel data and the empirical example shows that regression results that allow for firm heterogeneity are materially different from regression results that assume homogeneity among firms. The econometric analysis of panel data has advanced significantly in recent years and accounting researchers should benefit from those improvements.

This chapter reviews the recent developments in the estimation of panel data models in which some variables are only partially observed. Specifically we consider the issues of censoring, sample selection, attrition, missing data, and measurement error in panel data models. Although most of these issues, except attrition, occur in cross-sectional or time series data as well, panel data models introduce some particular challenges due to the presence of persistent individual effects. The past two decades have seen many stimulating developments in the econometric and statistical methods dealing with these problems. This review focuses on two strands of research of the rapidly growing literature on semiparametric and nonparametric methods for panel data models: (i) estimation of panel models with discrete or limited dependent variables and (ii) estimation of panel models based on nonparametric deconvolution methods.

This paper aims to deal with the grey relational problem of panel data with an attribute value of interval numbers. The grey relational model of interval number for panel data is constructed in this paper.

First, three kinds of interval grey relational operators for the behavior sequence of a dimensionless system are proposed. At the same time, the positive treatment method of interval numbers for cost-type and moderate-type indicators is put forward. On this basis, the correlation between the three-dimensional interval numbers of panel data is converted into the correlation between the two-dimensional interval numbers in time series and cross-sectional dimensions. The grey correlation coefficients of each scheme and the ideal scheme matrix are calculated in the two dimensions, respectively. Finally, the correlation degree of panel interval number and scheme ordering are obtained by arithmetic mean.

This paper proves that the grey relational model of the panel interval number still has the properties of normalization, uniqueness and proximity. It also avoids the problem that the results are not unique due to the different orders of objects in the panel data.

The effectiveness and practicability of the model is verified by taking supplier selection as an example. In fact, this model can also be widely used in agriculture, industry, society and other fields.

The accuracy of the relational results is higher and more accurate compared with the previous studies.

The purpose of the paper is to examine the impact of infrastructure investment and development on economic growth in Brazil, Russia, India, China and South Africa (BRICS) countries. The effect is examined for each country separately and also collectively by combining each country.

Ordinary least square regression method is applied to examine the effects of infrastructure investment and development on economic growth for each country. Panel data techniques such as panel least square method, panel least square fixed-effect model and panel least square random effect model are used to examine the collective impact by combining all countries in BRICS. The dynamic panel model is also incorporated for analysis in the study.

The results of the study are mixed. The association between infrastructure investment and development and economic growth for countries within BRICS is not robust. There is an insignificant relationship between infrastructure investment and development and economic growth in Brazil and South Africa. Energy and transportation infrastructure investment and development lead to economic growth in Russia. Telecommunication infrastructure investment and development and economic growth have a negative relationship in India, whereas there is a negative association between transport infrastructure investment and development and economic growth in China. Panel data results conclude that energy infrastructure investment and development lead to economic growth, whereas telecommunication infrastructure investment and development are significant and negatively linked with economic growth.

The study is novel as time series analysis and panel data analysis are used, taking the time span for 38 years (1980–2017) to investigate the influence of infrastructure investment and development on economic growth in BRICS Countries. Time-series regression analysis is used to test the impact for individual countries separately, whereas panel data regression analysis is used to examine the impact collectively for all countries in BRICS.

A linear interpolation (Lerp) approach, utilizing a common stochastic trend, is explored to impute missing values in nonstationary panel data models. The Lerp algorithm is considerably faster and easier to use than the leading methods recommended in the statistics literature. It shows through a set of simulations that the Lerp works well, whereas other existing methods fail to perform properly, when the panel data contain a high degree of missingness and/or a strong correlation across cross-sectional units. As an illustration, the method is applied to study the cost-of-living-index dataset with missing values. The test on the imputed panel data provides the supporting evidence for the U.S. economy convergence that depends on the state physical spatial proximities and the state industrial development similarities.

