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This study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to provide manufacturers and other practitioners with a more accurate and objective way to assess potential channel members and mitigate information asymmetry in channel selection and negotiation.

The authors developed an open-source approach using publicly available Google satellite imagery and ML algorithms. A computer vision algorithm was used to count cars in store parking lots, and the data were processed with a CNN. Linear regression and various ML algorithms were used to estimate the relationship between parked cars and sales.

The relationship between parked cars and sales was nonlinear and dependent on the type of channel member. The best model, a Stacked Ensemble, showed that parking lot occupancy could accurately predict channel member performance.

The proposed approach offers manufacturers a low-cost and scalable solution to improve their channel member selection and performance assessment process. Using satellite imagery data can help balance the marketing channel planning process by reducing information asymmetry and providing a more objective way to assess potential partners.

This research is unique in proposing a method based on publicly available satellite imagery data to assess and predict channel member performance instead of forward-looking sales at the firm and industry levels like previous studies.

The purpose of this research is to examine how a change in team dynamics impacts an individual's motivation to engage in helping behavior and operational performance.

An online vignette experiment and a hybrid discrete event and agent-based simulation model are used.

Study findings demonstrate how a non-core worker's perception of team dynamics influence engagement in helping behavior and system performance.

This study provides a further understanding on how team members react to changes in team processes. This study theorizes on how an individual team member responds to fairness concerns. This study also advances our understanding of the critical importance of helping behavior in a retail logistics setting. This research illustrates how the theory of strategic core and procedural justice literature can be adopted to explain team dynamics in supply chain management.

The purpose of studying the impact of artificial intelligence text generators (AITGs) on libraries is to examine the effect of AITGs on the library landscape, including the services offered, the resources provided and the roles of library staff.

The current study examined how AITGs impact libraries. The researcher was able to comprehend the problem by critically analyzing and reviewing the pertinent published works, such as books, journals and articles.

This study concludes AITGs can assist libraries in streamlining operations, enhancing services and making collections more accessible. It is vital to highlight that AITGs are not intended to dissuade its users from visiting physical libraries or to replace them with virtual ones. Instead, they are a tool that can improve and supplement the services and resources provided by virtual libraries.

The study’s observations add to the corpus of information on AITGs in libraries and help users comprehend their technological foundations. Further empirical research is recommended on the effects of AITGs and their impact on libraries.

The purpose of this study was to prioritize the appropriate generic contractor selection criteria for Engineering–Procurement–Construction (EPC) projects in the construction industry.

Proceeding from a review of previous studies and validation by a small group of experts, a preliminary set of 16 criteria was first identified. This was followed by three rounds of Delphi surveys: firstly, with 64 experienced participants confirming the relevance of the 16 criteria; secondly, with a reduced subgroup of 47 more experienced participants scoring the importance of each; and finally, providing the opportunity for these 47 to revise their scores in the light of knowing the aggregated results of the previous round.

The results show the consensus view, of which the most important criteria are ranked as past performance, project understanding, technical attributes, key personnel, health and safety, past experience, time, management, financial, contractual and legal, quality, cost, relationships, environmental and sustainability, organizational and industrial relations, and geographic location.

The findings are useful for both practitioners and academics in making a significant contribution to the body of knowledge of the EPC process. This will assist in providing a better understanding of criteria importance and pave the way to developing an EPC contractor selection model involving the criteria most needed to objectively identify potential contractors and evaluate tenders.