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This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

Facilities management (FM) as a strategic management tool has been an attractive research topic among scholars and practitioners alike for decades. The primary purpose of this paper is threefold: to assess the extent of use of FM roles (strategic, tactical and operational); to examine user satisfaction of service quality performance; and to analyse the influence of FM roles on service quality performance using data from Nigeria’s banking sector.

Relying on exploratory cross-sectional survey, 350 copies of a structured questionnaire were purposively distributed to senior management staff, bank staff, FM supervisors and bank customers in Lagos, Nigeria. One hundred and forty valid responses were returned to give a response rate of 40%. Data collected were analysed using descriptive, Spearman rank correlation and Kruskal–Wallis tests.

It was discovered that strategic facilities planning, IT planning strategy and real estate decisions are the most important FM roles at the strategic level; resource management, data control and planning change at the tactical level; and implementations, building operations and emergencies at the operation level. Findings equally revealed that visual appealing of materials associated with services (tangibles), insisting on error-free records (reliability), willing to help (responsiveness), having the knowledge to answer questions (assurance) and giving individualised attention (empathy) were the most important service quality performance indicators. Furthermore, the study revealed that strategic FM roles significantly influenced tangibles, reliability and responsiveness of staff and the services. Besides, tactical FM roles significantly influenced all service quality indicators except assurance, while operational FM roles had significant influence on tangibles, responsiveness and empathy.

To the best of the author’s knowledge, this study has provided first ever insight into the extent of FM strategic roles in the banking sector and influence of FM roles on service quality performance.

This study investigates the service quality dimensions of hotel resorts in renowned beach destinations of Thailand. It also explores the relationship between review text sentiment expressed in online platforms and the satisfaction ratings provided for those reviews.

The study employs a two-step analysis approach: first, supervised and unsupervised machine learning via support vector machine (SVM) and latent Dirichlet allocation (LDA) are used to identify service quality dimensions, and second, SmartPLS with PROCESS macro is applied to analyze the moderating roles of quality signals and reviewer’s experience on the relationship between sentiment and satisfaction rating. The dataset comprises 102,179 online reviews from TripAdvisor, focusing on 187 selected hotels rated from 3 to 5 stars.

Eight service quality dimensions were identified, including leisure activities, tangibles and surroundings, reliability, responsiveness, service process, food, empathy and ambience. The study underscores that the service process stands as the sole dimension exhibiting negative sentiment. Furthermore, the analysis revealed a robust positive association between sentiment of review texts and satisfaction, and reviewers’ experience and brand affiliation influenced the relationship between customer sentiment and satisfaction.

Hotel managers should focus efforts on maintaining tangible aspects while enhancing existing service quality level of other dimensions, particularly those related to intangible elements. Independent hotels might implement quality audit to ensure that service quality gaps are monitored.

This study contributes an examination of the moderating roles of quality signals and reviewer’s experience on the relationship between review sentiment and satisfaction rating in online reviews.

This study aims to develop and apply a process model for prioritizing and selecting basic research projects in developing countries.

Basic research is mainly funded by governments and since, unlike technological research, it does not have clear business goals, its prioritization is one of the complicated issues in formulating science and technology policy. Adopting a design science research methodology, the authors chose a general framework for project portfolio selection as an appropriate artifact for solving this problem. By customizing it for two specific features of this study, i.e. national scale of the problem and the basic nature of research proposals, the authors developed the proposed framework for solving the problem of priority setting.

The process for selecting basic research proposals consists of several steps, which can be categorized into eight steps including strategic decisions, preparation, pre-screening, evaluating individual proposals, screening, portfolio selection and monitoring. This study emphasizes the necessity of defining goals that can be evaluated for the national basic research portfolio, as a key strategic decision. Evaluating individual proposals is a peer-review-based process. In contrast, portfolio selection is done through a zero-one linear programming model. The validity of the proposed framework has been confirmed based on the data obtained from the Iran National Science Foundation.

To the best of the authors’ knowledge, in this research, for the first time, a mathematical model for prioritizing basic research at the national level has been presented, which effectively contributes to policymaking regarding the development of an optimum national research portfolio.

