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The purpose of this study was to explore whether a “value-action gap” exists between what members of the adolescent Generation Z (Gen Z) cohort value and how they act by investigating their actions related to sustainable and responsible fashion consumption (SRFC). Specific focus was placed on understanding these actions across the apparel consumption cycle, ranging from the acquisition, to use and disposal stages.

Forty-one members of Gen Z (20 males and 21 females) ranging in age from 15 to 18 participated in a total of seven focus groups.

Three emergent themes were identified and used to structure the interpretation: unintentionally sustainable, a knowledge conundrum and perceived barriers.

The majority of focus group participants were Caucasian, and all were teenagers from a single geographical area in the Southeastern USA. Findings provided by this study offer insight regarding the SRFC habits of Gen Z relative to their concerns regarding sustainability and social and environmental responsibility.

Findings offer practitioners an opportunity to better understand how to address the needs of this generational cohort as they progress through adulthood.

Findings of this study investigate the value-action gap to offer insight into how adolescent members of Gen Z make consumption decisions, and specifically within a framework of the apparel consumption cycle as a whole, including acquisition, use and disposal. Findings also reveal some of their more general views on SRFC.

The purpose of this paper is to investigate and report students’ perception of service quality in a university by examining the perceptual context of service quality with respect to students’ loyalty behavior, image of the university and culture/values.

A research framework is developed for quality assessment with three hypotheses. A questionnaire with 65 instruments was used for gathering the required data for the analysis. The questionnaire was sent through email to all engineering students. The analysis included descriptive statistics, reliability analysis, gap analysis and hypotheses tests. Seven dimensions of service quality were identified: the original dimensions of the SERVQUAL, namely, reliability, responsiveness, assurance, empathy and tangibles. Two additional dimensions image and culture/value were added for the research to understand perceived service quality and loyalty.

The results provide a significant positive correlation between service quality and student's loyalty. It also shows that there is statistically significant relation between the image of the institution and the perceived service quality, and culture/values of the students in the institution and perceived service quality.

This study used data collected from a survey in the university in a given period.

The findings indicate that to provide quality education, meeting students’ needs, wants and expectations of services quality should be carefully understood and addressed. Management also needs to consider factors such as corporate image and culture/value, as they have the ability to heavily impact the type of services provided by the institution.

The findings presented in this paper fill the gap in the current literature by providing empirical knowledge on the quality of service assessment and customer satisfaction in the higher education context. The study is the first of its kind in Qatar’s context and provides opportunities for higher institutions to focus more on current students’ services. This can lead to an increased brand value representing one of the premier institutes of higher education in the Middle East Gulf Region.

The paper offers a comprehensive understanding of how digital transformation affects business models and how firms operate and compete effectively and successfully in a digital economy.

The research adopted an abductive approach (Dubois and Gadde, 2002) through constant movement between theory and empirical evidence. A systematic literature review led the first conceptual development and examples of practices from cultural heritage sectors were used in the theorizing process.

This paper depicts a digital model framework through a set of assumptions about how an organization creates and delivers value in an interconnected way by orchestrating new interactive processes, and providing experience propositions to customers, and about how value is framed in terms of economic, social and cultural outcomes.

The study contributes to the scientific debate by discussing the role of digital business models as enhancements more rather than replacements of traditional business models; it frames a digital business model as consisting of three main pillars: value orchestration, experience propositions and value sharing.

This paper aims to address the longitudinal control problem for person-following robots (PFRs) for the implementation of this technology.

Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control.

A lab PFR with the bar-laser-perception device is developed and tested in the field, and the results indicate that the proposed models perform well in normal person-following scenarios.

This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.

With the help of “self-learning” algorithms and high computing power, companies are transforming Big Data into artificial intelligence (AI)-powered information and gaining economic benefits. AI-powered information and Big Data (simply data henceforth) have quickly become some of the most important strategic resources in the global economy. However, their value is not (yet) formally recognized in financial statements, which leads to a growing gap between book and market values and thus limited decision usefulness of the underlying financial statements. The objective of this paper is to identify ways in which the value of data can be reported to improve decision usefulness.

Based on the authors' experience as both long-term practitioners and theoretical accounting scholars, the authors conceptualize and draw up a potential data value chain and show the transformation from raw Big Data to business-relevant AI-powered information during its process.

