EXED521-18DT (HAM)

Analytics and Digital Business

10 Points

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Waikato Management School
Te Raupapa
Executive Education


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: clive.wilkinson@waikato.ac.nz

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  • Calling +64 7 838 4466 select option 1, then enter the extension.
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Paper Description

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Often termed as the ‘new gold’, the large amount of digital data available to businesses today has the potential to generate new insights and disrupt traditional business operations. Information technology advancements also present possible risks that need to be adequately managed to create value from data. This paper addresses key digital business opportunities and risks, with a specific focus on how to create business value from big data.
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Paper Structure

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The format for classroom sessions is a combination of instructor- and student-led presentations and discussion to provide the insights that will help you 'scaffold' your own learning. In addition, this paper features several hands-on tutorials designed to help you extract, analyze, and visualize social media text, networks, and search engine data to enhance business decision making. The course is non-technical better suited for business managers, professionals, and management students.
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Learning Outcomes

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Students who successfully complete the course should be able to:

  • Articulate, in a non-technical manner, the key digital business issues and opportunities facing businesses today
    Linked to the following assessments:
  • Have a thorough understanding of analytics value creation concepts and theories
    Linked to the following assessments:
  • Identify data sources (such as social media, mobile, and internet of things) and the analytical tools needed mine it
    Linked to the following assessments:
  • Articulate the role of basic digital data mining tools and techniques to analyse, visualise, and interpret digital data to make driven business decision making
    Linked to the following assessments:
  • Recognise, identify, and explain critical issues surrounding digital data including, policies, securities, and privacy
    Linked to the following assessments:
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Analytics-Business Alignment Assessment

The assessment report will be grading based on the following criteria:

  • Thoroughness of the background information (company selected, the person you interviewed, and the rationale behind your choice) (5 Marks);
  • Thoroughness of the analysis and reporting (5 Marks);
  • Thoroughness of the recommendations presented (10 Marks);
  • Report formate, structure, and quality of writing (5 Marks).

Network Analysis Assesment:

Your goal is to construct the network and then write a short report about it. Describe interesting features, important institutions, relationships contained in the network. This is not something you can do just by looking at the structure: you will need to analyze the network property (both node level and network level properties). You should provide a deep investigation into the network. Who are the central institutions/universities? What is their role in the network (e.g., their Degree, Betweenness, and Eigenvector Centrality)? What are the big clusters? What does each represent? How did you find that out? Please include a thorough analysis of all the major features of the network. The report will be graded:

  1. The report includes a meaningful visualization of the network. Try to replicate figure 1 with different colours(I will not accept similar colours). You should filter the network, for example, highlighting the important nodes, showing the intensity of collaboration (e.g., with links width and colours) (5 marks).
  2. The report includes network-level statistics (such as total number nodes, clustering coefficient, average degree, density, and diameter) with a brief explanation of results. What does it say about the network? (5 marks).
  3. The report includes top a list of top 10 nodes (institutions) in terms of degree, betweennes, and eigenvector centralise accompanied by with brief explanation the results. Provide a meaningful description of who the institutions are, what their role is in the network, and how their research activities related to that role, etc. (5 marks).
  4. In terms of Eigenvector Centrality, what is the position of Waikato University as compared to others and what does it mean? (6 marks).

Text Analytics Assignment

Following criteria will be used to grade the assignment.

  • An interesting topic and meaningful topic and the tweets associated with it identified (5 Marks)
  • The central topical themes and players identified (e.g., what customers are saying about a particular product/service/issue) (5 points);
  • The underlying sentiment (positive, negative, and neutral) contained in tweets identified and explained (5 Marks).
  • Determine how to use information from (1) and (2) to improve products/services under examination as well as to develop a communication strategy to influence online discourse on this topic (6 Marks).

Organisational Digital Business Preparedness Assessment

Further details, including assessment criteria, will be provided mid-way through the paper.

