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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 enhance the knowledge by offering perspectives from an emerging market by reviewing the existing literature on food tourism. This study applies a multimethod analysis, providing insights into the advancement of food tourism in India, the contributions, critical findings and emerging research themes.

The study adopted a multimethod analysis using descriptive, narrative and bibliometric analysis using VOSviewer to analyze research papers published on food tourism in India shortlisted from Scopus and Google Scholar.

Future research should focus on gauging the potential of food tourism in different regions of the country, applying theoretical frameworks that have not been used. “Culinary Diversity and Heritage,” “Sustainable and Responsible Food Tourism,” “Consumer Behavior and Food Tourism Experiences,” “Rural Development and Economic Impact,” “Technology and Digitalization,” “Stakeholder Collaboration,” “Food Safety and Hygiene” and “Policy and Regulation” were the identified future research themes. These findings are significant for emerging economies across the globe.

This study summarized the progress of food tourism research in India, the contributions and emerging themes, supplementing the need to study food tourism in India. This study contributed methodologically to the literature by adopting a multimethod review combining descriptive, narrative and bibliometric analysis. Finally, by proposing ideas and constructs for future research, this study provides directions for future food tourism research from an emerging market’s perspective.

Findings will assist managers and stakeholders in comprehending the factors influencing tourists, destination marketing and branding, aiding in effective planning and executing food tourism strategies to promote destinations. The identified themes and keywords can help academicians and researchers dwell upon the emerging research themes in food tourism.

To the best of the author’s knowledge, this is the first study analyzing food tourism research in India adopting a multimethod analysis. The study provides an in-depth analysis applying descriptive, narrative and bibliometric analysis, highlighting the critical trends and research themes. A framework for future studies was also proposed.

该研究旨在通过回顾有关美食旅游的现有文献, 提供来自新兴市场的观点, 从而强化现有的知识。本研究采用多方法分析, 深入了解印度美食旅游的发展、贡献、重要发现和新兴研究主题。

该研究采用了多种方法分析, 使用 VOSviewer 进行描述性、叙述性和文献计量分析, 以分析从 Scopus 和谷歌学术搜索中入围的关于印度美食旅游的研究论文。

未来的研究应侧重于衡量该国不同地区美食旅游的潜力, 应用尚未使用的理论框架。 “烹饪多样性和传统”、“可持续和负责任的美食旅游”、“消费者行为和美食旅游体验”、“农村发展和经济影响”、“技术和数字化”、“利益相关者协作”、“食品安全和卫生”, “政策与法规”是确定的未来研究主题。这些发现对全球新兴经济体具有重要意义。

本研究总结了印度美食旅游研究的进展、贡献和新兴主题, 补充了研究印度美食旅游的必要性。本研究采用结合描述性、叙述性和文献计量分析的多方法回顾, 在方法论上为文献做出了贡献。最后, 通过提出未来研究的思路和结构, 本研究从新兴市场的角度为未来的美食旅游研究提供了方向。

调查结果将帮助管理者和利益相关者理解影响游客、目的地营销和品牌的因素, 帮助有效规划和执行食品旅游战略以促进目的地。 确定的主题和关键词可以帮助院士和研究人员深入研究食品旅游中新兴的研究主题。

El estudio tiene como objetivo mejorar el conocimiento al ofrecer perspectivas de un mercado emergente mediante la revisión de la literatura existente sobre turismo gastronómico. Este estudio aplica un análisis de múltiples métodos, que proporciona información sobre el avance del turismo gastronómico en la India, las contribuciones, los hallazgos críticos y los temas de investigación emergentes.

El estudio adoptó un análisis multimétodo mediante un análisis descriptivo, narrativo y bibliométrico utilizando VOSviewer para analizar los artículos de investigación publicados sobre el turismo gastronómico en la India preseleccionados de Scopus y Google Scholar.

La investigación futura debería centrarse en calibrar el potencial del turismo gastronómico en las distintas regiones del país, aplicando marcos teóricos que no se han utilizado. “Diversidad culinaria y patrimonio”, “Turismo gastronómico sostenible y responsable”, “Comportamiento del consumidor y experiencias de turismo gastronómico”, “Desarrollo rural e impacto económico”, “Tecnología y digitalización”, “Colaboración de las partes interesadas”, “Seguridad e higiene alimentaria”, “Política y regulación” fueron los temas de investigación futura identificados. Estos hallazgos son significativos para las economías emergentes de todo el mundo.

Este estudio resumió el progreso de la investigación sobre turismo alimentario en la India, las contribuciones y los temas emergentes, complementando la necesidad de estudiar el turismo alimentario en la India. Este estudio contribuyó metodológicamente a la bibliografía al adoptar una revisión multimétodo que combina el análisis descriptivo, narrativo y bibliométrico. Por último, al proponer ideas y constructos para futuras investigaciones, este estudio proporciona orientaciones para futuras investigaciones sobre el turismo gastronómico desde la perspectiva de un mercado emergente.

Los resultados ayudarán a los gestores y a las partes interesadas a comprender los factores que influyen en los turistas, el marketing de los destinos y la creación de marcas, contribuyendo a una planificación y ejecución eficaces de las estrategias de turismo gastronómico para promocionar los destinos. Los temas y palabras clave identificados pueden ayudar a los académicos e investigadores a profundizar en los temas de investigación emergentes en el turismo gastronómico.

