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Process mining (PM) has emerged as a leading technology for gaining data-based insights into organizations’ business processes. As processes increasingly cross-organizational boundaries, firms need to conduct PM jointly with multiple organizations to optimize their operations. However, current knowledge on cross-organizational process mining (coPM) is widely dispersed. Therefore, we synthesize current knowledge on coPM, identify challenges and enablers of coPM, and build a socio-technical framework and agenda for future research.

We conducted a literature review of 66 articles and summarized the findings according to the framework for Information Technology (IT)-enabled inter-organizational coordination (IOC) and the refined PM framework. The former states that within inter-organizational relationships, uncertainty sources determine information processing needs and coordination mechanisms determine information processing capabilities, while the fit between needs and capabilities determines the relationships’ performance. The latter distinguishes three categories of PM activities: cartography, auditing and navigation.

Past literature focused on coPM techniques, for example, algorithms for ensuring privacy and PM for cartography. Future research should focus on socio-technical aspects and follow four steps: First, determine uncertainty sources within coPM. Second, design, develop and evaluate coordination mechanisms. Third, investigate how the mechanisms assist with handling uncertainty. Fourth, analyze the impact on coPM performance. In addition, we present 18 challenges (e.g. integrating distributed data) and 9 enablers (e.g. aligning different strategies) for coPM application.

This is the first article to systematically investigate the status quo of coPM research and lay out a socio-technical research agenda building upon the well-established framework for IT-enabled IOC.

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.

Art-infusion has become a common practice among brands across the globe. This is because marketers leverage the uniqueness and prestige of arts to earn superior profits. Hence, this research aims to understand and measure consumers' willingness to pay (WTP) for art-infused products.

A questionnaire was designed based on conjoint analysis and was responded to by 470 respondents from India. The estimation of preference functions in conjoint analysis was intended to use orthogonal arrays to measure WTP.

The study reveals consumers' utility and WTP for different art-infused products. The results indicated that consumers have the highest WTP for products that have artwork dominated by the visual elements of colour, shapes and space.

The paper presents valuable findings for marketers to develop their product design and earn superior profits.

This is the first study in the domain of the art infusion phenomenon that measures WTP for non-luxury art-infused products. Also, this is the first study to measure WTP for different kinds of art forms.

This study aims to examine the application of Wardley mapping on developing and implementing an information technology (IT) strategy in a small-scale business. This case study illustrates the potency of multitier analysis using Wardley mapping technique to explore capability gaps and areas to focus on before undertaking IT projects.

This study uses a single case of enterprise resource planning implementation and subsequent abandonment in a small-scale manufacturing company.

This study demonstrates that multitier analysis using Wardley mapping enables a comprehensive examination of organizational capabilities, including a granular analysis of issues that must be managed for successful implementation of an IT project.

While the findings of a single case study offer limited generalizability, this study sets out the approach that can be extended in more complex settings.

The approach to understanding the organizational capabilities required to execute IT projects, or any change management project, offers significant benefits to managers and leaders.

The authors make two distinct contributions: The Wardley mapping technique has emerged as a new tool for understanding business models, particularly in government projects notable in the UK. This study extends its application to the small-scale manufacturing sector. The second contribution is elaborating on multitier analysis, which allows a granular analysis of issues that can impair the IT project implementation, including an assessment of the organizational capabilities required to deliver a successful project.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

Additive manufacturing (AM) is growing economically because of its cost-effective design flexibility. However, it faces challenges such as interlaminar weaknesses and reduced strength because of product anisotropy. Therefore, the purpose of this study is to develop a methodology that integrates design for additive manufacturing (AM) principles with fused filament fabrication (FFF) to address these challenges, thereby enhancing product reliability and strength.

Developed through case analysis and literature review, this methodology focuses on design methodology for AM (DFAM) principles applied to FFF for high mechanical performance applications. A DFAM database is constructed to identify common requirements and establish design rules, validated through a case study.

Existing DFAM approaches often lack failure theory integration, especially in FFF, emphasizing mechanical characterizations over predictive failure analysis in functional parts. This methodology addresses this gap by enhancing product reliability through failure prediction in high-performance FFF applications.

While some DFAM methods exist for high-performance FFF, they are often specific cases. Existing DFAM methodologies typically apply broadly across AM processes without a specific focus on failure theories in functional parts. This methodology integrates FFF with a failure theory approach to strengthen product reliability in high-performance applications.

The hospitality and tourism industry is strongly influenced by new and immersive technologies, such as augmented reality (AR), to enhance customer experiences across a diverse set of touchpoints throughout the visitor journey. This paper aims to provide a holistic understanding of AR marketing for this industry context, present a number of fundamental premises of AR marketing within it and establish an agenda for future AR research.

This study reviews current literature on AR marketing, hospitality and tourism and industry use cases for the creation of a proposed conceptual framework to guide scholars and managers. Based on that, the authors propose fundamental premises.

The three fundamental premises of AR marketing presented are the need to clearly differentiate between AR and virtual reality within hospitality and tourism; the use of AR for the on-trip experience; and the combined focus on content, context, customer and computing devices for a successful strategic implementation of AR.

