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This paper aims to analyze the impact of tax digitalization, focusing on artificial intelligence (AI), machine learning and blockchain technologies, on enhancing tax compliance behavior in various contexts. It seeks to understand how these emerging digital tools influence taxpayer behaviors and compliance levels and to assess their effectiveness in reducing tax evasion and avoidance practices.

Using a systematic review technique with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method, this study evaluates 62 papers collected from the Scopus database. The papers were analyzed through textometry of titles, abstracts and keywords to identify prevailing trends and insights.

The review reveals that digitalization, particularly through AI and blockchain, significantly enhances tax compliance and operational efficiency. However, challenges persist, especially in emerging economies, regarding the adoption and integration of these technologies in tax systems. The findings indicate a global trend toward digital Tax Administration 3.0, emphasizing the importance of regulatory frameworks, capacity building and simplification for small and medium enterprises (SMEs).

The findings provide guidance for policymakers and tax administrations, underscoring the necessity of strategic planning, regulatory backing and global cooperation to effectively use digital technologies in tax compliance. Emphasizing the need for tailored support for SMEs, the study also calls for expanded research in less represented areas and specific sectors, such as SMEs and developing economies, to deepen global insights into digital tax compliance.

This study has attempted to fill the gap in the literature on the comprehensive impact of fiscal digitalization, particularly AI-based, on tax compliance across different global contexts, adding to the discourse on digital taxation.

This research presents a profile of the current skills and competencies that underpin construction management programmes' (CMP) curricula within United Kingdom (UK) higher education institutes (HEIs). In doing so, the work: synthesises disparate taught provisions across a range of HEIs; conducts a cross-comparative analysis between these provisions and engenders wider discourse and new insight into the consistency of current higher education practice.

Both interpretivism and pragmatism are adopted to analyse secondary data sourced from construction management undergraduate programmes in the UK inductive reasoning and inferential analysis (i.e. quantitative rank correlation, text/data mining and qualitative inquiry) are utilised to help underscore the current technical and interpersonal skills and competencies noted within the programmes and develop new theories on curriculum shortfalls and inadequacies.

Research findings demonstrate that the specific content of CMP are bespoke and tailored by the programme teaching team at each individual HEI; albeit, all programmes reviewed are in congruence regards the importance of broad technical and interpersonal themes. However, the degree to which these themes are publicly presented differ from the curricular and institutional documentation; specifically, a more “technical-based skill” image is being portrayed publicly whilst “interpersonal skills” are doing the heavy curriculum lifting. Hence, the foundational curriculum skills and competencies are firmly rooted in a sense of employability and career preparedness; a balance of technical and interpersonal skills. Identification of these skills and competencies provides a springboard for supplementary research to augment curriculum development.

This research constitutes the first attempt to conduct a cross-comparative analysis of descriptive metadata contained with curriculum development documents sourced from various UK HEIs. Emergent findings unearth the key skills and competencies that serve as the curriculum's foundation but also question whether a more consistent approach to construction management education should be sought.

This study aims to describe and analyze the differences in the linguistic styles of honest and dishonest stories.

This paper uses a descriptive study with a multivariate analysis of linguistic categories according to the story. The research analyzed 37 honest stories and 15 dishonest stories produced during actual legal proceedings through software Linguistic Inquiry and Word Count (LIWC).

The authors find that individuals who engage in deception use a different number of words when they narrate facts. The results suggest a need for additional investigation of the linguistic style approach because of its high applicability and detection accuracy. This approach should be complemented by other types of verbal, nonverbal and psychophysiological deception detection techniques.

Among the limitations, the authors consider length of the stories should be considered and scarce scientific literature in Spanish to compare with outcomes in English.

This research highlights the relevance to include linguistic style in real contexts to differentiate honest and dishonest stories due to objectivity and agility to implement.

Understanding deception as a social behaviour and its psychological processes associated are elements that contribute to people and justice to comprehend it.

Analyzing real statements and discriminate differences in linguistic style, contribute to understand deeply this important behaviour to propose new methodologies and theories to explain it.

In this article, the authors analyse the impact of the 2020 lockdown and the subsequent measures to contain the spread of COVID-19 in Italy in the hospitality industry by looking at the social demands brought forward by the restaurant sector.

