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This study aims to guide firms in emerging markets on whether corporate social responsibility (CSR) engagement facilitates their access to debt with the moderation of asset structure and firm performance. Considering the moderating effect analysis, this study explores the substitutive or complementary effect of these two contingencies on CSR-oriented firms in accessing debt financing.

Drawing on data collected for 16 emerging markets between 2008 and 2019, this study runs country–industry–year fixed-effects regression.

This study finds that CSR performance and reporting facilitate access to debt in emerging markets. However, CSR performance does not have an inverted U-shaped influence on firms’ access to debt financing. The moderation analysis of this study shows that asset tangibility has a negative moderating effect on the link between CSR engagements (i.e. both CSR performance and reporting) and access to debt, confirming a substitutive relationship between asset tangibility and CSR engagements in accessing debt. In contrast, firm performance is positively moderating the nexus between CSR engagement proxies and access to debt, which confirms a complementary type of relationship between firm performance and CSR engagements in accessing debt.

The empirical evidence of this study implies that creditors critically consider CSR engagements of firms in the loan-granting decision process. Similarly, the inverted U-shaped relationship between CSR and access to debt implies that there is an optimal level of CSR engagement creditors might consider in their decision. Likewise, the moderating effects analysis highlights that asset tangibility and firm performance are two conditions under which CSR performance and reporting are linked to access to debt.

Emerging countries are a different set of countries than developed ones; they have high growth rates and hence need financing, have a weaker institutional environment and have weaker stakeholder power. These particularities motivated the authors to conduct a separate study focusing on CSR and debt financing links drawing on a wide range of emerging countries. Thus, this study adds to the ongoing debate by examining the conditions under which CSR-oriented firms can access debt financing in emerging economies.

This research sought to examine the differential effect of chatbot banking artificial intelligence (AI) on consumer experience in the banking industry. A positivist paradigm was adopted to sample 389 consumers who were previously exposed to chatbot banking in Zimbabwe. A causal research design was employed whilst a quantitative approach was followed. In analysing data, the research study applied the structural equation modelling (SEM) technique. The authors found that chatbot banking significantly improves customer experience (CX) in the banking industry. Reliability and responsiveness of the chatbot need to be enhanced for effective improvements in CX. A need was also identified to enhance CX through the development of an ease-to-use chatbot which is embedded in everyday messaging applications of consumers. A significant association was also found between perceived benefits of chatbot banking and CX. This study informs the development of competitive advantage by banks and other related companies through AI-based CX management strategies. In times of pandemics and beyond, chatbot banking can be very instrumental in improving CX.

The purpose of this study is to identify the impact of online learning on university librarians’ professional development and library services.

A mixed-methods study through an explanatory research design was applied to address the study’s objectives. Quantitative data were gathered from 341 librarians working in 221 universities, while qualitative data were gathered from 27 experts working in 21 different universities of Pakistan.

The findings of the study revealed that online learning has a significant positive impact on the professional development of university librarians. Results revealed that online learning assists in the provision of sustainable, innovative library services in university libraries.

The study has offered a model in light of the study's quantitative and qualitative findings. It contributes to theoretical understanding by expanding the existing knowledge base. It offers managerial insights, enabling the development of policies that foster the professional development of library personnel and the implementation of smart library services.

The general data protection regulation (GDPR) was designed to address privacy challenges posed by globalisation and rapid technological advancements; however, its implementation has also introduced new hurdles for companies. This study aims to analyse and synthesise the existing literature that focuses on challenges of GDPR implementation in business enterprises, while also outlining the directions for future research.

The methodology of this review follows the preferred reporting items for systematic reviews and meta-analysis guidelines. It uses an extensive search strategy across Scopus and Web of Science databases, rigorously applying inclusion and exclusion criteria, yielding a detailed analysis of 16 selected studies that concentrate on GDPR implementation challenges in business organisations.

The findings indicate a predominant use of conceptual study methodologies in prior research, often limited to specific countries and technology-driven sectors. There is also an inclination towards exploring GDPR challenges within small and medium enterprises, while larger enterprises remain comparatively unexplored. Additionally, further investigation is needed to understand the implications of emerging technologies on GDPR compliance.

This study’s limitations include reliance of the search strategy on two databases, potential exclusion of relevant research, limited existing literature on GDPR implementation challenges in business context and possible influence of diverse methodologies and contexts of previous studies on generalisability of the findings.

The originality of this review lies in its exclusive focus on analysing GDPR implementation challenges within the business context, coupled with a fresh categorisation of these challenges into technical, legal, organisational, and regulatory dimensions.

