Date of Award

August 2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Social Sciences

Advisor(s)

Jason Dedrick

Keywords

IT adoption, Organizational learning, Smart grid

Subject Categories

Social and Behavioral Sciences

Abstract

The U.S. electric utility industry is facing a number of challenges today, including aging infrastructure, growing customer demand, CO2 emissions, and increased vulnerability to overloads and outages. Utilities are under greater regulatory, societal and consumer pressure to provide a more reliable and efficient power supply and reduce its carbon footprint. In response, utilities are investing in smart grid technologies. Despite various definitions of smart grid, it is characterized by employing a set of sophisticated sensing, processing and communicating digital technologies to enable a more observable, controllable, and automated power supply.

Yet, the adoption of smart grid technologies presents significant knowledge challenges to electric utilities. This study aims to advance the understanding of IT knowledge challenges in smart grid adoption by focusing on three research questions:

1) What knowledge requirements are critical for smart grid adoption?

2) What knowledge gaps are utilities facing with smart grid adoption? How do utilities vary in the level of knowledge gaps?

3) How do utilities overcome knowledge gaps through learning? How do utilities vary in the learning choices?

This study adopts a qualitative approach using data from 20 utility interviews and secondary information to address the above questions. The analysis indicates four broad areas of knowledge requirements, which are smart grid technology and vendor selection, smart grid deployment and integration, big data, and customer management. The data also reveals several knowledge gaps faced by utilities in these four areas, and confirms that utilities vary in the level of knowledge gaps, which depends on a mix of factors including prior experience, IT sophistication, service territory characteristics, size, ownership form, regulatory support and support from external organizations. The data further indicates several learning practices that are commonly adopted by utilities to overcome the knowledge gaps in smart grid adoption. It is also determined that utilities vary in the configuration of these practices, and the scale and format of many practices. The variance in learning responses is jointly determined by level of knowledge gaps, knowledge relatedness, size, risk-averse culture and top management support.

This study has both research and practical implications. Theoretically, it enriches IT adoption, broader IS research and organizational learning literature in several ways. From the practical perspective, it also has valuable implications for utilities, regulators and other regulated industries and economies.

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Open Access

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