To analyse social demands, the authors choose Twitter as an observation point using two hashtags as keywords to scratch the data: #iononriapro and #ioapro, which correspond to two different instances conveyed by the same subject: the restaurant sector. The instances linked to the hashtags produced different levels of engagement and penetration within the social structure and digital platform. To analyse the first block of data linked to the first hashtag-flag #iononriapro, the authors used content analysis. To analyse the second and third block of data linked to the hashtag-flag #ioapro, the authors used an automatic procedure, emotional text mining.

The analysis procedures allow us to reconstruct the positioning of the topics of closures and reopenings due to lockdown in this sector and to identify two explanatory dimensions: structural and affective, which explain the tension that has emerged between the State and the restaurant sector around COVID-related closures.

The study's findings not only contribute to the current understandings of the birth, transformation and penetration of social issues by the restaurant sector over the specific period linked to the COVID-19 pandemic and the measures imposed for its containment but are also valuable to analyse the dynamics through which Twitter hashtags and the social issues they represent find strength or lose interest in the public.

This paper aims to elaborate on current research on fourth party logistics “4PL” by offering a taxonomy that provides a deeper understanding of 4PL service offerings, thus drawing clear frontiers between existing 4PL business models.

The authors collected data using semi-structured interviews conducted with 60 logistics executives working in 44 “4PL” providers located in France. Using automatic analysis of textual data, the authors combined spatial visualisation, clustering analysis and hierarchical descending classification to generate the taxonomy.

Two key dimensions emerged, allowing the authors to clearly identify and distinguish four 4PL business models: the level of reliance on interpersonal relationships and the level of involvement in 4PL service offering. As a result, 4PL providers fall under one of the following business models in the taxonomy: (1) The Metronome, (2) The Architect, (3) The Nostalgic and (4) The Minimalist.

The study focuses on investigating 4PL providers located in France; thus, future studies should explore the classification of 4PL business models across different cultural contexts and social structures.

The findings offer valuable managerial insights for logistics executives and clients of 4PL to better orient their needs, the negotiations and the contracting process with 4PLs.

Using a Lexicometric analysis, the authors develop taxonomy of 4PL service providers based on empirical evidence from logistics executives; the work addresses the existing confusion regarding the conceptualisation of 4PL firms with other types of logistical providers and the role of in/formal interpersonal relationships in the logistical intermediation.

The recommendation of the analyst report is not only limited to a small number of ratings, but also biased toward a buy opinion with the absence of sell opinion. As an alternative to this, this paper aims to extract analysts' textual opinions embedded in the report body through text analysis and examine the profitability of investment strategies. Analyst opinion about a firm is measured by calculating the frequency of positive and negative words in the report text through the Korean sentiment lexicon for finance (KOSELF). To verify the usefulness of textual opinions, the author constructs a calendar-time based portfolios by the analysts' textual opinion variable of each stock. When opinion level is used, investment strategy has no significant hedged portfolio return. However, hedged portfolio constructed by opinion change shows significant return of 0.117% per day (2.57% per month). In addition, the hedged return increases to 0.163% per day (3.59% per month) when the opening price is used instead of closing price. This study show that the analysts’ opinion extracted from text analysis contains more detailed spectrum than recommendation and investment strategies using them give significant returns.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing extensive textual data. To bridge this knowledge gap, this paper introduces a new methodology that combines ML techniques with traditional qualitative approaches, aiming to reconstruct knowledge from existing publications.

In this pragmatist-rooted abductive method where human-machine interactions analyse big data, the authors employ topic modelling (TM), an ML technique, to enable constructivist grounded theory (CGT). A four-step coding process (Raw coding, expert coding, focused coding and theory building) is deployed to strive for procedural and interpretive rigour. To demonstrate the approach, the authors collected data from an open-source professional project management (PM) website and illustrated their research design and data analysis leading to theory development.

The results show that TM significantly improves the ability of researchers to systematically investigate and interpret codes generated from large textual data, thus contributing to theory building.

This paper presents a novel approach that integrates an ML-based technique with human hermeneutic methods for empirical studies in OM. Using grounded theory, this method reconstructs latent knowledge from massive textual data and uncovers management phenomena hidden from published data, offering a new way for academics to develop potential theories for business and management studies.

Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.