Teaching information technology alongside business analytics: Case study

Al-Haddad, Serina; Thorne, Betty; Ahmed, Vikram; Sause, William

doi:10.1080/08832323.2018.1507987

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Interestingly, Al‐Haddad et al. (2019) report that students who learned Excel alongside IBA performed better than students learning only Excel skills in an information technology course. Also, literature shows that purpose‐built Excel templates or workbooks can play a vital role in teaching logistic regression or Bayesian methods in more advanced courses (Brusco, 2022; Johnson & Berenson, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Using Excel to teach analytics also improves student employability as competency in Excel is expected of business graduates (Rienzo & Chen, 2018) and essential for entry-level jobs in business analytics (Cegielski & Jones-Farmer, 2016). Interestingly, Al-Haddad et al (2019) report that students who learned Excel alongside IBA performed better than students learning only Excel skills in an information technology course. Also, literature shows that purpose-built Excel templates or workbooks can play a vital role in teaching logistic regression or Bayesian methods in more advanced courses (Brusco, 2022;Johnson & Berenson, 2019).…”

Section: The Use Of Microsoft Excelmentioning

confidence: 99%

“…In response to the rapid development of the digital economy and data‐based decision‐making in the business world, many universities have introduced applied business analytics into undergraduate business curricula (Al‐Haddad et al., 2019; Mamonov et al., 2015; Phelps & Szabat, 2017; Yazici, 2020). A significant portion of business decision‐making requires dichotomously framing the determinates of outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Teaching binary logistic regression modeling in an introductory business analytics course

Hoang

Watson

2022

Decision Sci J Innov Edu

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There is an increasing demand to introduce Introductory Business Analytics (IBA) courses into undergraduate business education. Many real-world business contexts require predictive analytics to understand the determinants of a dichotomous outcome; hence, IBA courses should include binary logistic regression analysis. This article provides our reflective discussions on the design of learning activities and assessments to assist business students in learning binary logistic regression in an IBA course. Data on student engagement and learning outcomes are used to shed light on the impacts of teaching logistic regression on student learning and experience. Notably, students opt to focus their assessment work more on logistic regression than on multiple regression analysis, showing the potential attraction of students toward binary logistic regression analysis. We also observed several challenges, mainly related to the use of Excel, that require special attention from instructors.

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Section: Literature Reviewmentioning

confidence: 99%

Section: The Use Of Microsoft Excelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Teaching binary logistic regression modeling in an introductory business analytics course

Hoang

Watson

2022

Decision Sci J Innov Edu

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“…Exports play an essential role in creating employment and increasing foreign exchange earnings of a country and can be remembered as a growth strategy (Daruich et al , 2019; Feenstra et al , 2019; Leonidou, 2000). In recent decades, the growth of information technology (IT) has been the driving force behind the success of export expansion between companies and countries (Al-Haddad et al , 2019; Andersson et al , 2004; Dehgani and Navimipour, 2019; Narins, 2018). The need for e-commerce stems from the private and public sectors’ demand to use IT to achieve customer satisfaction and effective coordination within the organization (Liu and Li, 2020).…”

Section: Introductionmentioning

confidence: 99%

A new model for assessing the role of IT-based systems, public policies and business intelligence on the export competitiveness’s efficiency

Lakzi¹

2021

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Purpose By using some tools such as business intelligence (BI) and exploring data stored in data warehousing systems, it is possible to increase export competition efficiency. Efficiency in the export competition is affected by several factors. Therefore, this paper has attempted to provide a new conceptual framework for assessing the impact of new information technology (IT)-based systems, public policies and BI on the efficiency of export competitiveness. Design/methodology/approach Increasing exports and its high efficiency depend on each country’s IT. So, IT is the main tool to increase the production process, improve quality and reduce products’ cost. In addition, each country develops policies for business activities and commerce to increase productivity in export competition. Clarifying public policies and strategies critical to the development of export competition is important in this field. Therefore, this paper has tried to provide a new conceptual framework for examining the impact of new IT-based systems, public policies and BI on the effectiveness of export competition. The research method of this study is based on structural equation modeling. This method has been used to evaluate the causal model, assess the reliability and check the measurement model’s validity. Model and data were analyzed using SPSS 22 and SmartPLS software. Findings The results showed that the impact of new IT-based systems, public policies and BI on export competitiveness efficiency has been positive. Research limitations/implications To generalize this study’s results and have a more accurate external validity, it is necessary to conduct this research program over a more extended time and space. Also, larger and more comprehensive samples should be taken for further validation. The authors also suggest that other factors can be considered. Practical implications The framework is conceptually innovative in modeling the efficiency of export competitiveness and assessing implications. Export competitiveness is a crucial factor in long-term global farm business survival, as it fosters opportunities for business prosperity on global markets. Originality/value The research offers important hints for emerging markets to find their ways to maintain a sustainable competitive advantage in the export market by controlling the country-level factors. This research will be helpful to both policymakers and global managers.

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Spreadsheet skills training: Required and needs improvement

Roth

2023

Journal of Education for Business

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Teaching information technology alongside business analytics: Case study

Cited by 4 publications

References 19 publications

Teaching binary logistic regression modeling in an introductory business analytics course

Teaching binary logistic regression modeling in an introductory business analytics course

A new model for assessing the role of IT-based systems, public policies and business intelligence on the export competitiveness’s efficiency

Spreadsheet skills training: Required and needs improvement

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