3  Group decision making and theories of decision making

This chapter examines why and how groups make decisions in extension organisations, the strategies available to extension managers, and the main theories that explain how farmers and organisations actually decide.

3.1 Why group decision making matters in extension

Many of the most consequential decisions in agricultural extension — programme prioritisation, community intervention design, technology assessment — involve multiple stakeholders with diverse interests, knowledge bases and risk tolerances. Group decision-making processes, when well structured, harness this diversity to produce more robust decisions and generate the shared ownership essential for implementation (Leeuwis & Van den Ban, 2004).

3.2 Individual and group decision making

The relative merits of individual and group decision making are compared in Table 3.1.

Table 3.1: Individual and group decision making compared

Factor	Individual decision making	Group decision making
Information	Limited to the individual’s knowledge and experience	Draws on the combined knowledge of all members
Time	Generally faster; no coordination required	Slower; requires negotiation and coordination
Acceptance	May face resistance from those not involved	Greater legitimacy and buy-in from stakeholders who participated
Risk of error	Subject to individual cognitive biases and blind spots	Mutual error-correction; diverse perspectives reduce bias
Best for	Well-defined technical decisions; time-critical situations	Complex, multi-stakeholder decisions requiring broad acceptance

3.3 Decision-making strategies in extension organisations

Extension managers can choose among four broad decision-making strategies, depending on the situation (Table 3.2).

Table 3.2: Decision-making strategies in extension organisations

Strategy	Description	When to use	Extension example
Authoritative	Manager decides alone	Crisis or time-sensitive situations	Emergency response to a pest outbreak
Consultative	Manager decides after seeking input from others	Moderate complexity; stakeholder buy-in needed	Programme priority setting with farmer groups
Collaborative / participatory	Group decides together	Complex, multi-stakeholder situations	Designing a community extension programme
Delegative	Decision handed to a subordinate or farmer group	Routine decisions; capacity-building context	Farmer groups deciding on input procurement

3.4 Groupthink and its prevention

Groupthink occurs when the desire for harmony or conformity in a group overrides realistic appraisal of alternatives (Janis, 1982). Its symptoms include an illusion of invulnerability, collective rationalisation, pressure on dissenters and self-censorship. Prevention strategies include explicitly assigning a ‘devil’s advocate’ role, inviting external perspectives, allowing anonymous idea submission, and requiring each member to critically evaluate proposals before the final meeting.

3.5 Theories of decision making

3.5.1 Rational Choice Theory

Rational Choice Theory (RCT) holds that an actor identifies alternative actions, determines their consequences and chooses the option that optimally satisfies their preferences — specifically, the option with the highest expected utility (Arrow, 1951). Its meta-theoretical foundation is methodological individualism: social facts result from the interaction and aggregation of individual rational actors (Boudon & Betton, 1999). In farming, the primary objective is typically taken to be income maximisation (Edwards-Jones, 2006).

Case study: Organic farming as rational choice

Best (2009) conducted a postal survey of 657 farmers in western Germany to test a rational choice model of organic farming adoption using Subjective Expected Utility (SEU) theory.

Result: Subjectively expected utility explained the adoption decision well. Non-tangible consequences (daily working conditions, identity) were as relevant as financial returns.

Implication: Extension advisers must present technologies in terms of the full range of subjective values that farmers hold, not only economic returns.

3.5.2 Bounded rationality

H. A. Simon (Nobel Prize, 1978) introduced bounded rationality as a departure from RCT. Decision makers are constrained by limited information, limited cognitive processing capacity and limited time. As a result, they tend to satisfice — choosing options that are ‘good enough’ relative to an aspiration threshold — rather than optimise (Simon, 1955). Studies consistently show that bounded rationality provides a more realistic description of actual farmer decision making than perfect rationality (Wens et al., 2020).

Satisficing

The satisficing heuristic combines ‘satisfy’ and ‘suffice’. Rather than pursuing the absolute best option, the decision maker searches sequentially through alternatives and stops when one meets the aspiration threshold. For example, a household evaluating livelihood options stops searching when the first option guarantees at least 1,830 calories per person per day — the minimum food security threshold.

Fast and frugal decision-tree heuristics

Bounded rationality is also expressed through fast and frugal decision trees — simplified, rule-based methods that evaluate specific criteria in a predetermined sequence (Gigerenzer & Selten, 2001). Decision makers consider only selected pieces of information, opting for efficient shortcuts rather than exhaustive analysis. These heuristics are ecologically rational — they perform well because they are adapted to the statistical structure of their natural decision environment.

3.5.3 Theory of Planned Behaviour

The Theory of Planned Behaviour (TPB) proposes that behaviour is determined by behavioural intentions, which are in turn shaped by three components (Ajzen, 1991) (Figure 3.1):

• Attitude: a positive or negative evaluation of performing the behaviour;
• Subjective norms: perceived social pressure from important others; and
• Perceived behavioural control: perception of the ease or difficulty of performing the behaviour.

flowchart LR
  A[Attitude] --> I[Behavioural<br>intention]
  N[Subjective<br>norms] --> I
  C[Perceived<br>behavioural control] --> I
  I --> B[Behaviour]
  C -.-> B

Figure 3.1: Components of the Theory of Planned Behaviour (after Ajzen, 1991).