Analyzing current International Financial Reporting Standards (IFRS) regulations and their applicability, the authors show that current regulations are insufficient to provide useful information on the value of data. Following this, the authors propose a Framework for AI-powered Information and Big Data (FAIIBD) Reporting. This framework also provides insights on the (good) governance of data with the purpose of increasing decision usefulness and connecting to existing frameworks even further. In the conclusion, the authors raise questions concerning this framework that may be worthy of discussion in the scholarly community.

Scholars and practitioners alike are invited to follow up on the conceptual framework from many perspectives.

The framework can serve as a guide towards a better understanding of how to recognize and report AI-powered information and by that (a) limit the valuation gap between book and market value and (b) enhance decision usefulness of financial reporting.

This article proposes a conceptual framework in IFRS to regulators to better deal with the value of AI-powered information and improve the good governance of (Big)data.

Although several theoretical concepts imply different determinants of female entrepreneurship, the literature lacks a consensus on their significance. The aim of this paper is to verify how industry specificity influences the gender pay gap and its relation to female entrepreneurship.

The authors distinguish industries based on the gender equality level, measured jointly by two factors: pay gap level and female participation rate. The study has been conducted among 22 European countries with relatively similar institutional backgrounds. The authors carry out the analysis based on the panel regression models, which enable the authors to verify two predefined research questions.

The results of panel regression models indicate that industry specificity plays a significant role in the relation between the pay gap and female entrepreneurship. Generally, it can be concluded that gender pay gap as a measure of gender inequality is dependent on the industry specificity. The dependence is especially visible in the breakdown of male- and female-dominated industries.

The findings are consistent with the assumption that the gender pay gap is a discriminatory factor for women willing to become entrepreneurs in certain industries. The findings of the study may constitute a vital tool in planning to overcome it.

This paper appraises the effects of monetary policy on trade in value-added (TiVA) using a panel of 38 developing countries spanning the period 1990 to 2019. Specifically, the authors subsequently summon the theory of trade in intermediate products within the New Keynesian framework for open economies that comprises price rigidity to verify this relationship and thereon control for robustness by correcting for endogeneity and unbalanced panel effect.

The authors mobilize the within estimator corrected for cross sectional dependence as well as the two-stage-least squares fixed effect estimator which corrects for endogeneity. For robustness, the authors also use the Hausman–Taylor estimator to control for endogeneity and random effects in annualized data and the least squares dummy variable corrected estimator.

Results suggest that the monetary policy instruments such as inflationary gaps and anticipatory inflationary outcomes significantly affect TiVA in developing countries only in the short term with no long-term effect. In addition to contributing to the scanty empirical literature, the authors provide relevant insights on monetary policy tools that can be mobilized in fashioning a global value chain penetration and upgrading strategies.

The authors convoke the theory of trade in intermediate products casted into the New Keynesian framework comprising price rigidity to verify the relationship between TiVA and monetary policy (b) verify for robustness by correcting for endogeneity and unbalanced panel effect.

Going beyond the usual approach of measuring port performance – focusing on the efficiency of port operations – this paper aims to look into shipping lines and other port users’ perceptions on port performance.

The paper develops a framework measuring the perceptions of port users (i.e. shipping companies, shippers, etc.) on port performance. A typology of elements that shape port users’ perceptions has been developed with an eye on capturing the peculiarities of different port markets. Based on this typology, a tool to assess users’ perspectives, and subsequently evaluate, any given port has been developed. The tool provides port authorities the flexibility they need for customized approaches. The developed evaluation mechanism has been tested on a group of European seaports, and the results are presented by this study.

The framework and its pilot application unveil the key parameters that port users take into consideration when evaluating the effectiveness component of port performance. Moreover, the importance and evaluation ratings of specific performance parameters allow for a GAP analysis of the collected data.

The paper advances scholarly and practical discussion on how of port users’ perceptions can be a valuable tool for port performance measurement.

The proposed tool can be a valuable add-on for port authorities to evaluate their performance from the port users’ point of view and take the necessary actions to improve it. Also, the tool can be used for the evaluation of a new process, infrastructure. The evaluation of port users’ perception of port performance can and must be part of a European ports observatory, as it is a set of indicators that clearly reflects the satisfaction of port users by engaging their view on port performance issues, instead of relying almost exclusively on port-generated data.

The paper develops a framework for measuring port user’s perception on port performance, which is flexible and can be applied in any port.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.