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The Accounting Information Systems (AIS) research field emerged around 30 years ago as a subfield of accounting but is at risk to develop further as an isolated discipline. However, given the importance of digitalization and its relevance for accounting, an amalgamation of the parent research field of accounting and the subfield of accounting information systems is pivotal for continuing relevant research that is of high quality. This study empirically investigates the distance between AIS research that is included in accounting literature and AIS research that prevails in dedicated AIS research outlets.

To understand which topics define AIS research, all articles published in the two leading AIS journals since 2000 were analyzed. Based on this topical inventory, all AIS studies that were published in the top 16 accounting journals, also since 2000, are identified and categorized in terms of topic, subtopic and research methodology. Next, AIS studies published in the general accounting field and AIS studies published in the AIS field were compared in terms of topics and research methodology to gain insights into the distance between the two fields.

The coverage of AIS topics in accounting journals is, to no small extent, concentrated around the topics “information disclosure”, “network technologies” and “audit and control”. Other AIS topics remain underrepresented. A possible explanation might be the focus on archival studies in accounting outlets, but other elements might play a role. The findings suggest that there is only a partial overlap between the parent accounting research field and the AIS subfield, in terms of both topic and research methodology diversity. These findings suggest a considerable distance between both fields, which might hold detrimental consequences in the long run, if no corrective actions are taken.

This is the first in-depth investigation of the distance between the AIS research field and its parent field of accounting. This study helped develop an AIS classification scheme, which can be used in other research endeavors. This study creates awareness of the divergence between the general accounting research field and the AIS subfield. Given the latter's relevance to the accounting profession, isolation or deterioration of the AIS research must be avoided. Some actionable suggestions are provided in the paper.

This study aims to explore the psychological effects of lockdown during the early stages of the COVID-19 pandemic on people living in an Italian prison. The suspension of family visits and most activities, along with the difficulties in applying social distancing to this vulnerable population was associated with increased psychological distress. Riots broke out over two days in more than 22 prisons across Italy at the beginning of March 2020, highlighting the negative psychological impact of the pandemic and the country’s emergency policies.

The research involves 17 men (Italians and foreigners) detained in a Lombardy prison from 1 March to 4 May 2020, corresponding to the lockdown phase in Italy. The qualitative content analysis (CA) of 27 posts, written by participants during that period and published on the blog “L’Oblò”, were analysed. The analysis allowed the identification of topics and subtopics that are related to two major categories of content: cognitions and emotional connotations about the COVID-19 lockdown in prison.

Analysis showed that blog post content was predominately negative in terms of emotional connotations. The most frequent coded negative emotional connotations were: missing, worry, psychological pain and fear, whilst the most frequent coded positive emotional connotations were: hope and gratitude for the support they received from prison workers. The rest of the blog content was coded as “cognitions”. Cognitions were coded as descriptions of lockdown effect on detention; prison during the COVID-19 emergency; the pandemic situation in general; and comparison between inside and outside prison.

The current study is original as it describes through blog CA the psychological condition of prisoners during the first COVID-19 pandemic lockdown in the most affected region in Italy.

Amazon Explore is a new online experience product from Amazon, which offers live stream sightseeing of destinations around the world. This paper aims to provide a first insight into how exactly Amazon Explore enters the virtual experience (VE) industry, and discuss the possible implications of its business model for the future of virtual and offline travel experiences.

Qualitative and quantitative methods of content analysis methods were employed in this study. The sample of this research consisted of 175 Amazon Explore products. Firstly, the authors analysed the content of each product, and then quantitative method were used to present the data. With this analysis, the authors presented how Amazon Explore is testing the foundations for new business models, and discuss the possible implications for the future of tourism. One year later, those same Amazon Explore products were reviewed again to check development progress. The following were the main questions regarding VE: How interactive are Amazon Explore products? How does Amazon Explore aim to ensure the monetisation of these products in an economically sustainable way in order to go beyond the “freemium” business model, often associated with VE as just another destination marketing tool?

This article presents the discussions of VE and virtual reality (VR). The authors determined the level of interactivity of the diverse VE offered themes with a model of four levels of interactivity. The results show that the analysed products achieve mostly only low levels of interaction. Regarding monetisation, Amazon Explore shows three possible future directions in the development of VE: as a marketing tool, as an extension of the offline experience and as potentially someday replacing real travel as part of the future monetisation scenario.

The article offers the first insights into Amazon's extension into the realm of VE in two time periods in 2020 and 2021, and, based on these preliminary results, discusses four possible scenarios for the future development of VE monetisation.

The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research.

In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.

This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.

In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.

Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.

Many entrepreneurs want to reach high to the heavens to achieve unlimited success. These hardworking, often underappreciated, venturers often crave fame and fortune as they strive to create their personal business legacy. One strategic path many have wandered down is that of the Initial Public Offering (IPO), whereby shares of the company are sold to the public. The IPO has many strong attractions. Large amounts of capital can be brought into the company.The company's stock can be used as currency to acquire other companies. Early investors realize a good ROI. Employees can perceive real value in their stock options. Customers, banks, vendors, and other stakeholders pay more respect to the company. Is this truly the entrepreneurʼs nirvana? Or is it a case of “Be careful of what you wish for because it may really come true?” Read on.

The purpose of this study is to chart the development of Makerspaces in higher education (MIHE), by building a map of existing research work in the field. Based on a corpus of 183 manuscripts, published between January 2014 and April 2021, it sets out to describe the range of topics covered under the umbrella of MIHE and provide a holistic view of the field.