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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Digital Analytics-Business Alignment
3 Jun 2018
11:00 PM
  • Online: Submit through Moodle
2. Network Data Visualisation and Interpretation (Group Work)
10 Jun 2018
11:00 PM
  • Online: Submit through Moodle
3. Text Data Visualisation and Interpretation (Group)
17 Jun 2018
11:00 PM
  • Online: Submit through Moodle
  • Presentation: In Class
4. Organisational Digital Business Preparedness Assessment
9 Jul 2018
11:00 PM
  • Online: Submit through Moodle
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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Required Readings

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All necessary material and readings will be provided including the text below. If you wish to read further, the books and articles in the following section are a good starting point.

Creating Value with Social Media Analytics: Mining Business Insights from Social Media Text, Actions, Networks, Hyperlinks, Apps, Search Engine, and Location Data by Gohar F. Khan, 2018, Create Space, ISBN: 11977543979.

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Recommended Readings

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The Web at Graduation and Beyond: Business Impacts and Developments, Gottfried Vossen, Frank Schönthaler, & Stuart Dillon, 2017, ISBN 978-3-319-60160-1

Creating Value with Big Data Analytics: Making Smarter Marketing Decisions, 1st Edition by Peter C. Verhoef et al., 2016, ISBN 1138837970.

Analyzing Social Media Networks with NodeXL: Insights from a Connected World 1st Edition by Derek Hansen, Ben Shneiderman, Marc A. Smith ISBN-13: 978-0123822291

Davenport, T. H. & Patil, D. J. 2012. Data scientist: The sexiest job of the 21st century. Harvard Business Review, October: 70-76.

Duhigg, Charles. “How Companies Learn Your Secrets.” New York Times Magazine. 16 Feb 2012.

Markovitch, S. & Willmott, P. 2014. Accelerating the digitization of business processes. McKinsey Quarterly, May: 1-4.

McAfee, A., & Brynjolfsson, E. 2012. Big data: The management revolution. Harvard Business Review, October: 59-68.

Week 1

Optimizing Your Digital Business Model, https://sloanreview.mit.edu/article/optimizing-your-digital-business-model/

Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital innovation management: reinventing innovation management research in a digital world. MIS Q., 41(1), 223-238.

Week 2

Akter, S., Bhattacharyya, M., Wamba, S. F., & Aditya, S. (2016). How does Social Media Analytics Create Value?.Journal of Organizational and End User Computing (JOEUC), 28(3), 1-9. doi:10.4018/JOEUC.2016070101

Bekmamedova N., and Shanks, G. "Social Media Analytics and Business Value: A Theoretical Framework and Case Study,"2014 47th Hawaii International Conference on System Sciences, Waikoloa, HI, 2014, pp. 3728-3737.

Karim, A., Noushad, K., Khan, G. F., (2016), Social media Analytics Capability Framework, proceedings of 20th Pacific Asia Conference on Information Systems (PACIS) 2016, Taiwan.

Chen, H., C. R.H.L., et al.(2012). "Business Intelligence and Analytics: From Big Data to Big Impact."MIS Quarterly36 (4): 1165-1188.

Fan, W., & Gordon, M. D. (2014). The power of social media analytics. Commun. ACM, 57(6), 74-81. 10.1145/2602574

Henderson, J. C. and N. Venkatraman (1993). "Strategic alignment: leveraging information technology for transforming organizations."IBM Syst. J.32(1): 4-16.

Ransbotham, S. (2015) "Once You Align the Analytical Stars, What's Next?".

Andreas M. Kaplan, Michael Haenlein, (2010), Users of the world, unite! The challenges and opportunities of Social Media, Business Horizons, Volume 53, Issue 1, January–February 2010, Pages 59-68, ISSN 0007-6813, https://doi.org/10.1016/j.bushor.2009.09.003. (http://www.sciencedirect.com/science/article/pii/S0007681309001232)

Boyd, d. m. and N. B. Ellison (2007). "Social Network Sites: Definition, History, and Scholarship." Journal of Computer-Mediated Communication13(1): 210-230.

