Practical implications

The results of this study provide several practical implications for utility companies, regulators and other regulated economies.

Utilities

This research has significant implications for utilities that have adopted smart grid or plan to adopt smart grid. The results demonstrate that whereas external factors such as regulatory attitude and uncontrollable factors such as knowledge relatedness, size and service territory characteristics are key factors shaping level of knowledge gaps in smart grid adoption, internal organizational capabilities also influence the knowledge gaps. As evidenced in this study, utilities with prior communication and IT experience have lower gaps and smaller challenges in smart grid adoption. Therefore, utilities should be more active in incorporating IT investment in its R&D efforts to lower knowledge barriers for future technology adoption or upgrades, as this is the trend for future technology.

When it comes to learning, this study shows that top management support and level of resources play crucial roles in learning. The findings illustrate the importance of top management support in knowledge areas with great uncertainty and risks. In a few cases, top management support drives utilities to make dedicated learning efforts regardless of big knowledge gaps, as opposed to the majority of utilities that make limited learning efforts in the same knowledge areas. However, a sound and forward-looking top management requires an organizational culture that

encourages innovation and accommodate mistakes, and such culture is missing in the majority of utilities due to long-term regulation. This calls for managerial attention to create an innovative culture that is beneficial to utilities in the long run. Particularly for managers that practice more conservative learning in big data and customer engagement, they should be more proactive in the learning processes, even if they move more slowly in these two areas.

Managers should also factor in their level of resources when making decisions on learning choices- they need to consider how to allocate the human resources and time to improve the effectiveness of learning. While bigger utilities have more resources to support bigger scale of learning, it doesn’t suggest that utilities with smaller size would have bad learning outcomes due their limited budget and human resources. As indicated in this study, smaller utilities could embrace more flexible and less formal learning in training, sharing and learning by doing to align with their resource conditions.

Regulators

The findings indicate that regulators can play a significant role in lowering knowledge gaps in smart grid adoption. As evidenced in the results, a few state regulators are very proactive in smart grid—not only do they collaborate with standards organizations in the smart grid standards development to ease the interoperability issue but they also push regulated utilities to participate in the process. One state regulator also initiated an effort to establish a smart meter portal to better manage the data. This offers some encouraging evidence that regulatory authority should develop a positive attitude towards and be actively involved in the smart grid adoption to support utilities.

Furthermore, given the fact that most utilities make limited learning efforts in big data and customer engagement as a result of long-term regulation and associated risk-averse culture, there

is the practical implication that state regulators should create an environment that encourages innovation and exploration among utilities. Like many regulated industries, electric utility companies for a long time have operated in a relatively predictable and slowly changing technology environment, having no incentive to take advantage of technological advances (Energy Information Administration, 2000). Not surprising, compared to other firms in other industries (e.g. IT companies), utilities are widely recognized as risk-averse and lacking innovation. Clearly, a regulatory environment that allows mistakes would help lower the entrenched risk-averse state of mind and promote a more proactive attitude in smart grid

learning. Yet, the regulators should think about what are the mechanisms to bolster an open and innovative environment for utilities. The existing regulation model is a “cost-plus” model in which revenues are based on the utility’s total costs of providing service and utilities are guaranteed a percentage return on any approved investments (Energy Information

Administration, 2000). This is where the risk-averse culture and lack of innovation is rooted. An alternative regulation is called performance-based regulation that emphasizes incentives for good performance (Lazar, 2014). There has been heated discussion around this new model, but only a few states in U.S. have adopted it (Lowry, Woolf, & Schwartz, 2016). Yet, it could be the future model and deserves regulators’ attention.

Other industries or economies

The aforementioned implications also apply to other regulated industries or other economies that are adopting complex information and communication technologies. As IT is incorporated in every industry, many organizations face a range of new landscape-changing IT initiatives and struggle to implement and use them efficiently and effectively. Sometimes challenges arise because an organization simply does not have the knowledge required, and they need to learn to

acquire the knowledge. However, both firms and regulators could make efforts to smooth the learning. On one hand, firms could increase their R&D budget to support more IT investment to lower the knowledge gaps for potential technology upgrades, and develop an innovative culture to encourage and stimulate learning. The format and scale of learning should also be aligned with their resource level to achieve maximum efficiency. On the other hand, regulators should play a supportive role in complex IT adoption to lower the knowledge gaps and create an environment that rewards innovation among regulated firms to promote learning.