The aim of this study is to investigate the contribution of tangible and intangible investments in driving labour productivity growth in the European Union over the period 2000–2017 and their role in the short and medium run. Additionally, heterogeneity across countries is accounted for by performing estimates separately for Eastern and Western European countries.

The methodology used to conduct the analysis of the determinants of productivity is the two-way fixed-effect and the system generalised method of moments. We also include country-specific dummies in place of our variable on national innovative capacity as a means to further reduce the number of instruments.

The results reveal a long-term relationship of investment in intangible assets with labour productivity growth, more specifically of investment in R&D. This relationship holds both when considering the whole set of European countries and for Western European countries, demonstrating that R&D is key to enhancing labour productivity growth. On the contrary, the effect for Eastern countries is negative, probably due to the lack of capacity to turn this investment into an efficient and effective way to foster productivity.

Besides confirming the well-known role of tangible and intangible assets in productivity, the heterogeneity shown in our analysis highlights the need for improving capabilities in Eastern countries. Diversifying the decisions on the investments in European countries, depending on the specific needs and their heterogeneity, could help bridge the productivity gap and enhance specific capabilities of the country systems.

The purpose of the study was to assess how the well-being and loneliness of public relations and communication professionals are impacted by the post-pandemic characteristics of the work environment: flexible work schemes, non-territorial office arrangements and video communication technologies. It was hypothesised that the post-pandemic workplace landscape poses several new challenges to the practice of PR – an industry which invariably relies on working with other people and demands a good level of social resilience. Loneliness and well-being both depend on the experience of having good and efficient social relationships, but the pandemic has directly and indirectly led to their deterioration.

The project employed a correlational design and used an online survey system to collect responses from Gen Z professionals employed in the public relations and communications industry in the UK and the US via the Prolific platform. Demographical and workplace-related characteristics were assessed to investigate links with loneliness (measured using a three-item scale adopted from Russell et al., 1980 in Hughes, 2004) and well-being (using a short Warwick-Edinburgh Mental Well-being Scale scale). Causal relationships between data were tested using regression analysis for continuous variables and analysis of covariance for categorical factors. Bootstrapping was used to test mediated relationships that explain loneliness, job satisfaction and the well-being of Gen Z PR professionals.

Several types of flexible working schemes, defined as the ability to work from home on any number of weeks, showed an impact on loneliness and job satisfaction but not on well-being. However, all remaining aspects of the post-pandemic office did manifest as important predictors. In the sample, 30% of Gen Z PR professionals showed signs of mild to clinical levels of depression, and the best protection from this state was the presence of a significant other. Lower levels of loneliness were related to non-territorial office arrangements and job satisfaction. The use of hot desks and open-plan arrangements led to a significantly lower level of job satisfaction than a traditional, cellular office. Both excessive online meetings and face-to-face only interactions led to marginally lower levels of loneliness and job satisfaction.

The present research is limited in several aspects. Firstly, while the project evaluated loneliness, job satisfaction and mental well-being (with each of these elements including a component of the requirement for building effective relationships), the quality of relationships built by PR professionals was not measured. Secondly, the project focused only on post-pandemic aspects of the workplace and did not cover other important components of job satisfaction. Lastly, the measure of online meetings was declarative rather than behavioural, and greater control of the number of online meetings held would be required to show more reliable links between variables.

This study calls for proposing recommendations for employers to develop organisational-level measures and programmes to counteract loneliness. While traditionally intimate relationships of employees were not a direct focus of HR programmes, employers should develop elements of organisational culture that would support employees in building effective intimate relationships. Separately from this, despite immediate financial benefits, employers should avoid using open-space and hot desk policies, as they contribute negatively to job satisfaction (and indirectly to well-being). The sample of UK and US professionals was chosen for analysis because in these countries employers have more capacity to introduce changes to tangible characteristics of the workplace and work culture, which may positively impact the well-being of their employees.

