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This study presents the development of a supply chain (SC) observatory, which is a benchmarking solution to support companies within the same industry in understanding their positioning in terms of SC performance.

A case study is used to demonstrate the set-up of the observatory. Twelve experts on automatic equipment for the wrapping and packaging industry were asked to select a set of performance criteria taken from the literature and evaluate their importance for the chosen industry using multi-criteria decision-making (MCDM) techniques. To handle the high number of criteria without requiring a high amount of time-consuming effort from decision-makers (DMs), five subjective, parsimonious methods for criteria weighting are applied and compared.

A benchmarking methodology is presented and discussed, aimed at DMs in the considered industry. Ten companies were ranked with regard to SC performance. The ranking solution of the companies was on average robust since the general structure of the ranking was very similar for all five weighting methodologies, though simplified-analytic hierarchy process (AHP) was the method with the greatest ability to discriminate between the criteria of importance and was considered faster to carry out and more quickly understood by the decision-makers.

Developing an SC observatory usually requires managing a large number of alternatives and criteria. The developed methodology uses parsimonious weighting methods, providing DMs with an easy-to-use and time-saving tool. A future research step will be to complete the methodology by defining the minimum variation required for one or more criteria to reach a specific position in the ranking through the implementation of a post-fact analysis.

The purpose of this study was to comprehend the adoption of artificial intelligence (AI) technology-driven natural large language model (LLM)-based chatbots by customers.

A qualitative research study method was conducted. This was to explore managerial perspectives towards consumer centric technology adoption of AI plus LLM-based chatbots. This was specifically for AI-driven natural LLM-based chatbots services. The author conducted conducted in-depth personal interviews with 32 experts of digital content AI + LLM chatbot services. Thematic content analysis was undertaken to analyse the data.

The advent of natural language processing tools driven by AI technology chatbots has altered human-firm interaction. The research findings indicated that the push-pull-mooring (PPM) factors captured the phenomenon in the most comprehensive way. A total of 15 key factors influencing the adoption of AI technology-driven natural LLM-based chatbots by customers during firm customer interaction were identified in this study by the author. The thematic content analysis unraveled insights regarding transformed consumer adoptions towards AI-driven LLM-based chatbots by means of the PPM framework factors.

The empirical research investigation contributed to the literature on the PPM theoretical framework. This was specifically in the context of adoption of AI technology-driven natural LLM-based chatbots by customers during firm customer interaction.

The research study insights would help managers to restructure and reconfigure their organizational processes. This would neccessiated a shift in firm-customer interactions as demanded because of the availability of AI technology-driven natural LLM-based chatbots by customers.

This research study was based upon the PPM theoretical framework. This study provided a unique analysis of the altered firm customer interaction needs and requirements. This was one of the first studies that applied the framework of PPM theory regarding the adoption of AI technology-driven natural LLM-based chatbots by customers.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

Nowadays, a prominent research area is the development of competitive advantages in companies, due to their environmental commitment and orientation. Based on resource-based view (RBV) and institutional theory (InT), this paper aims to investigate the influence of internal and external orientation on businesses' sustainable performance while considering the effect of sustainable supply chain management (SSCM) practices.

Data from 351 manufacturing companies in the Kingdom of Saudi Arabia have been collected and analysed through structural equation modelling (SEM) using the partial least squares (PLS) method.

The results indicated that both internal and external environmental orientation have important effects on SSCM practices, which in turn have a considerable beneficial effect on environmental, social and economic performance.

Although SSCM is constantly gaining ground in the literature, most SSCM research and models examine its effects, antecedents or motivation, mainly adopting a qualitative approach. Research on the topic adopting a large-scale empirical approach is still limited. In this context, this study contributes to the SSCM management literature by exploring the role of environmental orientation in facilitating the adoption of SSCM practices and improving companies' performance.