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Data collection, use, leveraging, and sharing as a business practice and advantage has proliferated over the past decade. Along with this proliferation of data collection is the increase in regulatory activity which continues to morph exponentially around the globe. Adding to this complexity are the increasing business disruptions, productivity and revenue losses, settlements, fines, and penalties which can amount to over $15 million, with many penalties now being ascribed to the organization's leadership, to include the Board of Directors (BoD), the CEO and members of the senior leadership team (SLT). Thus, boards of directors can no longer ignore and in fact must embrace data privacy as a critical part of doing business in the digital world. In fact, not embracing data privacy as a critical part of their strategy, not only puts their stakeholders and stockholders at risk, but also places the future success of their organization in jeopardy. Additionally, increasingly through legal, regulatory, and normative occurrences, Boards are being pressured into taking a more active role in the data privacy activities in their organizations. Therefore, it behooves the BoD to be proactive vice reactive toward their data privacy endeavors.

Social media can contribute to an inclusive society, but they are also asymmetrical and polarised communication spaces. This requires competent teachers to build critical digital citizenship. The aim of this article is twofold: to present web scraping and text analytics as tools that define teachers' digital competences, and to investigate which posts on Twitter and Instagram are most viral in relation to education, disability and inclusion. A total of 48,991 publications in Spanish and English were analysed, corresponding to the period from 13 October 2021 to 1 May 2022. The 100 most viral posts were selected, and correlations were identified between the sentiment, gender and influence associated with the content, its temporal and geographic space. The results show that economic and political influence groups are the most viral, relegating non-profit organisations or individuals with altruistic outreach to second place; only on international days is this trend reversed. Bots do not interfere to impose messages; it is artificial intelligence algorithms that overshadow vindictive and humanistic content. The most influential people are predominantly male, associated with institutional accounts in the political sphere. It is concluded that Big Data and Business Intelligence tools help teachers to analyse relevant educational and social issues, and to acquire a collective ethic in the face of new educational challenges.

Businesses and other organizations across the globe are becoming more and more data-driven, using a combination of descriptive, diagnostic, predictive and prescriptive analytics to gain a strategic advantage through understanding the past, what we hope to happen in the future, and the ability to accurately predict future outcomes. These forms of analytics span from basic statistical summaries and data visualization to artificial intelligence models. Many organizations are now requiring new job applicants, new hires, and existing employees to be data literate. As such, it is becoming incumbent on teachers, students, and practitioners to possess some basic knowledge or experience in business analytics, at least within their educational and functional domains. Current best-practice in business school curriculum embeds some form of analytics across the curriculum. Unfortunately, many business colleges do not have the experience or resources to do so, hence teachers are unprepared to teach, and students are not prepared to enter the business world being data literate. While higher levels of analytics can be statistically intimidating, there are numerous applications of analytics that do not require statistics or higher-level models. This paper introduces one such technique practiced within marketing education and industry since 1995 and is called RFM. RFM has long been known in marketing curriculum and practice but has seen virtually no exposure in business schools outside of marketing major courses. This reflects an unintended consequence of teaching and learning within "functional" silos. It is hoped that teachers and students across the business curriculum, as well as workforce participants, can use this case to gain an appreciation of data literacy and analytics toward application within any functional area of business. The purpose of this paper is to avail those outside of marketing education and practice with an effective, easy to understand, easy to apply model, with no statistics involved. The goal is to facilitate increased data literacy and interest in understanding and/or applying analytics to other functional arear of business. RFM is not unique to this paper but is aimed at broadening teacher, student and workforce participant experience and knowledge of business analytics.

In statistics classes, the central limit theorem has been demonstrated using simulation-based illustrations. Known population distributions such as a uniform or exponential distribution are often used to consider the behavior of the sample mean in simulated samples. Unlike such simulations, a number of real-data-based simulations are here implemented in which the populations are empirical distributions of data selected from Japanese firms. The dataset chosen contains 38 variables familiar to business students, such as sales and assets. The maximum population size of the variables is 2243. One thousand samples with replacement are selected for specific variable--sample size combinations. Hypothesis testing results indicate that the normality hypothesis for the sample mean is rejected for 31 variables at the 0.1% level even with a sample size of 500. It is emphasized that the data for these variables indicate that this should not be a surprise, and emphasize the importance of looking at data.