Jan H. Kietzmann, Kristopher Hermkens, Ian P. McCarthy, Bruno S. Silvestre, Social media? Get serious! Understanding the functional building blocks of social media, Business Horizons, Volume 54, Issue 3, 2011, Pages (http://www.sciencedirect.com/science/article/pii/S0007681311000061)

Week 3

Khan, G. F., Yoon, H. Y., & Park, H. W. (2014). Social media communication strategies of government agencies: Twitter use in Korea and the USA. Asian Journal of Communication, 24(1), 60-78. 10.1080/01292986.2013.851723

Khan G. F., Jacob W.,”Knowledge Networks of the Information Technology Management Domain: A Social Network Analysis Approach,”Communications of the Association for Information Systems: Vol. 39, Article 18.

Steketee, M., Miyaoka, A., & Spiegelman, M. (2015). Social Network Analysis A2 - Wright, James D International Encyclopedia of the Social & Behavioral Sciences (Second Edition) (pp. 461-467). Oxford: Elsevier.

Monaghan, S., Lavelle, J., & Gunnigle, P. (2017). Mapping networks: Exploring the utility of social network analysis in management research and practice. Journal of Business Research, 76, 136-144. https://doi.org/10.1016/j.jbusres.2017.03.020

Week 4

Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding Satisfied and Dissatisfied Hotel Customers: Text Mining of Online Hotel Reviews. Journal of Hospitality Marketing & Management, 25(1), 1-24. 10.1080/19368623.2015.983631

Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241-4251. http://dx.doi.org/10.1016/j.eswa.2013.01.019

He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472. http://dx.doi.org/10.1016/j.ijinfomgt.2013.01.001

Chakraborty, G., M. Pagolu, et al. (2013).Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS, SAS Institute.

Shulman, S. (2014). "Five Pillars of Text Analytics, available at:http://www.screencast.com."

Week 5

CHOI, H. and VARIAN, H. (2012), Predicting the Present with Google Trends. Economic Record, 88: 2–9. doi:10.1111/j.1475-4932.2012.00809.x

Zhang, S., & Cabage, N. (2017). Search engine optimization: Comparison of link building and social sharing.The Journal of Computer Information Systems,57(2), 148-159.

Iredale S., Heinze A. (2016) Ethics and Professional Intimacy Within the Search Engine Optimisation (SEO) Industry. In: Kreps D., Fletcher G., Griffiths M. (eds) Technology and Intimacy: Choice or Coercion. HCC 2016. IFIP Advances in Information and Communication Technology, vol 474. Springer, Cham

Yang, X., Pan, B., Evans, J. A., & Lv, B. (2015). Forecasting Chinese tourist volume with search engine data. Tourism Management, 46, 386-397. http://dx.doi.org/10.1016/j.tourman.2014.07.019

Week 6 (Stuart)

Week 7

Debatin, B., Lovejoy, J. P., Horn, A.-K. and Hughes, B. N. (2009), Facebook and Online Privacy: Attitudes, Behaviors, and Unintended Consequences. Journal of Computer-Mediated Communication, 15: 83–108. doi:10.1111/j.1083-6101.2009.01494.x

Pekka, A. (2010). Social media, reputation risk and ambient publicity management. Strategy & Leadership, 38(6), 43-49. 10.1108/10878571011088069

Wu He,(2012)"A review of social media security risks and mitigation techniques",Journal of Systems and Information Technology,Vol. 14Issue: 2,pp.171-180,https://doi.org/10.1108/13287261211232180

Wu He, (2013) "A survey of security risks of mobile social media through blog mining and an extensive literature search",Information Management & Computer Security, Vol. 21 Issue: 5, pp.381-400,https://doi.org/10.1108/IMCS-12-2012-0068

Ajami, R., Qirim, N. A., & Ramadan, N. (2012). Privacy Issues in Mobile Social Networks. Procedia Computer Science, 10, 672-679. http://dx.doi.org/10.1016/j.procs.2012.06.086

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Other Resources

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Tools and resources

Twitter Tools

Twitter Analytical Applications (no programming skills required)

Twitter Help Resources

Facebook Tools

Applications (no programing skills required)

YouTube Tools

Applications (no programming skills required)

Data Resources

  • Pajek (a social network analysis software)sample datasets(note that Pajek network data can be imported into NodeXL).
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Online Support

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All paper materials will be provided through Moodle.
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The total learning hours for this paper is 100. Twenty one of these will occur in-class, with the remaining being taken up by class preparation (when required) and assignment work.
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