• Solutions
  • Cases
  • Resources
  • About
  • Log In

Collective Intelligence: The New Source of Sustainable Competitive Advantage

Collective IntelligenceSat Mar 07 2020Written by Carina A. Hallin5 minute read

Competition among firms is becoming increasingly harsh across all industries. This is predominantly due to an intensity in the ability to adapt to continuous disruptions, resulting from new technologies, pandemics, trade wars, climate changes, corruption, and financial crises to name a few changes that we are experiencing at the outset of the 21st century.

As a result, corporations are on the hunt for new ways to build innovative capabilities that can help them navigate their businesses ahead of events. The implementation and use of collective intelligence (CI) in organizations can help build such innovative capabilities and competitive advantages to solve challenges rapidly through the exchange and cooperation with groups that are both internal and external to organizations.

Collective intelligence may be defined as “the result of groups of individuals acting together in ways that seem intelligent” (Malone, T.W., 2018). It can be understood “as an emergent property from the synergies among 1) data-information-knowledge, 2) software-hardware; and 3) individuals with minimum insights that continually learn from feedback to produce just-in-time knowledge for better decisions than these three elements acting alone” (Glenn, Jerome C., 2009).

The possibility of combining CI with existing corporate perspectives and bring about rapid decision-making and efficient use of internal resources has become a key factor in determining future sustainable development. In other words: “Collective intelligence increases the adaptive capacity for rapid action in the pursuit of emergent and sustainable solutions to the complex problems.” (Lee and Yin, p. 1, 2019).

At the onset of the new decade, corporations are about to reach their limit in terms of their internal capacities and current management methods to deal with the complex changes, and a new source of competitiveness is desperately needed to develop sustainable management practices (Lee and Yin, 2019). Yet, research on identifying cause-and-effect of collective intelligence is still limited and only a few studies have measured the impact of CI on actual firm performance. These few evidential studies are particularly related to crowd prediction studies inside organizations.

Crowd predictions inside organizations can take two approaches. One mechanism is prediction markets and the other is to set up crowd predictions in software platforms without markets.

The success of public prediction markets, such as the Iowa Electronic Markets that trade predictions of presidential elections and sports events have led to considerable interest in running prediction markets inside organizations. The adoption of public markets to private markets has been motivated by the idea that prediction markets can help collect employee information in its ‘purest form’ and avoid biases in the information flow. For example, employees may suppress updated insights from their operational experiences for political and power reasons as they can be punished for sharing bad news about their organizational experiences.

Cowgill and Zitzewitz (2015) demonstrate how employees at Google and Ford Motor company can predict important performance indicators by the use of prediction markets in Google and Ford. Predictions markets work as a stock market, but instead of trading stocks, crowds trade opinions (Wolfers and Zitzewitz, 2004). The study by Cowgill and Zitzewitz demonstrates the effectiveness of such prediction markets inside organizations. The markets outperform other forecasts available to management. Moreover, they find that prediction markets get better with age. In both the Google and Ford cases when employees predicted sales, the initial pricing biases disappeared as the groups became more familiar with the trading of their predictions of sales. These findings are consistent with the fact the more experienced stayed in the market and earned higher returns on their betting, while unskilled traders tended to drop their participation in the market.

An alternative method to prediction markets, to avoid potential group manipulation with prices or unskilled employees in prediction markets are demotivated, is predictions without markets. Such studies have also concentrated on examining the cause-and-effect of collective for firm performance.

For example, Hallin, Andersen, and Tveteraas (2017) show that collective intelligence as a new firm resource measured by frontline employees’ ability to predict changes in essential performance indicators such as team performance, managerial performance, customer satisfaction, innovativeness, and competitiveness are associated with financial firm performance. The study offers a judgmental forecasting aggregation mechanism, an Employee Sensed Operational Capabilities (ESOC), to capture the sensing by frontline employees of operational and dynamic capabilities across three companies in the hospitality sector. In two out of three samples, the ESOC index predicts future firm performance. Therefore, the authors consider the judgmental forecasting time-series as being generally supportive of the hypothesized predictive power of frontline sensing. The study has three implications for businesses:

  • it operationalizes the sensing capacity of frontline employees as important informants,
  • it demonstrates that the sensing of frontline employees can predict firm performance and thus constitutes information on potential strategic value for dealing with complex issues, and
  • it also shows that an ESOC index can provide early performance signals as a new decision support system.

Lee and Yin (2019) study the motivational factors that lead to collective intelligence and understand how these factors relate to each other and to innovation in enterprises. They find that when corporate employees work in an environment where CI is highly developed, work procedures or efficiency is depending on the view of collective intelligence from the beginning of the projects. The results show some indications of the association between CI and incremental innovation. The findings also indicate that if corporations pursue active participation in collective intelligence projects for sharing and collaborating about collective intelligence practices, then from an organizational or corporate management standpoint, the effects of decision-making processes, as well as product and service development efforts will improve.

The above studies provide valuable findings to support the importance of collective intelligence as a new source for building sustainable competitive advantage. As the studies indicate, the direct actions in operations and engagement with external and internal make employees the first to observe subtle changes that will affect how the firm operates and performs. The direct and on-hands daily actions make it possible for employees to sense the effectiveness of the firm's state of performance in many different areas, and thus be able to predict future performance. Consequently, it is just a matter of a short time before firms will take action to strengthen decision-making through the continuous harnessing of collective intelligence in dealing with emergent and complex problems.

References

  • Malone (2018), "Superminds: The Surprising Power of People and Computers Thinking Together."
  • Glenn (2009), "Collective Intelligence – One of the Next Big Things"
  • Cowgill (2015), "Corporate prediction markets: Evidence from Google, Ford and Firm X." pp. 82: 1309−1341.
  • Hallin and Tveteraas (2017), "Harnessing the frontline sensing of capabilities for decision support."
  • Lee and Yin (2019), "How collective intelligence fosters incremental innovation." 5(3), 53.
  • Wolfers and Zitzewitz (2004), "Prediction Markets."

Tap into your organization’s collective intelligence

Sign up to receive news and updates from the forefront of collective intelligence