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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.

Data science promises new opportunities for organizational decision-making. Data scientists arguably play an important role in this regard and one can even observe a certain “buzz” around this nascent occupation. This paper enquires into how data scientists construct their occupational identity and the challenges they experience when enacting it.

Based on semi-structured interviews with data scientists working in different industries, the authors explore how these actors draw on their educational background, work experiences and perception of the contemporary digitalization discourse to craft their occupational identities.

The authors identify three main components of data scientists’ occupational identity: a scientific mindset, an interest in sophisticated forms of data work and a problem-solving attitude. The authors demonstrate how enacting this identity is sometimes challenged through what data scientists perceive as either too low or too high expectations that managers form towards them. To address those expectations, they engage in outward-facing identity work by carrying out educational work within the organization and (paradoxically) stressing both prestigious and non-prestigious parts of their work to “tame” the ambiguity and hype they perceive in managers’ expectations. In addition, they act upon themselves to better appreciate managers’ perspectives and expectations.

This study contributes to research on data scientists as well as the accounting literature that often refers to data scientists as new competitors for accountants. It cautions scholars and practitioners alike to be careful when discussing the possibilities and limitations of data science concerning advancements in accounting and control.

The purpose of the article is to gain more insight into factors that can explain students' success in business subjects. The focus is on the connection between performance in introductory courses in business mathematics (BM) and business statistics (BS) and success in various business subjects.

Use of a regression model with administrative data from a business school in Norway over a period of 10 years.

The findings show a strong correlation, especially in quantitative subjects. The results suggest that statistical skills are more strongly related to academic success than mathematical skills.

The data are collected from only one school. No information on undergraduates' personalities and behaviours is available.

There are limited published studies that have explored the relationship between success in statistics and later achievements in business courses. This is useful knowledge for planning the content of the bachelor's programme.

The study's objective was to address the factors impacting the employability attributes of fresh graduates in the Kingdom of Bahrain while considering the various challenges. This study used a quantitative approach which employed the questionnaire tool, and data were collected by using a convenience sampling method. The study sample comprised n = 385 respondents from different industries, including manufacturing, banking and finance, hospitality, healthcare, oil and gas, and real estate sectors of Bahrain. Data gathered from questionnaire were analyzed using Statistical Package for the Social Sciences (SPSS), where descriptive and inferential statistics was used to analyze the data. The results of the study showed for the major Hypothesis 1 that the demographic variables have no significant statistical impact on employment attributes of the new fresh graduates. Moreover, findings suggest that null hypothesis for major Hypothesis 2 has been rejected as applied academic skills and critical thinking skills have no significant impact on employability attribute of fresh graduates in the Kingdom of Bahrain. Null hypothesis for major Hypothesis 3 has been accepted as findings suggest that technology use skills (β ₁ = 0.080), system thinking skills (β ₂ = 0.210), communication skills (β ₃ = 0.402), and information skills (β ₄ = −0.100) which are an antecedent of workplace skills, have significant statistical impact on employability attribute of fresh graduates in Kingdom of Bahrain. Lastly, null hypothesis for major Hypothesis 4 has been accepted as findings suggest that interpersonal skills (β ₅ = 0.229) which are an antecedent of effective relationship have significant statistical impact on employability attribute of fresh graduates in the Kingdom of Bahrain.

Although the Net Promoter Score (NPS) index is simple, NPS has weaknesses that make NPS's interpretation misleading. The main criticism is that identical index values can correspond to different levels of customer loyalty. This makes difficult to determine whether the company is improving/deteriorating in two different years. The authors describe the application of statistical tools to establish whether identical values may/may not be considered similar under statistical hypotheses.

Equal NPSs with a “similar” component composition should have a two-way table satisfying marginal homogeneity hypothesis. The authors compare the marginals using a cumulative marginal logit model that assumes a proportional odds structure: the model has the same effect for each logit. Marginal homogeneity corresponds to null effect. If the marginal homogeneity hypothesis is rejected, the cumulative odds ratio becomes a tool for measuring the proportionality between the odds.

The authors propose an algorithm that helps managers in their decision-making process. The authors' methodology provides a statistical tool to recognize customer base compositions. The authors suggest a statistical test of the marginal distribution homogeneity of the table representing the index compositions at two times. Through the calculation of cumulative odds ratios, the authors discriminate against the hypothesis of equality of the NPS.

The authors' contribution provides a statistical alternative that can be easily implemented by business operators to fill the known shortcomings of the index in the customer satisfaction's context. This paper confirms that although a single number summarizes and communicates a complex situation very quickly, the number is ambiguous and unreliable if not accompanied by other tools.

This study aims to assess the scholarly communication competence of Zambian library and information science (LIS) professionals by evaluating their awareness, knowledge and practices regarding scholarly publication.

Applying a quantitative research approach, the study used a specially designed questionnaire. Responses from 57 professionals across universities and colleges in Zambia were gathered using convenience sampling. Data analysis involved descriptive statistics, mean and standard deviation calculations and t-values and p-values to understand respondents’ perceptions and knowledge of scholarly communication and publication.

