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How do organisations know which problems are worthy of their attention? Despite good intentions, many attempts to solve problems fail. One reason for this failure might be because of attempts to solve non-problems or to solve problems with insufficient means, a concept proposed by Deming as tampering. The purpose of this paper is to suggest a definition of tampering, outline what is currently known about possible practical implications of tampering and to suggest how to extend this knowledge by proposing an agenda for future research.

To fulfil the purpose, a narrative literature review was conducted.

Through this review, common aspects of what constitutes tampering are identified and the following definition is proposed: Tampering is a response to a perceived problem in the form of an action that is not directed at the fundamental cause of the problem, which leads to a deterioration of the process or the process output. In addition, recommendations are generated regarding how tampering manifests itself in practice and why tampering occurs. These recommendations could be studied in future research.

To the best of the authors’ knowledge, this is the first paper that suggests a revitalisation of tampering. The results presented in this paper form the basis for continued studies on how tampering in organisations can be understood, managed and prevented.

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.

Analytics technologies are profoundly changing the way in which organizations generate economic and social value from data. Consequently, the professional roles of data scientists and data analysts are in high demand in the labor market. Although the technical competencies expected for these roles are well known, their behavioral competencies have not been thoroughly investigated. Drawing on the competency-based theoretical framework, this study aims to address this gap, providing evidence of the emotional, social and cognitive competencies that data scientists and data analysts most frequently demonstrate when they effectively perform their jobs, and identifying those competencies that distinguish them.

This study is exploratory in nature and adopts the competency-based methodology through the analysis of in-depth behavioral event interviews collected from a sample of 24 Italian data scientists and data analysts.

The findings empirically enrich the extant literature on the intangible dimensions of human capital that are relevant in analytics roles. Specifically, the results show that, in comparison to data analysts, data scientists more frequently use certain competencies related to self-awareness, teamwork, networking, flexibility, system thinking and lateral thinking.

The study was conducted in a small sample and in a specific geographical area, and this may reduce the analytic generalizability of the findings.

The skills shortages that characterize these roles need to be addressed in a way that also considers the intangible dimensions of human capital. Educational institutions can design better curricula for entry-level data scientists and analysts who encompass the development of behavioral competencies. Organizations can effectively orient the recruitment and the training processes toward the most relevant competencies for those analytics roles.

This exploratory study advances our understanding of the competencies required by professionals who mostly contribute to the performance of data science teams. This article proposes a competency framework that can be adopted to assess a broader portfolio of the behaviors of big data professionals.

This paper aims to describe the “InDO: Institute Demographic Ontology” and demonstrates the InDO-based semiautomated process for both generating and extending a knowledge graph to provide a comprehensive resource for marginalized US graduate students. The knowledge graph currently consists of instances related to the semistructured National Science Foundation Survey of Earned Doctorates (NSF SED) 2019 analysis report data tables. These tables contain summary statistics of an institute’s doctoral recipients based on a variety of demographics. Incorporating institute Wikidata links ultimately produces a table of unique, clearly readable data.

The authors use a customized semantic extract transform and loader (SETLr) script to ingest data from 2019 US doctoral-granting institute tables and preprocessed NSF SED Tables 1, 3, 4 and 9. The generated InDO knowledge graph is evaluated using two methods. First, the authors compare competency questions’ sparql results from both the semiautomatically and manually generated graphs. Second, the authors expand the questions to provide a better picture of an institute’s doctoral-recipient demographics within study fields.

With some preprocessing and restructuring of the NSF SED highly interlinked tables into a more parsable format, one can build the required knowledge graph using a semiautomated process.

The InDO knowledge graph allows the integration of US doctoral-granting institutes demographic data based on NSF SED data tables and presentation in machine-readable form using a new semiautomated methodology.

A literature review of 28 data literacy, education articles from 2010 to 2018 was conducted to gain a better understanding of the current state of data literacy research.

A systematic literature review of ERIC, Education Source, and JSTOR was conducted. Articles were included in this literature review if they focused on “data literacy” for K-12 teachers or leaders.

Results demonstrated that the concept of data literacy has become more concrete, but there is still disagreement about the parameters of the construct. While data literacy was shown to be gaining in importance, training from schools of education were focused heavily on assessment literacy. Four recommendations are made as follows: (1) create skill-focused educator prep programs, (2) encourage opportunities for collaboration, (3) model data use from both quantitative and qualitative sources and (4) investigate the role of technology and big data on data literacy.

The scope of this literature review was very narrow and, as such, does not fully encapsulate data-driven decision-making in K-12 education overall.

Data literacy is important for both teachers and leaders, as educational environments strive to better understand individual learners and improve learning outcomes. This literature review looks to pull together the current status of data literacy research with hopes of inspiring more targeted research that influences training practices for both teachers and leaders.

This study aimed to determine mothers' knowledge of children with congenital heart disease (CHD) and to examine the relationship between types of CHD, the duration of treatments, the perception of the severity of illness and the mothers' knowledge.

A correlation study was conducted among 84 mothers of children (from infancy to six years old) with CHD who had attended pediatric cardiology clinics and pediatric units in three tertiary hospitals in Bangkok, Thailand. The two questionnaires aimed to evaluate the mothers' knowledge and perceptions of the severity of illness. Descriptive statistics, Spearman's rank-order correlation and Fisher's exact test were used to analyze the data.

Knowledge levels of mothers of children with CHD were at a high level with a mean score of 34.79 (SD = 8.23), but the knowledge domain of preventing complications was at a low level with a mean score of 14.95 (SD = 5.28). The types of CHD and the perceptions of illness were not correlated with the mothers' knowledge, but the duration of treatments was significantly correlated (r = 0.271, p < 0.05).

Healthcare professionals, especially nurses, should emphasize proper health education on complication prevention and the duration of treatments for children. Moreover, mothers should be supported to nurture children with CHD to reduce possible complications and prepare for cardiac surgery where needed.

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.

Faculties have a vital role to play in ensuring that their graduates are trained with relevant skills. Formally arranged capacity building programs (CBPs) can aid in training and retraining the faculties with relevant contemporary skills so that they in turn can do justice to their students. The purpose of this study is to analyze the efficacy of such public-funded online CBPs in enhancing faculties’ research and teaching capabilities.

Ascientifically designed questionnaire was sent to faculties who attended 12-day public-funded online capacity building workshop. Binomial logistic regression models were constructed to analyze as to how effective the online workshop was in enhancing teaching and research skills of the faculties involved.

From the research study, the authors were able to infer that although faculties were able to understand theoretical concepts of qualitative nature relatively easily, the authors felt that its value addition in enhancing the research output was rather limited. The study also found that the faculties felt teaching concepts outside their “syllabus” to be counterintuitive. A significant finding of the study was that research had an important role in enhancing their teaching efficacy.

Studies which are undertaken to test the efficacy of online capacity building workshops are scarce. This domain is going to gain importance in near future as technology is evolving at a rapid rate and online training of faculties helps in optimizing scarce resources.