This study serves as a first point of reference for the strategic integration of AR into hospitality and tourism marketing, both from an industry and academic point of view.

The authors provide a number of managerial recommendations based on our three fundamental premises.

To the best of the authors’ knowledge, this study is one of the first to holistically characterize AR marketing in the hospitality and tourism context. It also highlights the fundamental premises of successful AR marketing and future directions of AR research today and in a spatial computing future.

This study aims to evaluate the effectiveness of tax incentives provided by the Ethiopian government in spurring private investment and job creation, using unique administrative and survey data.

The study employs a dataset covering large- and medium-scale manufacturing in Ethiopia from 2012 to 2018, combined with administrative data on actual tax payments and statutory obligations to gauge the impact of tax incentives. Regression analysis using the generalized method of moments (GMM) is used to examine the relationship between tax incentives and employment, taking into account variations in production, distribution and financial costs.

The study finds that tax incentives do not significantly affect employment at conventional significance levels. The incentive elasticity of employment appears to diminish as production, distribution and financial costs increase. Consequently, the incentives provided by the government have not had a substantial impact on employment generation within the manufacturing sector.

This study is unique for its comprehensive analysis of tax incentives in the Ethiopian manufacturing sector using both administrative and survey data. It highlights that increasing production and financial costs can offset the employment benefits of these incentives, emphasizing the need for a more favorable business environment for private investors.

The aim of this study was to examine the moderating roles of the legitimate power and distributive justice of the tax authority on the effect of procedural justice on the voluntary tax compliance of taxpayers in Addis Ababa, the capital of Ethiopia, by using survey data collected from taxpayers in the city.

Data for the study were collected from 800 sample taxpayers who were drawn by using a systematic sampling technique. The variables of the study were constructed as indices from composing the scale items developed and tested for their validity by prior researchers. Having collected the data by using a 7-point Likert scale questionnaire and forming the latent variables, hierarchical multiple regressions were applied to determine the moderating effects of the two variables (i.e. legitimate power and distributive justice) on the effect of procedural justice on voluntary tax compliance.

The author found that both the legitimate power of the tax authority and distributive justice of the authority moderate the effect of procedural justice on voluntary tax compliance. The moderating roles of the two variables appear to be opposite in that low (but not high) distributive justice and high (but not low) legitimate power of the tax authority stimulate the effect of procedural justice on voluntary tax compliance.

The first limitation is that the data used in this study are self-reported data while the subject of the study is sensitive subject about which respondents are not believed to provide genuine responses. This is presumably because taxpayers are less likely to confess their tax evasion as they fear legal actions following their self-report. Hence, other controlled methods such as the experimental design are recommended to replicate the results of this study. The second limitation is that data for the study were gathered through a one-time cross-sectional survey and hence it would not warrant a causal claim between the study variables. Consequently, other research with a longitudinal or experimental design might warrant a causal relationship between the variables.

Therefore, the tax authorities must endeavor to attain high legitimacy by doing “the right things” as perceived by the taxpayers so that their tax-related decisions gain acceptance from the decision recipients. Tax policy makers as well ought to consider the importance of and the relationship between procedural justice, distributive justice and legitimate power of the tax authority in order to attain the maximum possible voluntary compliance of taxpayers that significantly reduces the administrative cost of taxes.

The study benefits society by enhancing tax compliance and hence helping the government secure a better amount of tax revenue and provide better public goods and services.

The findings of this study are of high theoretical and policy significance. Theoretically, the findings contribute to the integrative literature on economic deterrence and social-psychological factors that are responsible for voluntary tax compliance decisions. The parallel moderating roles of the two variables on the relationship between procedural justice and voluntary cooperation in a single model and in the tax compliance context are novel. In terms of applicability to policy formulations, they shed light on the need for a shift from a pure focus on aggressive tax audits and penalties, especially in emerging economies to a combination of the tax audits and the nurturing of the voluntary deference of taxpayers to the tax authority's decisions. Caution must, however, be taken that the results of this study may not be applicable to tax environments in other countries.

In online purchase for dietary supplements, due to the lack of professional advice from pharmacists, electronic word-of-mouth (eWOM) has become an important source of information for consumers to make purchase decisions. How can firms use eWOM resources to increase sales? The purpose of this paper is to provide practical methods for firms by exploring the effects of eWOM on sales and developing a sales prediction model based on eWOM.

The data came from 120 dietary supplements on Tmall.com. The authors extracted the product sales as dependent variable and 11 eWOM factors as independent variables. The multicollinearity was tested by using variance inflation factor and least absolute shrinkage and selection operator. The multiple linear regression was used to investigate the effects of eWOM on sales. Drawing on white- and black-box approaches, six models were developed. Comparing the root mean square error, the authors selected the optimal one as their target sales prediction model.

Product ratings, total reviews and favorites are positively and strongly associated with sales. Questions and additional reviews have negative effects on sales. The random forest model has the best prediction performance.

The research focuses on eWOM of dietary supplement. First, the authors show that easily accessible eWOM from online platforms can be used to evaluate effects and predict sales. Second, the authors introduce white- and black-box models through machine learning to assess eWOM. Firms could use the described models to foster their marketing initiatives.