The findings revealed significant gaps in respondents’ knowledge and awareness, particularly regarding predatory journals, journal selection factors, open-access models, publication challenges, reference management software (RMS) usage and research obstacles. The study underscored the necessity for increased training and capacity-building initiatives among Zambian LIS professionals to enhance their scholarly communication competence.

This research contributed to the field by highlighting deficiencies in scholarly communication awareness among Zambian LIS professionals. It emphasised the need for targeted interventions, awareness programs and educational support to improve academic literacy and scholarly publication practices. Additionally, the study suggested future research avenues, such as longitudinal studies and strategies for enhancing RMS adoption, to advance scholarly practices among Zambian professionals further.

This study aims to explore the emergence of the human resource (HR) analyst role. The job posts on LinkedIn display the industry demand and skills required by the organizations. This study identifies the different knowledge, skills and abilities (KSA) required for an HR analyst role in different stages of professional growth (i.e. entry-level, middle-senior level and top-level) across different industries/sectors as applicable to the crisis.

A total of 80 job posts were extracted from LinkedIn. Details such as industry, job levels, qualifications, job experience, job functions, job descriptions (JDs) and job skills (JS) were collected. Further, 30 videos were extracted from YouTube and converted into text. Text analysis was conducted using NVivo software to analyze JDs, JS and job functions. Using NVivo, word frequency, word cloud, word tree and treemap were created to visualize the data. Finally, ten in-depth interviews were conducted with senior HRA managers based in India to understand the essential competencies required for the HR analyst role and the strategies to develop them.

The findings indicate that not only technical skills are needed, but business and communication skills are particularly important for all job levels during a crisis. The JD word cloud showed words, such as data, business, support and management, and the word tree depicted HR data and change agents as important words with many related sentences as branches. General JS included analytical, communication, problem-solving and management. Technical JS were the most widely used and included structure query language, system applications & products in data processing, human capital management, TABLEAU, management information system and PYTHON. Strategies to develop these competencies included case studies, live projects, internships on HR analytics (HRAs) assignments and mentoring by senior HRA professionals.

The sample used was small, as the study included 80 job posts available on LinkedIn restricted to India. The study was restricted to qualitative approach and text analytics was used. Survey methods and a quantitative approach can be used to collect data from HR recruiters, job holders and senior leaders to understand the role of HRAs in the job market and then these variables can be tested empirically.

Based on the McCartney et al.’s (2020) competency model for the HR Analyst role, this study has explored the KSA framework using data visualization techniques and used text analytics to analyze LinkedIn job posts for different levels, videos from YouTube and in-depth interviews. It also mapped the KSA for the HR analyst role to the various stages of crisis system management given by Mitroff (2005). The use of social media analytics, such as analyzing LinkedIn data and YouTube videos, are highlighted.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India.

Study employs an in situ experiment using a conceptual model based on learning theory. The participant's end-of-semester GPA is Performance Indicator. Integrating PBL with classroom teaching is unique instructional approach to this study. An unsupervised and supervised data mining approach to analyse PBL impact establishes research conclusions.

The administration of PBL results in improved learning patterns (above-average) for students with medium attendance. PBL, Gender, Math background, Board and discipline are contributing factors to students' performance in the decision tree. PBL benefits a student of any gender with lower attendance.

This study is limited to course students from one institute and does not consider external factors.

Researchers can apply learning patterns obtained in this paper highlighting PBL impact to study effect of every innovative pedagogical study. Classification of students based on learning behaviours can help facilitators plan remedial actions.

1. Clustering is used to extract student learning patterns considering dynamics of student performances over time. Then decision tree is utilized to elicit a simple process of classifying students. 2. Data mining approach overcomes limitations of statistical techniques to provide knowledge impact in presence of demographic characteristics and student attendance.

Knowledge is the source of competitive advantage, but when shared at all levels. Unfortunately, there is a universal unruly present in the form of knowledge hiding at employees’ level, but the causes and remedies are still vague as past studies have rarely investigated the causes of daily knowledge hiding behavior. Against this backdrop, this study aims to entail a daily diary method investigation of the role of daily abusive supervision in daily employees’ knowledge hiding through the mediation of dehumanization and moderation of psychological capital.

The data for this study is collected using a daily diary method approach, which estimates the daily workplace events and their continuous influence on employees’ feelings (i.e. dehumanization) and actions (knowledge hiding). The daily responses of 279 respondents were considered useful for analysis purposes.

The findings of the study revealed that the daily events of abusive supervision have both direct and indirect (through dehumanization) influence on employees’ daily knowledge hiding behavior. Moreover, psychosocial capital has a significant conditional influence in the relationships of negative workplace treatments (abusive supervision and dehumanization) and their outcomes (i.e. knowledge hiding).

The study provides some theoretical and practical insights by providing the explanatory and coping mechanism between continuous abusive supervision and daily knowledge hiding behavior.

There is a dearth of literature that has focused on daily episodes of abusive supervision, dehumanization and knowledge hiding behavior. Furthermore, the moderating role of psychological capital has also been rarely investigated.