This study aims to examine the relationship between the environment and tourism flows, as well as the economic variables of the 32 states of Mexico for the period 1999–2019 based on data availability. The related literature studying tourism and environmental impacts is scarce at a national level, with most of them being local case studies. Some international studies find that if the relationship exists, it is weak or nonexistent, using CO₂ as a proxy in most cases.

The present study uses panel data and cointegration panel methodologies, while also using geographic information systems to observe the distribution of variables at a state level between tourism and environmental variables.

The findings of the study are as follows: state gross domestic product, the inertia of environmental variables (i.e. volume of water treatment and solid waste), occupied rooms (proxy variable for tourism activity) and average temperature have an impact on the contemporary evolution of environmental variables; national and international tourist variables have no impact on the environment; the panels are integrated in such a way that there is a long-term equilibrium between states and some environmental care variables; and no conclusive evidence is found regarding the impact of tourism activity on the considered environmental variables.

The main limitations and areas of opportunity of the work refer to the amount of data available over time and the precision of the measurement of the variables. The availability, temporality and frequency of the data are also limitations of the research. An example of this is the nonexistence of CO₂ emissions at the state level. Additionally, studying other countries and regions for which there are limitations of data and applied studies is also a challenge.

The results are important for economies (in growth) and societies whose economic growth depends on tourism flows and have done little to reverse the damage that tourism has on the environment.

The models can contribute to study the relation between tourism and environmental variables and could be extended to regions, states and provinces for decision-making on actions to be taken for the present and future.

The originality of the research is innovative for the region: Mexico, Central and Latin America. There are no works that have studied these problems with this methodology and these variables. In terms of originality, the classic models of panel data and cointegration of panel data are useful and easily replicable for others to use for different countries. The results are relevant because there is apparently no relationship between tourism and some environmental variables in the short run, but there exists a weak and strong long-run relation between some of them.

本研究采用面板数据和协整面板模型方法, 同时利用地理信息系统(gis)观察州一级层面旅游和环境方面的变量分布。

本研究根据数据可用性, 研究了墨西哥32个州1999–2019年期间环境与旅游流量及经济变量之间的关系。在国家层面上研究旅游与环境影响的相关文献很少, 而且大多是地方的个案研究。一些国际研究发现, 即使有这种关系, 大多数案例中使用二氧化碳作为替代变量, 这种关系也是很弱或不存在。

i)国家国内生产总值, 环境变量的惯性(即水处理量和固体废物量), 占用的房间(旅游活动的代理变量)和平均温度对环境变量的现有演化有影响。ii)国内和国际旅游变量对环境没有影响。iii)面板数据以这样一种方式集成, 即国家和一些环境变量之间存在一种长期平衡。iv)关于旅游活动对所考虑的环境变量的影响没有确凿的证据。

这项工作的主要局限和机会领域是指随着时间的推移可获得的数据量和变量测量的精度。数据的可用性、时效性和频率也是本研究的局限性。这方面的一个例子是在州一级不存在二氧化碳排放。此外, 由于数据和应用研究的局限, 研究其他国家和地区也是一个挑战。

研究结果对经济增长依赖旅游业流量的经济体和社会具有重要意义, 这些经济体和社会对扭转旅游业对环境的破坏方面做得还不够。

这些模型有助于研究旅游业与环境变量之间的关系, 并可推广到地区、州和省, 以制定当前和未来的行动决策。

这项研究的原创性对该地区(墨西哥、中美洲和拉丁美洲)来说是具有创新性的。没有人用这种方法和这些变量研究过这些问题。就原创性而言, 面板数据和面板数据协整的经典模型是有用的且易于复制, 可供其他国家使用。 研究结果具有一定的相关性, 因为旅游业与部分环境变量在短期内不存在明显的相关性, 但在它们中的一些变量在长期内存在着或强或弱的相关性。

Se examina la relación entre medio ambiente y flujos turísticos, así como variables económicas de los 32 estados de México para el período 1999-2019 basado en la disponibilidad de datos. La literatura relacionada que estudia el turismo y los impactos ambientales es escasa a nivel nacional, siendo la mayoría de ellos estudios de casos locales. Estudios internacionales encuentran que, si la relación existe, es débil o inexistente, utilizando el CO2 como un indicador en la mayoría de los casos.