The approach adopted in this research includes development of the 2014–2021 MIHE corpus; literature overview and initial coding scheme development; refinement of the initial coding scheme with the help of a focus group and construction of the MIHE map version 1.0; refinement of the MIHE map version 1.0 following a systematic approach of content analysis and development of the MIHE map version 2.0; evaluation of the proposed structure and inclusiveness of all categories in the MIHE map version 2.0 using card-sorting technique; and, finally, development of the MIHE map version 3.0.

The research trends in the categories of the MIHE map are discussed, as well as possible future directions in the field.

This paper provides a holistic view of the field of MIHE guiding both junior MIHE researchers to place themselves in the field, and policymakers and decision-makers who attempt to evaluate the current and future scholar activity in the field. Finally, it caters for more experienced researchers to focus on certain underinvestigated domains.

The purpose of this paper is to describe a range of innovative (for public library performance measurement and impact assessment) methods and tools developed by country teams as part of the Global Libraries (GL) initiative. Short reports are provided on: a return on investment study, a simplified data processing system for library managers and an online reporting system for public libraries in Ukraine; a study of the public image of Polish libraries in print mass media, two approaches to sustainability of performance measurement and impact assessment in Romania, through tools to conduct pop-up surveys and use of agricultural subsidies support data, assessments of changes in public library managers’ planning efforts in Poland and of their perceptions of libraries and their own role, using Modified Delphi forecasting, in Lithuania, two ways of focussing on the world of public library users by engaging non-profit organizations in library research in Poland and conducting impact studies in virtual environments in Chile.

A range of methods and tools and their uses are described.

No specific research findings are reported.

All of these tools and methods have been (or are being) trialed in national public library contexts; some have been developed over several years.

Useful for people in other (non-GL) countries who may be contemplating public library evaluation at regional, national or local level or who are interested in performance measurement and impact evaluation.

This paper is part of a GL effort to share what participants have learnt about impact planning and assessment in public libraries with the wider international libraries community.

The impact planning and assessment program of GL has been the largest sustained international public library evaluation program so far attempted. This paper reports on the more innovative evaluation activities undertaken at country level through this program.

This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research longitudinally from 1965 to 2015.

The study employs a quantitative intellectual content analysis of articles published in 30+ scholarly LIS journals, following the design by Tuomaala et al. (2014). In the content analysis, we classify articles along eight dimensions covering topical content and methodology.

The topical findings indicate that the earlier strong LIS emphasis on L&I services has declined notably, while scientific and professional communication has become the most popular topic. Information storage and retrieval has given up its earlier strong position towards the end of the years analyzed. Individuals are increasingly the units of observation. End-user's and developer's viewpoints have strengthened at the cost of intermediaries' viewpoint. LIS research is methodologically increasingly scattered since survey, scientometric methods, experiment, case studies and qualitative studies have all gained in popularity. Consequently, LIS may have become more versatile in the analysis of its research objects during the years analyzed.

Among quantitative intellectual content analyses of LIS research, the study is unique in its scope: length of analysis period (50 years), width (8 dimensions covering topical content and methodology) and depth (the annual batch of 30+ scholarly journals).

This research examines what motivates professional truck drivers to engage in eco-driving by linking their self-reports with objective driving scores.

Theory of Planned Behavior (TPB) is illustrated in an embedded, single-case study of a Finnish carrier with 17 of its truck drivers. Data are obtained through in-depth interviews with drivers, their fuel-efficiency scores generated by fleet telematics and a focus group session with the management.

Discrepancies between drivers’ intentions and eco-driving behaviors are illustrated in a two-by-two matrix that classifies drivers into four categories: ideal eco-drivers, wildcards, wannabes and non-eco-drivers. Attitudes, subjective norms and perceived behavioral control are examined for drivers within each category, revealing that drivers’ perceptions did not always align with the reality of their driving.

This study strengthens the utility of TPB through data triangulation while also revealing the theory’s inherent limitations in elucidating the underlying causes of its three antecedents and their impact on the variance in driving behaviors.

Managerial insights are offered to fleet managers and eco-driving solution providers to stipulate the right conditions for drivers to enhance fuel-efficiency outcomes of transport fleets.

This is one of the first studies to give a voice to professional truck drivers about their daily eco-driving practice.

The purpose of this paper is to identify knowledge gaps on insinuations of possible directions of European Union (EU) and international climate policies.

This study has used participatory approach of highly experienced stakeholders’ engagement, involved directly or indirectly in the process of policymaking. A range of priority issues has been initially identified through desk analysis and key stakeholders have been selected and invited to partake in the process. Preliminary results have been validated through interaction with stakeholders during a series of workshops.

The results show the identification of a series of sectors, where climate policy is expected to focus in the future and the definition of 11 specific topics upon which knowledge gaps are expected to appear. Results on the identified knowledge needs are analysed and categorized by each prioritized main topic and compared with literature findings. Emphasis is identified to be placed on the topics of renewable energy, EU climate policy and international climate negotiations, which are the most popular ones, followed by energy policy and energy efficiency.

A key element of the approach is the consideration of key experts’ feedback on their specific area of expertise, instead of general public engagement, therefore leading to accurate results. Despite the fact that our approach was applied to a specific problem, the overall analysis could provide a framework for supporting applications in various problems in the field of priorities’ identification and even expanding to decision-making problems.