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Editor’s Note:

This essay, conducted by Chong(Alex)Wang, Professor of Guanghua School of Management, Peking University, and his collaborators, is meant to be urged IT researchers to expand economics of IT research’s boundary to make it a reference discipline for other fields. New and innovative research projects must push beyond the boundaries that restrict questions and methods. We must focus on novel and interesting research questions, adopt correct and useful methods, and achieve important and impactful research outcomes.

The following summary is excerpted from the paper The Economics of IT and Digitization: Eight Questions for Research, which was published in MIS Quarterly Vol. 45.

01 Background

The development of information technology (IT) and digitization in past 30 years has not only generated challenging research questions but also provided novel tools for answering them. Theorists studying the economics of IT and digitization have embraced opportunities for innovation.

We have explored basic economics-related methodologies, questions, and emerging topics such as the future of work, the changing boundaries of the firm, online auctions and online consumer behavior. IT research now drives frontier work in finance, marketing, operations management, and even human resource management. Yet, to what extent are economics of IT researchers leading this theory advancement?

02 Opportunities for Leading Economics of IT and Digitization Research

IT has profound, broad, and multifaceted implications for economic development. Nano-data from the digitization of production, market transactions, and human behavior allow rapid data-driven decision making (Brynjolfsson and McElheran 2016; Brynjolfsson et al. 2020). AI and automation technologies boost productivity in all types of businesses, but at the same time disrupt the labor economy. Products and services are merging online and offline experiences, and consumption is becoming more social. Consumers are making better choices by using search engines, recommender systems, and AI-powered decision-support tools to retrieve and process information.

Markets are changing too. Digital markets are lowering prices, increasing variety and transparency, and allowing consumers and businesses to search more efficiently (Brynjolfsson and Smith 2000). New IT such as chatbots, blockchain, virtual reality, and live streaming will further lead to a more open, sharing, efficient and increasingly digital economy.

03 Criteria and Challenging Questions

Just as IT has disrupted the economy in the digital age, researchers studying the economics of IT and digitization should go beyond narrow research silos and confront challenging theories of decision making, industrial organization, labor economics, social welfare and equality, and other subfields of economics. We should ultimately be a reference discipline for research in fields such as marketing, finance, and operations management, particularly by answering forward-looking questions and shifting attention to broadly defined topics, according to the following two tables.

Table 1 presents six evaluation criteria for evaluating and advancing new economics of IT and digitization research. They are helpful for us to expand research questions, methods, and outcomes. Such expansion requires that we embrace disruptive research and shatter our imaginary security and fictitious identity.

Table 2 highlights eight important but under-researched questions for the next generation of economics of IT theories. These challenging and disruptive questions suggest promising research opportunities. For each question, we propose some example research topics according to the judging criteria.

04 Leading the Research Paradigm Shift with IT

Research on the economics of IT and digitization should fully embrace new IT-enabled research methodologies. Digitization and computation technologies have profoundly transformed social science research, particularly economics, and provided new tools for qualitative and quantitative understanding of society. Nano-data, machine-learning, and disruptive digital infrastructures are empowering a paradigm shift that features semi-automatic theory discovery, prediction-oriented research, and massive field experiments (Figure 1).

Nano-data from search engines, clickstreams, social media posts, and IoT open new frontiers for accurate predictions. Remote sensing and mobile device data provide comprehensive spatial information once difficult to obtain. Faster mobile broadband connections, higher-resolution cameras, and smarter digital machinery continue expanding full spectrum, real-time observations of socioeconomic activities. Even better, digital platforms yield real-time quantitative data. Massive online field experiments conducted on such platforms offer invaluable opportunities for theory development (Gupta et al. 2018; Karahana et al. 2018).

Advanced machine learning algorithms may disrupt methods for discovering and testing theories by providing abundant and surprisingly effective tools for analyzing nano-data. Economics of IT research has just begun to embrace applications of machine learning algorithms to generate new variables, inspire innovative questions, suggest new theories, empirically identify causal relationships, predict counterfactuals, and simulate policy outcomes.

Nano-data, machine-learning, and digital infrastructures drive disruptive paradigm shifts in theory development. Researchers can now conduct extensive field experiments beyond previous capacities. They can analyze real-time, nonstructured data for new insights, innovative patterns, and accurate theory- and data-driven predictions. Such changes will continue challenging the current paradigm of economics of IT and digitization research.

05 Summary and Practical Implications

Next-generation innovation requires economics of IT and digitization researchers to lead in embracing new research methodologies and answering new questions. The future requires all community members to recognize the value of interdisciplinary research, ask the right questions, accept new methodologies, and contribute to other disciplines.

To achieve our goal to be a reference discipline for other fields, we must expand our questions, methodologies, and outcomes. Our research can and should bring IS/IT knowledge or methods to management disciplines outside our domain. We must consider questions that may appear to be more relevant to marketing or finance if the questions address novel and important challenges in the digital economy. Simultaneously, we should adopt unfamiliar methodologies that appear to address economic questions in data science and machine learning. Such openness and confidence will create important interdisciplinary research opportunities that contribute to all fields.

Our doctoral programs should promote interdisciplinary training. Beyond mastering theories and econometrics, students must be able to work with big data, advanced datamining techniques, and deep-learning methods, while adhering to business operations, working on-site, and collaborating with practitioners in building IT systems with sound economic rationale. Our journals should use simpler review processes that will allow publication of APIs, software packages, short original ideas, and reports of fieldwork with quick iterations. Fast track publication opportunities should go to research that targets pressing issues such as inequality, data ownership, and new disruptive mechanism design. We may also open review processes and engage more researchers in the development stage of large projects.

Research on the economics IT and digitization should take the lead in embracing new research methodologies and answering new questions. After all, next-generation innovation can happen only when we are willing to take such calculated risks.

About the Author

Chong(Alex)Wang is a Professor at the Peking University, Guanghua School of Management. He holds a Ph.D. in Business Administration (Information Systems) from the Hong Kong University of Science and Technology. He got an MSc in Finance from Tsinghua University and a BS in Applied Mathematics from Peking University.

Dr. Wang's research focuses on social and economic impacts of modern information technologies. Specifically, he is interested how information technologies, such as the Internet, blockchain and artificial intelligence shape and enhance social, economic coordination/intelligence. He studies social media production, online social networks, crowdsourcing, emerging financial technologies and online privacy. He has published in journals such as MIS QuarterlyManagement ScienceInformation Systems Research, and Journal of Management Information Systems. He serves as Associate Editor for MIS Quarterly and Information Systems Journal.