Se utilizaron metodologías de datos de panel y cointegración de panel, además sistemas de información geográfica para observar la distribución de variables a nivel estatal.

i) El Producto Interno Bruto Estatal, la inercia de las variables ambientales (es decir, volumen de tratamiento de agua y residuos sólidos), habitaciones ocupadas (proxy de la actividad turística) y temperatura promedio tienen un impacto en la evolución contemporánea de las variables ambientales, ii) las variables turísticas nacionales e internacionales no tienen un impacto en el medio ambiente, iii) los paneles están integrados de tal manera que existe un equilibrio a largo plazo entre turismo, crecimiento económico y algunas variables ambientales, y iv) no se encuentra evidencia concluyente con respecto al impacto de la actividad turística en las variables ambientales consideradas.

Las principales limitaciones y áreas de oportunidad del trabajo se refieren a la cantidad de datos disponibles en el tiempo y a la precisión de la medición de las variables. La disponibilidad, temporalidad y frecuencia de los datos también son limitaciones de la investigación. Un ejemplo de ello es la inexistencia de emisiones de CO2 a nivel estatal. Además, el estudio de otros países y regiones para los que existen limitaciones de datos y estudios aplicados también es un reto.

Los resultados son importantes para las economías (en crecimiento) y las sociedades cuyo crecimiento económico depende de los flujos turísticos y que han hecho poco por invertir los daños que el turismo produce en el medio ambiente.

Los modelos pueden contribuir a estudiar la relación entre el turismo y las variables medioambientales y podrían extenderse a regiones, estados y provincias para la toma de decisiones sobre las acciones a emprender para el presente y el futuro.

El artículo proporciona un análisis innovador y exploratorio hacia una perspectiva futura que agrega valor al turismo y la planificación para la sostenibilidad. La relación entre turismo y medio ambiente se ha estudiado durante varios años. La UNTWO ha abordado las consecuencias del turismo en el medio ambiente, particularmente, más basura, mayor consumo de agua, emisiones de CO2 y otros aspectos. Pocos trabajos estudian la relación entre estas variables.

La originalidad de la investigación es innovadora para la región: México, América Central y América Latina. No existen trabajos que hayan estudiado estos problemas con esta metodología y estas variables.

En términos de originalidad, los modelos clásicos de datos de panel y cointegración de datos de panel son útiles y fácilmente replicables para que otros los utilicen en diferentes países.

Los resultados son relevantes porque aparentemente no hay una relación entre el turismo y algunas variables ambientales a corto plazo, existe una relación débil y fuerte a largo plazo entre algunas de ellas.

This paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use of solar panels is becoming widespread, and control problems are increasing. Physical control of the solar panels is critical in obtaining electrical power. Controlling solar panel power plants and rooftop panel applications installed in large areas can be difficult and time-consuming. Therefore, this paper designs a system that aims to panel detection.

This paper designed a low-cost AI-based unmanned aerial vehicle to reduce the difficulty of the control process. Convolutional neural network based AI models were developed to classify solar panels as damaged, dusty and normal. Two approaches to the solar panel detection model were adopted: Approach 1 and Approach 2.

The training was conducted with YOLOv5, YOLOv6 and YOLOv8 models in Approach 1. The best F1 score was 81% at 150 epochs with YOLOv5m. In total, 87% and 89% of the best F1 score and mAP values were obtained with the YOLOv5s model at 100 epochs in Approach 2 as a proposed method. The best models at Approaches 1 and 2 were used with a developed AI-based drone in the real-time test application.

The AI-based low-cost solar panel detection drone was developed with an original data set of 1,100 images. A detailed comparative analysis of YOLOv5, YOLOv6 and YOLOv8 models regarding performance metrics was realized. Gaussian, salt-pepper noise addition and wavelet transform noise removal preprocessing techniques were applied to the created data set under the proposed method. The proposed method demonstrated expressive and remarkable performance in panel detection applications.