Prioritising the Big Issues: revealing pertinent sustainability issues with 'big data'

Materiality analysis is a process used to ensure that organisations are focusing on issues that have a direct or indirect impact on an organisation’s ability to create, preserve or erode economic, environmental and social value for itself, its stakeholders and society. It is important to understand a large range of forces driving an organisation towards sustainability (shown in the picture below). Some of these drivers are easier to measure and understand than others.

drivers picture.png

The Drivers of Business Sustainability (Whitehead 2018) 

For example, regulations are either announced before implementation, or are already in effect, and are well documented. Science is reported on and published, and a business usually has at least some understanding of its own performance levels, risks, or weaknesses. What has been harder to measure are stakeholders’ sentiments around sustainability. Understanding which issues consumers or wider society is concerned about typically requires expensive and time-consuming social surveys. However, a new approach to understanding wider public sentiments around different sustainability issues is rapidly becoming possible thanks to ‘big data’ available on internet search behaviour.

I am exploring an approach using ‘big data’ from internet search queries to determine the saliency of different sustainability issues, across different countries, and over time. Based on millions of Google searches, public interest in 64 different sustainability issues has been mapped across eight countries. The data are analysed and trend lines are fitted. Based on aggregate search data, per capita search data, and trends in searches over time, it is possible to establish the importance of different sustainability issues across different cultural contexts, and forecast future salient topics for organisations to address. This information can be a powerful aid in setting organisational sustainability strategies, and tailoring sustainability reporting to public concerns. This page presents the results of different search query studies of interest in sustainability issues.

Internet searches have been shown to reflect real world behaviour, in that, online behaviour is a representation of offline interests. Studies have shown that data from internet search trends closely matches, and is sometimes superior to social surveys when exploring issue saliency. Some scientific studies, which address this line of enquiry, are listed below. In a follow up article, I will be describing the relevancy of internet search trends to the United Nations Sustainable Development Goals, which are growing in importance for many businesses.

Jay Whitehead |

References (Science Base)

Askitas, N., & Zimmermann, K. F. (2009). Google Econometrics and Unemployment Forecasting. Applied Economics Quarterly, 55(2), 107-120. doi:10.3790/aeq.55.2.107

Carneiro, H. A., & Mylonakis, E. (2009). Google Trends: A Web-Based Tool for Real-Time Surveillance of Disease Outbreaks. Clinical Infectious Diseases, 49(10), 1557-1564. doi:10.1086/630200

Hand, C., & Judge, G. (2012). Searching for the picture: forecasting UK cinema admissions using Google Trends data. Applied Economics Letters, 19(11), 1051-1055. doi:10.1080/13504851.2011.613744

Jie, Q., & Tai-Quan, P. (2016). Googling environmental issues: Web search queries as a measurement of public attention on environmental issues. Internet Research, 26(1), 57-73. doi:doi:10.1108/IntR-04-2014-0104

Jun, S.-P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological Forecasting and Social Change, 130, 69-87. doi:

Kristoufek, L. (2013). Can Google Trends search queries contribute to risk diversification? [Article]. Scientific Reports, 3, 2713. doi:10.1038/srep02713

Mitsutoshi, N., Hiroaki, S., Toshihiro, W., Katsumi, T., & Yoshimasa, S. (2018). Mining online activity data to understand food consumption behavior: A case of Asian fish sauce among Japanese consumers. Food Science & Nutrition, 6(4), 791-799. doi:doi:10.1002/fsn3.622

Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying Trading Behavior in Financial Markets Using Google Trends [Article]. Scientific Reports, 3, 1684. doi:10.1038/srep01684

Reilly, S., Richey, S., & Taylor, J. B. (2012). Using Google Search Data for State Politics Research: An Empirical Validity Test Using Roll-Off Data. State Politics & Policy Quarterly, 12(2), 146-159. doi:10.1177/1532440012438889

Ruohonen, J., & Hyrynsalmi, S. (2017). Evaluating the use of internet search volumes for time series modeling of sales in the video game industry [journal article]. Electronic Markets, 27(4), 351-370. doi:10.1007/s12525-016-0244-z

Seifter, A., Schwarzwalder, A., Geis, K., & Aucott, J. (2010). The utility of “Google Trends” for epidemiological research: Lyme disease as an example [Google Trends, Lyme disease, ticks, epidemiology.]. 2010, 4(2), 3. doi:10.4081/gh.2010.195

Siliverstovs, B., & Wochner, D. S. (2018). Google Trends and reality: Do the proportions match?: Appraising the informational value of online search behavior: Evidence from Swiss tourism regions. Journal of Economic Behavior & Organization, 145, 1-23. doi:

Simeon, V., & Torsten, S. (2011). Forecasting private consumption: survey-based indicators vs. Google trends. Journal of Forecasting, 30(6), 565-578. doi:doi:10.1002/for.1213

Soriano-Redondo, A., Bearhop, S., Lock, L., Votier, S. C., & Hilton, G. M. (2017). Internet-based monitoring of public perception of conservation. Biological Conservation, 206, 304-309. doi:

T., F. M., T., J. A., Louis, T., & Ed, D. (2018). Internet Searches for Affect-Related Terms: An Indicator of Subjective Well-Being and Predictor of Health Outcomes across US States and Metro Areas. Applied Psychology: Health and Well-Being, 10(1), 3-29. doi:doi:10.1111/aphw.12123

Tomas, R., Nicolás, M., & Ricardo, I. (2018). Using Internet Search Data to Measure Changes in Social Perceptions: A Methodology and an Application*. Social Science Quarterly, 99(2), 829-845. doi:doi:10.1111/ssqu.12449

Wu, L., & Brynjolfsson, E. (2015). The future of prediction: How Google searches foreshadow housing prices and sales. In Economic analysis of the digital economy (pp. 89-118): University of Chicago Press.

Yan, C.-S., & Felipe, L. (2013). Nowcasting with Google Trends in an Emerging Market. Journal of Forecasting, 32(4), 289-298. doi:doi:10.1002/for.1252

Jay Whitehead