As a researcher with a background in information systems, economics, and management sciences, my main area of interest is understanding the impact of emerging technologies on organizations, markets, and society. My interdisciplinary background has allowed me to approach research from different perspectives, with a deep understanding of the economic, management, and technical aspects of information systems.
My ongoing research focuses on using advanced analytics and machine learning techniques to investigate how organizations can benefit from these technologies. However, my work extends beyond simply using innovative methodologies. I also critically examine the importance of emerging technologies and the firms that produce them. This includes understanding the factors that drive the adoption of these technologies, as well as the potential implications of their widespread use on organizations, markets, and society as a whole.
Bendix, William, and Jon Mackay. 2022. “Fox in the Henhouse: The Delegation of Regulatory and Privacy Enforcement to Big Tech.” International Journal of Law and Information Technology XX (September): 1–20. https://doi.org/10.1093/ijlit/eaac011 (PDF)
Abstract
The Federal Trade Commission (FTC) requires tech giants to identify and remove apps from their platforms that use deceitful sales tactics or violate user privacy. Tech giants have often resisted FTC orders because policing diminishes their profits. But while some firms have eventually complied with FTC demands, other firms have continued to shirk enforcement at the risk of escalating fines. What accounts for these different responses? Examining Apple, Google and Facebook, we find that tech giants willingly police consumer fraud but not consumer privacy violations. Failures to police fraud have led to public complaints and negative press attention, while failures to police data breaches often go undetected by users, the media and thus the FTC. We conclude that tech giants can act as effective regulatory agents on the government's behalf, but only when they police activities they cannot conceal.
MacKay, J. 2021. “Re-connecting the public service: Openness and data sharing during the COVID19 pandemic.” In J. Toland (Ed.), From Yesterday to Tomorrow: 60 Years of Tech in New Zealand. (PDF)
Using emerging technology to manage the economy in lockdown
The COVID-19 Data Portal was a Stats NZ initiative to provide up-to-date information to policymakers and the public during a time of unprecedented uncertainty caused by the global pandemic. This chapter explains the development of the data portal by contextualizing its development broadly in terms of both the history of changes to the way the New Zealand public approached policy as well as the new responsibilities for data stewardship in government given to Statistics New Zealand.
MacKay, J. 2022. Data Discovery Challenge Using the COVID-19 Data Portal from New Zealand. Journal of Statistics and Data Science Education, 30(2): 187–190. (PDF)
Using technology to teach business students
Students need to know how to discern patterns and make decisions using visual information in our modern economy. However, there are few sources of real-world information available to instructors that give students access to visualizations to help develop their skills in interpreting complex situations using diverse data sources. This article outlines a teaching exercise that uses the New Zealand government’s data portal. This website contains detailed time series data and visualizations that span economic, social and health data derived from multiple government ministries and New Zealand businesses. The portal continues to be used by government decision-makers to make real-time decisions about the nation’s economy and citizen well-being. Typically, statistical agencies carefully vet the data they supply. The data portal prioritizes the timeliness of the information for decision-makers working in a crisis. This brief communication outlines an exercise for students to explore and interpret data through visualizations.
These papers bring computational methods to bear on topics ranging from history, trade and politics. Despite the diverse topics, all of these papers reflect my interest in network theory and data analytics.
MacKay, J. 2019. “Explaining Declining Economic Complexity in Canada: An Examination of Oil and Automotive Exports.” In M. S. Bonham (Ed.), Trade-Offs: The History of Canada-U.S. Trade Negotiations (1st edition): 147-170. Toronto, Canada: Canadian Business History Association.
Bendix, W., Mackay, J. 2017. “Partisan Infighting Among House Republicans: Leaders, Factions, and Networks of Interests” Legislative Studies Quarterly, 42(4): 549-577. (PDF)
About Partisan Infighting
This paper was published in a top field journal for Congressional studies. We wrote the paper at the height of the Tea Party movement but before Trump was a candidate. The paper takes aim at the dominant theories of the leadership of the Republican Party. The paper uses a network-based cluster analysis to infer different ideological factions within the US Republican Party. Based on the insights from this unsupervised learning method, we built a theory about how the Republican Party was becoming increasingly ungovernable. The party’s leadership had to balance the interests of factions oriented toward business interests, working-class interests, and an increasingly powerful ethno-radical faction. By the time the paper was published, Donald Trump was President. We wrote an op-ed in the Globe and Mail about the challenges Trump would face. I stand by our analysis, but we were wrong in our predictions. Trump managed to balance the factions by dramatically remaking the Republican Party’s policies – an outcome no one expected!
Mackay, J. 2017. “Canadian Regional and National Business Elites in 1912: Who Was Connected, Who Wasn’t and Why?” A History of Socially Responsible Business, c.1600-1950: 189-212. Palgrave Macmillan, Basingstoke, United Kingdom. (PDF)
About Canadian Regional and National Business Elites in 1912
This paper examines the regional reach and social structure of the corporate elite in Canada near the turn of the 20th century. Using the 'Directory of Directors of Canada' published in 1912, I recreate the national network of business elites and their business ties. I find evidence that businesses in most of the growing urban centres were hierarchically interlocked with the major business and financial centres of Montreal and Toronto. An exception to this pattern was the ethnic majority-German city of Berlin in Waterloo county. Berlin businesses had few ties within the existing Anglo-Canadian business hierarchy even though the area was an important manufacturing centre. This paper explores the hierarchical business network that existed across Canada and hypothesizes about how and why Berlin businesses were able to remain inward focused.
MacKay, J. 2017. “Dealing with Big Data and Network Analysis Using Neo4j and Python” Programming Historian. Programming Historian is both a peer-reviewed book and a website that remains open to contributions from the academic community. https://doi.org/10.46430/phen0074
MacKay, J. 2015. “Network Theory.” The SAGE Encyclopedia of Corporate Reputation, edited by Craig Carroll. Thousand Oaks, CA, USA: SAGE.
Brox, J., Carvalho, E., and MacKay, J. 2010. “Regional Employment Changes in a Booming Resource Economy: A Modified Shift-Share Analogue Regression of Changes in Employment Patterns Within the Economic Regions of Alberta.” Canadian Journal of Regional Science, 33(2): 25-44. (PDF)
About Regional employment changes
Regional Employment Changes is the result of my independent project during my master's degree in Economics. The paper uses a regional economic analysis technique called shift-share. The method lacks any specific mechanism to account for regional connectedness. This caused me to become more interested in network analysis. Here is the abstract:
This paper seeks to answer how a thriving energy sector in Alberta, Canada, has affected rates of employment growth in various occupations and industries within the regions of the province. To accomplish this, both a traditional shift-share method and a shift-share model based on occupational employment, rather than on the conventional industry data, and a version combining both sets of data are utilised. For all versions of the model, the regression analogue is used to estimate and test changes in eight regions across Alberta, Canada
This op-ed is based on our Partisan Infighting paper, above.
MacKay, J., Bendix, W. 2017, March 26. “Republican infighting key for candidate Trump. Now it’s a roadblock.” The Globe and Mail. Toronto, Canada.
Link to op-ed http://goo.gl/RQA4Ov
See the PDF version of the op-ed here.