It is expected that both employers and employees will revisit their approach to post-pandemic financial and logistic challenges related to the workplace. A lower level of job satisfaction and well-being is linked to the lack of assigned office space, but the ability to work exclusively from home leads to loneliness. Employees – when offered this possibility – should work in offices they are provided. Employers must appreciate the negative link between open and hot-desking policies and job satisfaction and well-being of their employees.

This study is the first to examine the post-pandemic workplace and personal characteristics of public relations and communications professionals in the UK and US and show how they impact job satisfaction and well-being. The study shows that 30% of employed in the PR industry are at risk of depression or anxiety. The connecting factor between personal and work-related characteristics that explains this problem is loneliness.

Cryptoassets are a diverse category of digital assets that rely on blockchain technology. They encompass various categories, such as cryptocurrencies, utility tokens, security tokens, tokenized assets and securities, and stablecoins. Cryptocurrencies are decentralized digital units of value that enable secure and transparent transactions. Utility tokens provide access to specific services or products within a blockchain network. Security tokens offer rights and entitlements similar to traditional securities, representing ownership in real-world assets or participation in investment opportunities. Tokenized assets and securities are digital representations of tangible or intangible assets, allowing for fractional ownership and enhanced liquidity. Stablecoins are blockchain-based digital assets designed to maintain a stable value, often pegged to fiat currencies or physical assets. This chapter examines each category's characteristics, benefits, and risks; explores their implementations and current applications in the fintech ecosystem; and discusses relevant regulations and future development opportunities.

This study aims to explore a potential conference experience design strategy, namely, festivalization. It investigates the potential festivalization effects on conference attendees in two formats of business conferences: virtual and in-person.

A series of two scenario-based experimental studies were conducted. A series of one-way analysis of covariance and PROCESS procedures (Model 6) were performed for data analysis.

The inclusion of festivalization elements significantly enhances positive responses of attendees, especially for in-person conferences. This effect is further explained by a serial mediation effect, where enhanced perceived values and conference engagement play key roles in improving attendees’ conference experience.

By incorporating festivalization elements, conference organizers can create a more engaging and satisfying event experience for attendees. This can lead to greater satisfaction, positive word-of-mouth and increased registrations.

This study represents a pioneering effort in revealing the underlining mechanisms that explain how festivalization affects attendee engagement and subsequent behaviors in business event management in both face-to-face and virtual settings.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

The purpose of this study is to assess a novel method for creating tangible three-dimensional (3D) morphologies (scaled models) of neuronal reconstructions and to evaluate its cost-effectiveness, accessibility and applicability through a classroom survey. The study addresses the challenge of accurately representing intricate and diverse dendritic structures of neurons in scaled models for educational purposes.

The method involves converting neuronal reconstructions from the NeuromorphoVis repository into 3D-printable mold files. An operator prints these molds using a consumer-grade desktop 3D printer with water-soluble polyvinyl alcohol filament. The molds are then filled with casting materials like polyurethane or silicone rubber, before the mold is dissolved. We tested our method on various neuron morphologies, assessing the method’s effectiveness, labor, processing times and costs. Additionally, university biology students compared our 3D-printed neuron models with commercially produced counterparts through a survey, evaluating them based on their direct experience with both models.

An operator can produce a neuron morphology’s initial 3D replica in about an hour of labor, excluding a one- to three-day curing period, while subsequent copies require around 30 min each. Our method provides an affordable approach to crafting tangible 3D neuron representations, presenting a viable alternative to direct 3D printing with varied material options ensuring both flexibility and durability. The created models accurately replicate the fidelity and intricacy of original computer aided design (CAD) files, making them ideal for tactile use in neuroscience education.

The development of data processing and cost-effective casting method for this application is novel. Compared to a previous study, this method leverages lower-cost fused filament fabrication 3D printing to create accurate physical 3D representations of neurons. By using readily available materials and a consumer-grade 3D printer, the research addresses the high cost associated with alternative direct 3D printing techniques to produce such intricate and robust models. Furthermore, the paper demonstrates the practicality of these 3D neuron models for educational purposes, making a valuable contribution to the field of neuroscience education.