TPB has been widely applied in agricultural extension research to predict farmers’ adoption intentions for new technologies and conservation practices.

3.5.4 Diffusion of Innovations theory

Rogers (2003) conceptualises technology adoption as a decision-making process unfolding across five stages: knowledge, persuasion, decision, implementation and confirmation. Farmers are classified as innovators, early adopters, early majority, late majority or laggards based on adoption timing. Extension agents must understand the characteristics and information needs of each adopter category to design appropriate communication and advisory strategies.

3.5.5 Theories of decision making compared

The main theories are compared in Table 3.3.

Table 3.3: Theories of decision making and their agricultural applications

Theory	Core assumption	Decision behaviour	Agricultural example
Rational choice (Arrow, 1951)	Full information; utility maximisation	Choose the option with the highest expected utility	Farmer calculates the expected profit from each variety and selects the highest
Bounded rationality (Simon, 1955)	Limited information; satisficing	Choose a ‘good enough’ option meeting an aspiration threshold	Farmer chooses the first variety that meets a minimum yield threshold
Prospect theory (Kahneman & Tversky, 1979)	Loss aversion; reference-point evaluation	Weight losses more heavily than equivalent gains	Resistance to new practices despite expected yield gains
Theory of planned behaviour (Ajzen, 1991)	Intentions driven by attitude, norms and control	Behaviour predicted by intention, which is shaped by social and psychological factors	Extension training targets both farmer attitude and community norms around a new practice

3.6 Chapter Summary

Fill in the blanks

Answers are given at the end of the chapter.

1. The decision-making strategy in which the manager decides alone is called __________.
2. Groupthink was described by __________.
3. Assigning a devil’s __________ role is a strategy to prevent groupthink.
4. The meta-theoretical foundation of Rational Choice Theory is methodological __________.
5. Choosing an option that is ‘good enough’ rather than the best is called __________.
6. In the Theory of Planned Behaviour, perceived social pressure from important others is called subjective __________.
7. Rogers’ innovation-decision process has __________ stages.

Short-answer questions

1. Compare individual and group decision making.
2. Describe the four decision-making strategies available to extension managers, with an example of each.
3. What is groupthink? How can it be prevented in extension organisations?
4. Explain bounded rationality and satisficing with an agricultural example.
5. Explain the three components of the Theory of Planned Behaviour.
6. What lesson does Best’s (2009) study of organic farming hold for extension advisers?

Quick revision

• Group decisions → more information and buy-in; slower; better for complex, multi-stakeholder issues.
• Strategies → authoritative, consultative, collaborative/participatory, delegative.
• Groupthink (Janis) → conformity overrides appraisal; prevent with devil’s advocate, outsiders, anonymity.
• Rational choice → highest expected utility; methodological individualism.
• Bounded rationality (Simon) → satisficing; fast and frugal heuristics (Gigerenzer).
• TPB (Ajzen) → attitude + subjective norms + perceived behavioural control → intention → behaviour.
• Diffusion (Rogers) → knowledge, persuasion, decision, implementation, confirmation; five adopter categories.

Answers to fill in the blanks

1. authoritative 2. Janis 3. advocate 4. individualism 5. satisficing 6. norms 7. five

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Arrow, K. J. (1951). Social choice and individual values. Wiley.

Best, H. (2009). Organic farming as rational choice: Empirical investigations in environmental decision making. Rationality and Society, 21(2), 197–224.

Boudon, R., & Betton, E. (1999). Explaining the feelings of justice. Ethical Theory and Moral Practice, 2(4), 365–398. https://doi.org/10.1023/A:1009918510993

Edwards-Jones, G. (2006). Modelling farmer decision-making: Concepts, progress and challenges. Animal Science, 82(6), 783–790.

Gigerenzer, G., & Selten, R. (Eds.). (2001). Bounded rationality: The adaptive toolbox. MIT Press.

Janis, I. L. (1982). Groupthink: Psychological studies of policy decisions and fiascoes (2nd ed.). Houghton Mifflin.

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185

Leeuwis, C., & Van den Ban, A. W. (2004). Communication for rural innovation: Rethinking agricultural extension (3rd ed.). Blackwell Science.

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852

Wens, M. L. K., Veldkamp, T. I. E., Mwangi, M., Johnson, J. M., Lasage, R., Haer, T., & Aerts, J. C. J. H. (2020). Simulating small-scale agricultural adaptation decisions in response to drought risk: An empirical agent-based model for semi-arid Kenya. Frontiers in Water, 2, 15. https://doi.org/10.3389/frwa.2020.00015