Research on online user privacy shows that empirical evidence on how privacy literacy relates to users' information privacy empowerment is missing. To fill this gap, this paper investigated the respective influence of two primary dimensions of online privacy literacy – namely declarative and procedural knowledge – on online users' information privacy empowerment.

An empirical analysis is conducted using a dataset collected in Europe. This survey was conducted in 2019 among 27,524 representative respondents of the European population.

The main results show that users' procedural knowledge is positively linked to users' privacy empowerment. The relationship between users' declarative knowledge and users' privacy empowerment is partially supported. While greater awareness about firms and organizations practices in terms of data collections and further uses conditions was found to be significantly associated with increased users' privacy empowerment, unpredictably, results revealed that the awareness about the GDPR and user’s privacy empowerment are negatively associated. The empirical findings reveal also that greater online privacy literacy is associated with heightened users' information privacy empowerment.

While few advanced studies made systematic efforts to measure changes occurred on websites since the GDPR enforcement, it remains unclear, however, how individuals perceive, understand and apply the GDPR rights/guarantees and their likelihood to strengthen users' information privacy control. Therefore, this paper contributes empirically to understanding how online users' privacy literacy shaped by both users' declarative and procedural knowledge is likely to affect users' information privacy empowerment. The study empirically investigates the effectiveness of the GDPR in raising users' information privacy empowerment from user-based perspective. Results stress the importance of greater transparency of data tracking and processing decisions made by online businesses and services to strengthen users' control over information privacy. Study findings also put emphasis on the crucial need for more educational efforts to raise users' awareness about the GDPR rights/guarantees related to data protection. Empirical findings also show that users who are more likely to adopt self-protective approaches to reinforce personal data privacy are more likely to perceive greater control over personal data. A broad implication of this finding for practitioners and E-businesses stresses the need for empowering users with adequate privacy protection tools to ensure more confidential transactions.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

This paper aims to critically examine the accounting and information systems literature to understand the changes that are occurring in the management accounting profession. The changes the authors are interested in are linked to technology-driven innovations in managerial decision-making and in organizational structures. In addition, the paper highlights research gaps and opportunities for future research.

The authors adopted a grounded theory literature review method (Wolfswinkel et al., 2013) to achieve the study’s aims.

The authors identified four research themes that describe the changes in the management accounting profession due to technology-driven innovations: structured vs unstructured data, human vs algorithm-driven decision-making, delineated vs blurred functional boundaries and hierarchical vs platform-based organizations. The authors also identified tensions mentioned in the literature for each research theme.

Previous studies display a rather narrow focus on the role of digital technologies in accounting work and new competences that management accountants require in the digital era. By contrast, the authors focus on the broader technology-driven shifts in organizational processes and structures, which vastly change how accounting information is collected, processed and analyzed internally to support managerial decision-making. Hence, the paper focuses on how management accountants can adapt and evolve as their organizations transition toward a digital environment.

This study aims to determine whether socially friendly business strategy impacts social sustainability performance and, if so, whether social management process and spiritual capital act as mediators and moderators of the relationship.

This study uses a comprehensive research framework consisting of the mediation and moderation relationship among four constructs, namely, socially friendly business strategy, social management process, spiritual capital and social sustainability performance. A total of 433 owners/managers of micro, small and medium-sized firms (MSMEs) in the Indonesian province of East Java took part in this study, and the data were gathered using a survey method. The resource-based view, stakeholder theory and partial least squares structural equation modelling are all used in this study to evaluate and explain the hypotheses.

The results show that both socially friendly business strategy and social management process positively affect social sustainability performance. Further analysis reveals that spiritual capital moderates the effect of socially friendly business strategy on social sustainability performance. Second, social management process mediates the influence of socially friendly business strategy on social sustainability performance in part.

The current study has limitations. First, it restricts the scope of its sample to MSMEs in Indonesia’s East Java Province. As a result, it also restricts its generalizability, and care must be used if the findings are applied to other types of organizations and geographic areas. Second, some survey participants needed help to complete the online questionnaire. As a result, collecting the data were less successful than anticipated. This study has significant implications for the development of the stakeholder theory, particularly in elucidating the mechanisms by which socially responsible corporate strategies, social management practices and performance in terms of social sustainability are affected.

The findings provide a comprehensive guidance for owners/managers in reorienting their business strategy, managing the social management process and building their spiritual capital to achieve social sustainability performance. It provides materials for researchers and students who are interested in studying the subject matter.

MSMEs have a significant role in society. The welfare of society will therefore increase if social sustainability performance is successful. The overall model of social sustainability performance improvements and its antecedents are presented in this study.

To the best of the authors’ knowledge, this study is among the first attempts to explore the general model of improving social sustainability performance using four constructs that are rarely used in previous studies. It also uses a new data set and research setting in Indonesia as one of the emerging countries.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.