This teaching tip describes using Microsoft Power BI Desktop in a class to analyze unstructured data from an exit survey of prior students from a Master of Science in Management Information Systems program. Results from a short survey administered to these students showed that the students, using the no-code Power BI, were able to accomplish their text analytics tasks in a shorter period, and with less overall effort, as compared to using traditional code-rich text analytics methods. The process is described in detail as to how the students, new to unstructured data analysis, can uncover these results. This process can be replicated by other instructors teaching text analytics.

This study aims to validate a data collection tool's psychometric properties to explore whether business management research's relevance is a high-order construct made up of its perceived interest and perceived relevance. The authors conducted a confirmatory factor analysis to validate a two-factor scale. The sample was composed of 148 MBA students from a Peruvian business school. Results showed the appropriate levels of validity and reliability and confirmed the usefulness of this data set for exploring the relationship between perceived interest and relevance. Businesses and schools can replicate the validated tool for reviewing their curricula and teaching practices, exploring the gap between business and management research results and advancing managers' research literacy and evidence-based practice in business education and training.

The ongoing datafication of our social reality has resulted in the emergence of new data-based business models. This development is also reflected in the education market. An increasing number of educational technology (EdTech) companies are entering the traditional education market with data-based teaching and learning solutions, and they are permanently transforming the market. However, despite the current market dynamics, there are hardly any business models that implement the possibilities of Learning Analytics (LA) and Artificial Intelligence (AI) to create adaptive teaching and learning paths. This paper focuses on EdTech companies and the drivers and barriers that currently affect data-based teaching and learning paths. The results show that LA especially are integrated into the current business models of EdTech companies on three levels, which are as follows: basic Learning Analytics, Learning Analytics and algorithmic or human-based recommendations, and Learning Analytics and adaptive teaching and learning (AI based). The discourse analysis reveals a diametrical relationship between the traditional educational ideal and the futuristic idea of education and knowledge transfer. While the desire for flexibility and individualization drives the debate on AI-based learning systems, a lack of data sovereignty, uncertainty and a lack of understanding of data are holding back the development and implementation of appropriate solutions at the same time.

Business analytics is a fast-growing area in practice. The rapid growth of business analytics in practice in the recent years is mirrored by a corresponding fast evolution of new educational programs. While more than 130 graduate and undergraduate degree programs in business analytics have been launched in the past 5 years, no commonly accepted model of business analytics curriculum yet exists. Drawing on competency-based curriculum design literature, we take the first steps towards initiating a debate on the model curriculum in business analytics. We analyze a sample of business analytics job announcements from different industries and identify a preliminary set of business analytical competencies sought in practice. Further, we examine six existing graduate programs in business analytics, which reveal divergent approaches to business analytics curricula. These institutions were selected since they offered a graduate degree program in business analytics for at least two years. Our findings indicate that there are significant variations in the program structure in terms of program length (10 to 18 months) and flexibility (electives comprise 0 to 37% of the course work). We also found that the programs vary greatly in the coverage of both traditional analytics and the new emergent technologies and analytical methods. We conclude with a commentary on the emergent trends in business analytics in practice and the opportunities presented by these trends for the academia.

Business Intelligence (BI) and Business Analytics (BA) Software has been included in many Information Systems (IS) curricula. This study surveyed current and past undergraduate and graduate students to evaluate various BI/BA tools. Specifically, this study compared several software tools from two of the major software providers in the BI/BA field. The participants in the study evaluated each software tool according to three key criteria: 1) functionality, 2) ease of use, and 3) learning effectiveness. The "learning effectiveness" criterion was used to determine which BI/BA tools provided the most effective learning of BI/BA concepts in the IS classroom. The three criteria were used to develop recommendations for including specific BI/BA software tools in the IS curriculum. Based on the findings of the study, the authors recommend that colleges and universities consider the use of the IBM-Cognos suite of tools as a viable means for teaching BI/BA concepts in their Information Systems curricula. The results of the study are relevant to any college or university that currently includes (or is considering the inclusion of) Business Intelligence / Business Analytics concepts in its Information Systems curriculum.

In this research, a combination of both quantitative and qualitative approaches is used to identify different market segments in the education industry. To solve the research problem, an exploratory approach to data mining is used and, using a series of interviews with experts, the factors affecting segmentation are identified. Then, using the clustering method (in the form of specific two-step and K-means algorithms), customers are clustered and features of each cluster are identified. This research is based on data provided by a large Iranian research and education company. After examining the clusters identified in both methods, it is determined that the clusters provided by the two-step algorithm are more in line with the organizational and market reality of the business. Finally, the marketing mix model is used to formulate strategic approaches and actions.

