Articles

Companies have long used teams to solve problems: focus groups to explore customer needs, consumer surveys to understand the market and annual meetings to listen to shareholders. But the words “solve,” “explore,” “understand” and “listen” have now taken on a whole new meaning. Thanks to recent technologies, including many Web 2.0 applications, companies can now tap into “the collective” on a greater scale than ever before. Indeed, the increasing use of information markets, wikis, crowdsourcing, “the wisdom of crowds” concepts, social networks, collaborative software and other Web-based tools constitutes a paradigm shift in the way that many companies make decisions. Call it the emerging era of “Decisions 2.0.” But the proliferation of such technologies necessitates a framework for understanding what type of collective intelligence is possible (or not), desirable (or not) and affordable (or not) & #8212; and under what conditions. At a minimum, managers need to consider the following key issues: loss of control, diversity versus expertise, engagement, policing, intellectual property and mechanism design. By understanding such important issues, companies like Affinnova, Google, InnoCentive, Marketocracy and Threadless have successfully implemented Decisions 2.0 applications for a variety of purposes, including research and development, market research, customer service and knowledge management. The bottom line is this: For many problems that a company faces, there could well be a solution out there somewhere, far outside of the traditional places that managers might search, within or outside the organization. The trick, though, is to develop the right tool for locating that source and then tap into it.

In his book Remix, Stanford"s Lawrence Lessig argues for a new approach.

Over the past several decades, research has shown how both cognitive biases and small group dynamics can undermine effective decision making in organizations. However, there has been little work on the ways that informal networks impact framing and execution of decisions. In this article, the authors examine the roles decision networks play, both within teams and throughout organizations, in the way decisions are framed and carried out. Although company leaders frequently recognize the importance of such decision networks, they fail to leverage their potential and focus instead on the organization’s formal structure. The authors present two indepth case studies to show how network analysis applied to decision-making interactions within organizations can help improve the effectiveness and efficiency of decision making. The first case demonstrates how a rapidly growing pharmaceuticals company used process mapping and network analysis to streamline decision-making interactions. A team found that decision-making inefficiencies permeated the organization. Decision rights were not clearly delineated or allocated, and even mundane approvals had high collaborative costs. The second case, based on a larger, more established company, shows how network analysis can improve top-team decision making and execution in organizations slowed by bureaucracy and a culture of consensus. The company had sought a technological fix in an effort to rescue itself from organizational gridlock, but the problems persisted. A network analysis helped senior managers identify the underlying network drivers of gridlock, thereby enabling them to take targeted steps to speed up and improve decision making. In both cases, the authors highlight the insights and performance impact that can result when decisions are viewed through a network perspective. Although it is still early, the benefits of understanding how decision-making networks affect the top team appear to be compelling. The number of collaborations required to execute decisions at key points in the network was significantly reduced. This had a positive effect on both company performance and morale.

Historically, neither the creators nor the distributors of cultural products such as books or movies have used analytics & #8212; data, statistics, predictive modeling & #8212; to determine the likely success of their offerings. Instead, companies relied on the brilliance of tastemakers to predict and shape what people would buy. Creative judgment and expertise will always play a vital role in the creation, shaping and marketing of cultural products. But the balance between art and science is shifting. Today companies have unprecedented access to data and sophisticated technology that allows even the best-known experts to weigh factors and consider evidence that was unobtainable just a few years ago. And with increased cost and risk associated with the creation of cultural products, it has never been more important to get these decisions right. In this article, the authors describe the results of a study of prediction and recommendation efforts for a variety of cultural products. They discuss different approaches used to make predictions, the contexts in which these predictions are applied and the barriers to more extensive use, including the problem of decision making pre-creation. They then discuss two aspects of the prediction market. First, the need for better prediction for distributors of cultural products, and second, the potential for business models around prediction techniques.

That is a question that has long puzzled marketers who have heard from customers that they want to do business with ethically based firms & #8212; defined as companies that produce products under conditions of progressive stakeholder relations, advanced environmental practices and respect for human rights. Marketers had no reason to doubt that sentiment, but they have always wondered if consumers would be willing to pay a higher price for ethically produced goods (since they tend to be more expensive to create.) It turns out that a series of controlled experiments proves that consumers will, in fact, pay a premium for ethically produced goods. But perhaps of equal interest is the fact that they will punish (by demanding a lower price) companies that are not seen as ethical. That relationship is not symmetrical. The punishment exacted is greater than the premium customers are willing to pay. How ethical do you have to be? Perhaps not as much as you might think. The research shows that a small degree of ethicalness “pays off.” It is not necessary for a company’s product to be “100% pure” in order to receive a price premium. This research is the first to find that consumers use price to punish unethical companies more than they use price to reward ethical companies, and that the ethicality of a company’s behavior is, indeed, an important consideration for consumers (as demonstrated in their willingness-to-pay decisions).

Companies are on the lookout for new forms of competitive advantage. One emerging possibility: the idea that a company's management model can become a source of advantage.