Theoretical Background

This section provides brief introductions to the theories that are most important to the individual research contributions of this thesis. For each theory, an overview of its main constructs is provided.

4.2.1 Wisdom of Crowds

The term Wisdom of Crowds describes the phenomenon in which groups often out-perform experts, even if the individual estimations of the group members are inferior to the expert assessment. The study of this effect has a long history in the sciences. An early study regarding this phenomenon was conducted by Galton (1907), who found that median group estimates can outperform expert opinion. Surowiecki (2005) pro-vides a high-level overview of this area of interest and proposes that the quality of a crowds’ assessment depends on the constructs shown in Figure 3, which shows the five constructs constituting the main themes of his view of the drivers of Crowd Wis-dom.

As shown, these relate to both the composition of the group and the characteristics of its individual members. Within this theoretical framework, diversity is perhaps the most crucial factor influencing a group performance because the overarching idea of diversity underlies the other crowd characteristics outlined in the figure.

Increases in data availability and contexts, in which the effect can be observed, have enabled a resurgence of research in this area. Lorenz et al. (2011) study the negative impact of social influence on group decisions, Nofer and Hinz (2014) assess the per-formance of stock prediction communities, and Chen et al. (2014) examine the value of stock predictions transmitted through social media. In this thesis, Eickhoff and Mun-termann (2015) and the extended version by Eickhoff and MunMun-termann (2016b, paper III.1) investigate ways to make these constructs measurable and compare social media users and stock analysts’ opinion evolution based on per-situation metrics.

Research Background

Figure 3: Wisdom of Crowd Theory (Surowiecki, 2005). Constructs influencing the quality of the average crowd opinion according to WoC theory (left). The role of coordination in arriving at a group consensus (center) and how the quality of this consensus depends on the drivers of crowd wisdom (right).

4.2.2 Decision Making and Information Overload

As discussed, the information value of stock analysts’ recommendations depends on their information processing capability. Likewise, the value readers derive from them constitutes another information processing task, which consists of using the available analyst research, along with other sources of information, to arrive at an investment decision.

However, how do investors arrive at their investment decisions? Simon (1977) de-scribes a general model for decision processes, which can help to structure this ques-tion into distinct phases, making it easier to understand the process. In the context of this thesis, Figure 4 provides an overview of how this process integrates with invest-ment decision making. The upper part of the figure (1) shows some of the information sources available regarding listed companies in the example of data sources used throughout this thesis, which are elaborated in section 4.4. The central part of the figure (2) shows the decision process itself, which consists of surveying the available infor-mation and arriving at a problem statement, creating several potential solutions to the problem, and finally choosing from this pool of potential solutions and acting upon this alternative. Supporting decision makers in overcoming this problem has always been one of the main tasks of information systems. However, as noted by Simon (1976), information systems also contribute to this problem themselves.

Intelligence Phase

Available Information at Time of Decision, e.g.:

Analyst Opinion

Figure 4: Investment Decision Making. Investors assess the available information at any given point in time, as shown in the top part of figure (1). This process can be structured by the three phases of decision making proposed by Simon (1977), as shown in the middle part of figure (2). On this basis, an investment decision can be made (3).

These diverse sources of information can overwhelm the information processing ca-pabilities of decision makers, especially when operating under time constraints, in which case information overload can occur (Pennington and Tuttle, 2007). Due to the ever-increasing volume of digitally available information, the risk of information over-load becomes more relevant as time progresses.

Making investment decisions is made more difficult by the need to assess the quality of the information made available by analysts and other sources of information. In the case of analyst opinion, prior research suggests that stock analysts exhibit several in-efficiencies, which may influence the quality of their analyses in any situation. For example, analysts tend to “stick to the herd” by being careful to voice dissenting opions (Twedt and Rees, 2012). One reason for this behavior is the concern that an in-correct opinion may have a negative impact on the future careers of analysts if the majority of their peers made a correct assessment in the same situation (Clement and Tse, 2005; Hong et al., 2000). Another reason is given by misguided incentive struc-tures, which aim at increasing a firm’s brokerage or investment banking revenue in-stead of rewarding analysts for the accuracy of their predictions (Groysberg et al., 2011). Thus, the research presented in research area III focuses on the properties of different information sources in the context of investment decisions and how under-standing these properties can help decision makers arrive at informed judgements.

4.2.3 Media Richness Theory

Media richness theory, or sometimes the information richness theory, as proposed by Daft and Lengel (1983), analyzes the properties of different media types to determine what media type is suited best for the transmission of a particular type of information or a specific circumstance of the intended transmission (Daft and Lengel, 1983; Daft and Lengel, 1986; Daft and Macintosh, 1981). In its context, richness refers to the overall complexity of the medium regarding its information transmission capabilities.

It argues that information transmission is most effective when the complexity of the transmitted information and the complexity of the medium used to transmit it are aligned. As shown in Figure 5, media richness theory (MRT) uses four constructs to explain this richness:

Figure 5: Media richness theory. Overview of the constructs of media richness theory based on Daft and Lengel (1983).

1. Language variety (symbol variety) refers to the number of different symbol types that a medium can transmit. Symbol types can, as the name suggests, be given by different human languages, but the idea of language variety exceeds this. High language variety also refers to the ability of the medium to transmit a wide spectrum of concepts and ideas. For instance, Daft and Lengel (1983) considers music to be a medium with high language variety.

2. Cue multiplicity (channel variety) considers the number of simultaneous channels a medium uses to transmit information. For example, face-to-face communication involves many different channels such as facial expressions, the spoken text itself, and the posture of all people taking part in the conversa-tion.

3. Personalization concerns the extent to which a medium allows for messages to be customized for specific recipients. For example, a text written for children can be designed to be easier to understand than a technical document.

4. Feedback immediacy is defined by how interactive a medium is. For example, the ability to ask questions by the recipient of the communication or the ability to correct wrong perceptions constitute high feedback immediacy.

A medium is evaluated on the basis of these criteria and is consequently ranked on a low-richness to high-richness spectrum. Obviously, this is not a categorical assignment but rather a judgement call on a continuous scale of media richness. MRT considers two main problems that can inhibit effective communication (Daft and Lengel, 1986):

1. Equivocality refers to a situation of information oversupply, in which a deci-sion maker has access to conflicting sources of information, which make it difficult to discern what information is relevant.

2. Uncertainty refers to a situation in which the decision maker has not been supplied with enough information to reach a decision.

The relationship between these two problems and the media richness property is given by the mitigation of either problem based on the richness of a given media type. Within the scope of MRT, high richness media mitigates uncertainty, while low richness me-dia mitigates equivocality.