Over the past 20 years, many colleges and universities have made significant progress in building data warehouses and implementing business intelligence tools to provide better access to administrative data that were previously locked away in enterprise systems. However, the data architectures from the 1990s and 2000s are aging as new technologies offer expanded possibilities to collect, manage, and engage with data, including tapping into sources of data that were not available using previous approaches. This is coming at a time where there are increasing demands from stakeholders across higher education (internal and external) to "see the data" in response to constricting sources of funding, pressure to make higher education more affordable, changing student populations, and increased focus on student success and on-time graduation. This chapter starts with a look at the historical approaches most schools have taken, then explores new analytic opportunities for IR offices and the impact these new technologies have on its operations while providing support for data-driven decision-making. The chapter concludes with a look at the future of business intelligence (BI) and data analytics and what it means for higher education.

Product development of Japanese companies has changed greatly from the post-war revival period, when it was promoted mainly to provide low-cost, high-quality products. In recent years, it has become necessary to develop products and services that correspond to social issues. Research and development has shifted from a technological basis to a perspective based on societal trends for developing next-generation products. According to Kubo (2011) and Asai (2013), scenario planning is an effective method for considering the future environment in R&D. Furthermore, the functions and roles of the information section, because of the contributions of information services, have changed along with the use of the R&D situation. Based on findings from questionnaires and interviews of Japanese companies, this paper presents a discussion of the importance of information collection and intelligence generation for scenario planning along with the role of an information section in the area of research and development.

Within the context of the telecom industry, this teaching case is an active learning analytics exercise to help students build hands-on expertise on how to utilize Big Data to solve a business problem. Particularly, the case utilizes an analytics method to help develop a customer retention strategy to mitigate against an increasing customer churn problem in a telecom company. Traditionally, the forecast of customer churn uses various demographic and cell phone usage data. Big Data techniques permit a much finer granularity in the prediction of churn by analyzing specific activities a customer undertakes before churning. The authors help students to understand how data from customer interactions with the company through multiple channels can be combined to create a "session." Subsequently, the authors demonstrate the use of effective visualization to identify the most relevant paths to customer churn. The Teradata Aster Big Data platform is used in developing this case study.

Big Data is currently a hot topic in the field of technology, and many campuses are considering the addition of this topic into their undergraduate courses. Big Data tools are not just playing an increasingly important role in many commercial enterprises; they are also combining with new digital devices to dramatically change privacy. This article will argue that it is absurd to teach about Big Data in our classes without discussing its effect on privacy in our society.

As the study of sexual commerce has grown dramatically in recent decades due to interest in HIV/AIDS, an expanded literature has scrutinized how research teams manage the operational challenges of accessing spaces that typically resist scrutiny. This paper ventures a combination of both scholarly reflections on the utility of ethical listening and specific methodologies for working with hard-to-reach populations, and selective use of field notes to illustrate the ethical and operational challenges of data collection with marginalized youth. The paper highlights several pivotal commitments and procedures for generating an effective community-based research project, the extent of time demanded for such research, and collective reflections on the potential for both harm and good in such projects. Efforts to understand the social context in which young adults engage in sexual exchange--both on the street and in erotic dance clubs--requires a commitment to ethical listening, and to progressive learning.

Assessing program impact can provide useful program evaluation data. It also provides a basis for program development, marketing, and justification. This article discusses recent impact evaluation efforts and findings of a long-time Extension program; referred to as Business Retention and Expansion (BR&E). How such information can be communicated to existing program underwriters and prospective program partners is also described.

The present study investigated the processes information professionals, working in a business environment, follow to meet business clients' information needs and particularly their involvement in information synthesis and analysis practices. A combination of qualitative and quantitative data was collected via a survey of 98 information professionals across North America and follow-up interviews with eight Canadian information professionals. It was found that there is an increasing need for value-added services, which incorporate synthesis and analysis but the level of information professionals' involvement differs depending on a range of factors such as clients' needs and attitudes, information professionals' knowledge of the subject area and their length of time working in a particular organization. Information synthesis and analysis in a business context is mostly a collaborative process and principles of analysis are required throughout the entire cycle of information seeking. For the effective transition of information professionals to information synthesists and analysts more effective support may be required to develop a set of essential skills and knowledge. (Contains 4 figures.)