Se trata del primer estudio que analiza la investigación sobre turismo gastronómico en la India adoptando un análisis multimétodo. El estudio proporciona un análisis en profundidad aplicando un análisis descriptivo, narrativo y bibliométrico, destacando las tendencias críticas y los temas de investigación. También se propone un marco para futuros estudios.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

This study aimed to evaluate the potential of artificial intelligence (AI) as a tool for knowledge synthesis, the production of written content and the delivery of coaching conversations.

The research employed the use of experts to evaluate the outputs from ChatGPT's AI tool in blind tests to review the accuracy and value of outcomes for written content and for coaching conversations.

The results from these tasks indicate that there is a significant gap between comparative search tools such as Google Scholar, specialist online discovery tools (EBSCO and PsycNet) and GPT-4's performance. GPT-4 lacks the accuracy and detail which can be found through other tools, although the material produced has strong face validity. It argues organisations, academic institutions and training providers should put in place policies regarding the use of such tools, and professional bodies should amend ethical codes of practice to reduce the risks of false claims being used in published work.

This is the first research paper to evaluate the current potential of generative AI tools for research, knowledge curation and coaching conversations.

The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.

This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.

The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.

According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

Websites, search engines, recommender systems, artificial intelligence and digital libraries have the potential to support serendipity for unexpected interaction with information and ideas which would lead to favored information discoveries. This paper aims to explore the current state of research into serendipity particularly related to information encountering.

This study provides bibliometric review of 166 studies on serendipity extracted from the Web of Science. Two bibliometric analysis tools HisCite and RStudio (Biblioshiny) are used on 30 years of data. Citation counts and bibliographic records of the papers are assessed using HisCite. Moreover, visualization of prominent sources, countries, keywords and the collaborative networks of authors and institutions are assessed using RStudio (Biblioshiny) software. A total of 166 papers on serendipity were found from the period 1989 to 2022, and the most influential authors, articles, journals, institutions and countries among these were determined.

The highest numbers of 11 papers were published in the year 2019. Makri and Erdelez are the most influential authors for contributing studies on serendipity. “Journal of Documentation” is the top-ranking journal. University College London is the prominent affiliation contributing highest number of studies on serendipity. The UK and the USA are the prominent nations contributing highest number of research. Authorship pattern for research on serendipity reveals involvement of single author in majority of the studies. OA Green model is the most preferred model for archiving of research articles by the authors who worked on serendipity. In addition, majority of the research outputs have received a citation ranging from 0 to 50.

To the best of the authors’ knowledge, this paper may be the first bibliometric analysis on serendipity research using bibliometric tools in library and information science studies. The paper would definitely open new avenues for other serendipity researchers.

Open-access repositories (OARs) are essential for openly disseminating intellectual knowledge on the internet and providing free access to it. The current study aims to evaluate the growth and development of OARs in the field of technology by investigating several characteristics such as coverage, OA policies, software type, content type, yearly growth, repository type and geographic contribution.

The directory of OARs acts as the source for data harvesting, which provides a quality-assured list of OARs across the globe.

The study found that 125 nations contributed a total of 4,045 repositories in the field of research, with the USA leading the list with the most repositories. Maximum repositories were operated by institutions having multidisciplinary approaches. The DSpace and Eprints were the preferred software types for repositories. The preferred upload content by contributors was “research articles” and “electronic thesis and dissertations”.

The study is limited to the subject area technology as listed in OpenDOAR; therefore, the results may differ in other subject areas.

The work can benefit researchers across disciplines and, interested researchers can take this study as a base for evaluating online repositories. Moreover, policymakers and repository managers could also get benefitted from this study.

The study is the first of its kind, to the best of the authors’ knowledge, to investigate the repositories of subject technology in the open-access platform.

Seekers who visit professional virtual communities (PVCs) are usually motivated by knowledge-seeking, which is a complex cognitive process. How do seekers search for knowledge, and how is their search linked to prior knowledge or PVC situation factors? From the cognitive process and interactional psychology perspectives, this study investigated the three-way interactions between seekers’ expertise, task complexity, and perceptions of PVC features (i.e. knowledge quality and system quality) on knowledge-seeking strategies and resultant outcomes.

A field experiment was conducted with 119 seekers in a PVC using a 2 × 2 factorial design of seekers’ expertise (i.e. expert versus novice) and task complexity (i.e. low versus high).

The study reveals three significant insights: (1) For a high-complexity task, experts adopt an ask-directed searching strategy compared to novices, whereas novices adopt a browsing strategy; (2) For a high-complexity task, experts who perceive a high system quality are more likely than novices to adopt an ask-directed searching strategy; and (3) Task completion time and task quality are associated with the adoption of ask-directed searching strategies, whereas knowledge seekers’ satisfaction is more associated with the adoption of browsing strategy.

We draw on the perspectives of cognitive process and interactional psychology to explore potential two- and three-way interactions of seekers’ expertise, task complexity, and PVC features on the adoption of knowledge-seeking strategies in a PVC context. Our findings provide deep insights into seekers’ behavior in a PVC, given the popularity of the search for knowledge in PVCs.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.