Background: Businesses have been exposed to many positive accounts of the benefits of employee wellness to improve employee performance as well as reduce health and injury claims costs for the employer. However, many do not have the tools or experience to effectively demonstrate the benefits of a workplace wellness program for their own management and other stakeholders. Purpose: This paper presents a) basic components for evaluating a workplace wellness program, b) observations gleaned from evaluating a wellness project, and c) a design for a simple evaluation system that provides information about the efficacy of the wellness program and establishes a foundation for more formal wellness program evaluation. Setting: Maine. Intervention: Evaluating a wellness program and creating a system of measures for a sustainable evaluation system. Research Design: Steps in wellness program evaluation are delineated (common definitions, evaluation design, i.e., logic model, data collection tools, data collection, comparison analyses, and ROI) and carried out. Particulars for a sustainable wellness program evaluation are rendered. Data Collection and Analysis: We describe how to apply the process and measurements depicted in the logic model. We illustrate a method for calculating return on investment (determine the ratio of known wellness program costs to decreases in injury claims costs and sick leave costs). We recommend components for a sustainable evaluation system based on our experience in actualizing the logic model. Findings: Applying the analyses we found positive benefits of employee wellness in our Maine DOT case. Workers' compensation hours claims dropped from 875 hours in 2006, to 236 hours in 2007. Strains contributed roughly 17% of the overall injury costs reported for all three years, and almost one third of the costs for 2005. We computed a four-year ROI of $2.90. To help establish regular and routine wellness program evaluation we describe and recommend additional data sources and measurement points. (Contains 2 figures and 2 tables.)

This paper focuses on firm behaviour within business clusters and cluster-type environments. Using survey data to distinguish cluster members by the perceived degree of participation, the authors categorize them as leading, proactive or reactive players. They then examine the clustering behaviour of each of these categories. They find that (a) cluster development needs leaders or champions who are willing to commit time and effort to take the initiative; (b) when committed, leading players facilitate culture change so that open relationships with other cluster members can be established relatively easily; and (c) the champions of clusters are prepared to share information and knowledge in exchange for mutual benefits. Therefore, leadership and championing are matters of priority for supporters of cluster initiatives. (Contains 5 tables.)

Purpose: The motivation for this investigation is to apply social networking features to a semantic network portal, which supports the efforts in enterprise training units to up-skill the employee in the company, and facilitates the creation and reuse of knowledge in online communities. Design/methodology/approach: The paper provides an overview of an emerging area for work-related research in the field of knowledge management and collaborative online communities. Findings: The growing number of social network online communities requires a systematic assessment of the application and design of social network technologies, which makes this study relevant and timely. Practical implications: This paper gives guidance in an emerging research area with major implications for online communities and human resources management. Originality/value: Fulfils a need, since a lack of literature in the field is apparent. (Contains 2 figures and 2 tables.)

Churn management is a concern for businesses, particularly in the digital economy. A customer relationship framework is proposed to help deal with churn issues. The model integrates the electronic channel and involves four tools for enhancing data collection, data treatment, data analysis and data integration in the decision-making process. Explains the adequacy of data mining for alleviating churn issues. (Author/AEF)

Presents issues concerning return on investment (ROI) and its use in education and training. Discussion includes building a feasible ROI process; criteria for an effective ROI process; a practical model for ROI, which includes preliminary evaluation, data collection, isolating program effects, converting data to monetary values, tabulating program costs, calculating the ROI, and identifying intangible benefits. (AEF)

The practical goal of graduate education is placement of graduates. But what does "placement" mean? Academics use the word without thinking much about it. "Placement" is a great keyword for the graduate-school enterprise. For one thing, its meaning certainly gives a purpose to graduate education. Furthermore, the word is a portal into the way of thinking about the success or failure of what one does. So who determines the definition of placement that directs--and also constrains--the work of graduate deans? None other than their fellow deans and administrators. The problem is embedded in the foundation of the edifice itself. Reform therefore depends upon a more self-conscious definition of the word "placement." It should mean the jobs that graduate students want--not just the academic kind. A broader definition would overturn the common assumption that a Ph.D. is a pipeline leading to an evaporating pool of academic jobs. How do advisers know what kinds of jobs their graduate students want? By asking them, and then by periodically asking them again, to see if the answer changes over time. That's part of good graduate advising. Successful placement of graduate students has never been harder these days, and not just because the academic job market is a mostly dry well. As recent graduates know, the nonacademic market isn't exactly overflowing, either.

Surveyed attitudes toward the importance of privacy, seriousness of various privacy invasions, organizations that are trusted or mistrusted, etc. Concluded, among other findings, that most respondents were wary of possible invasions of privacy by the government but preferred government regulation of data collection by private businesses to self-regulation by businesses. (PD)