Introduction

Strategic decisions shape the allocation of resources, the configuration of organizational capabilities, and the long-term competitive position of firms. Because such decisions commit substantial resources and generate consequential organizational actions, their process characteristics have long occupied a central place in strategic management research (Rajagopalan, Rasheed, & Datta, 1993; Dean & Sharfman, 1996; Papadakis, Lioukas, & Chambers, 1998). Among these characteristics, decision speed has received particular attention. In competitive and uncertain environments, firms that decide quickly may exploit transient opportunities, respond earlier to technological or market shifts, and reduce the costs associated with organizational inertia (Bourgeois & Eisenhardt, 1988; Eisenhardt, 1989; Baum & Wally, 2003). Yet speed alone may not be sufficient. Strategic decisions must also be based on adequate information, coherent analysis, consideration of alternatives, and alignment with organizational objectives. This raises a central issue in strategic decision-making research: do fast decisions improve organizational performance because they enable timely action, or do they undermine performance when they sacrifice decision quality?

Prior research provides important but incomplete answers to this question. Eisenhardt’s (1989) inductive study of high-velocity environments showed that fast decision makers were not necessarily superficial decision makers; rather, they often used more real-time information, more alternatives, and more active conflict resolution than slower decision makers. Judge & Miller (1991) further suggested that the performance consequences of decision speed depend on environmental conditions. Baum & Wally (2003), using a four-year study of 318 CEOs, found that strategic decision speed predicted subsequent firm growth and profitability and mediated the relationship between environmental and organizational characteristics and firm performance. These findings support the view that speed can be a source of strategic advantage when it enables firms to act before opportunities disappear.

However, another stream of research emphasizes the quality, rationality, and comprehensiveness of strategic decision processes. Fredrickson & Mitchell (1984) examined the relationship between comprehensiveness and performance, while Dean and Sharfman (1996) showed that procedural rationality can enhance strategic decision effectiveness. Priem et a. (1995) also demonstrated that the value of rational decision processes varies with environmental dynamism. More recently, Forbes (2007) argued that prior studies often conflate different dimensions of uncertainty and decision quality, thereby obscuring the conditions under which comprehensive decision-making improves performance. Taken together, these studies suggest that the relationship between speed, quality, and performance is neither universal nor linear. Rather, it depends on the interaction between decision processes, organizational structures, and environmental conditions.

This tension between speed and quality is particularly relevant in emerging economy contexts. Firms and public organizations operating in such environments often face resource constraints, infrastructural weaknesses, regulatory complexity, and unpredictable market conditions. These constraints intensify the need for rapid decisions while also increasing the risks associated with poorly informed choices. Cameroon provides a particularly relevant setting for examining this issue. The country’s development agenda, articulated through Vision 2035 and the National Development Strategy 2020–2030, places strong emphasis on industrialization, structural transformation, private sector development, and institutional modernization. At the same time, recent diagnostic reports highlight persistent constraints related to the business environment, infrastructure, governance, and private sector competitiveness. These conditions create an empirical context in which organizational leaders must frequently decide under pressure, with incomplete information and uneven institutional support.

Despite the importance of this context, the strategic decision-making literature remains largely grounded in studies conducted in North American, European, or high-technology settings. Much less is known about how decision speed and decision quality jointly affect organizational performance in African economies. Existing studies often examine speed or quality separately, treat environmental context as a background condition, or focus on a single sector. As a result, we know relatively little about whether the benefits of fast decision-making depend on the quality of the decision process, whether high-quality decisions compensate for slower action, or whether the speed–quality relationship varies across organizational sectors within the same national environment.

This study addresses this gap by examining the effect of decision speed and decision quality on organizational performance in Cameroon. We define decision speed as the time required for an organization to move from problem recognition or opportunity identification to commitment to a strategic course of action. We define decision quality as the extent to which decisions are informed, analytically grounded, coherent with organizational objectives, and supported by relevant stakeholder input. Organizational performance is approached as a multidimensional construct, including financial, operational, and market-related outcomes. By adopting a cross-sectional comparative survey design, the study investigates whether decision speed and decision quality exert independent, complementary, or contingent effects on organizational performance across different sectors.

The study is guided by the following research question: How do decision speed and decision quality jointly and comparatively affect the performance of organizations in Cameroon? More specifically, it asks whether decision speed improves performance independently of decision quality, whether decision quality strengthens or weakens the speed–performance relationship, and whether this relationship varies across organizational contexts. In addressing these questions, the study draws on strategic decision process theory, bounded rationality, and contingency perspectives. The core argument is that organizational performance does not depend simply on deciding fast or deciding carefully, but on achieving a strategic fit between decision speed, decision quality, and the environmental conditions under which organizations operate.

This research makes three contributions. First, it contributes to strategic decision-making theory by integrating two process dimensions that are often examined separately: speed and quality. Rather than treating them as opposing attributes, the study examines how they combine to influence organizational outcomes. Second, it extends the empirical scope of strategic management research by focusing on Cameroon, an underexplored African context characterized by institutional complexity and developmental transformation. Third, it offers practical implications for managers and public decision makers by identifying when rapid decisions enhance performance, when they may become risky, and how decision quality can improve the effectiveness of organizational action. In doing so, the study aims to move beyond generic prescriptions about “fast” or “rational” decision-making and to develop a more context-sensitive understanding of strategic decision effectiveness.

1. Literature Review

1.1. Strategic Decision-Making as a Bounded and Contextual Process

Strategic decision-making constitutes one of the central mechanisms through which organizations interpret environmental signals, allocate resources, and commit themselves to courses of action that shape future performance. Classical models of decision-making assumed that managers could identify problems, collect relevant information, generate alternatives, evaluate consequences, and select optimal solutions. However, this rational-comprehensive view was progressively challenged by the behavioral theory of decision-making. Simon (1947, 1955, 1957) introduced the concept of bounded rationality, arguing that decision-makers operate under cognitive limits, incomplete information, time constraints, and uncertain consequences. Under such conditions, managers rarely optimize; instead, they satisfice by selecting solutions that appear acceptable within existing constraints.

This behavioral perspective was extended by Cyert & March (1963), who conceptualized the firm as a coalition of actors with divergent goals, routines, aspiration levels, and search processes. Strategic decisions are therefore not only analytical choices; they are also shaped by organizational routines, political interactions, resource constraints, and managerial interpretation. Subsequent work in strategy process research confirmed that strategic decisions are influenced by environmental conditions, organizational structures, top management team characteristics, and decision-specific attributes (Rajagopalan et al., 1993; Papadakis et al., 1998; Hutzschenreuter & Kleindienst, 2006; Shepherd & Rudd, 2014). Hutzschenreuter & Kleindienst (2006), for example, show that strategy-process research increasingly links antecedents, process characteristics, and outcomes rather than treating strategic choice as a purely rational event.

From this perspective, strategic decision-making must be understood as a process embedded in context. Elbanna (2006) identifies three major process perspectives: procedural rationality, intuitive synthesis, and political behavior. Procedural rationality refers to systematic information search, explicit analysis of alternatives, and disciplined evaluation. Intuitive synthesis refers to experience-based judgment and rapid pattern recognition. Political behavior refers to bargaining, coalition-building, and power dynamics. These perspectives are not mutually exclusive. They suggest that decision effectiveness depends on how organizations combine analysis, intuition, participation, authority, and implementation capacity.

This theoretical evolution is important for the present study. It implies that decision speed and decision quality should not be treated as isolated managerial attributes. Rather, they are process characteristics shaped by environmental uncertainty, organizational routines, available information, and institutional conditions. Decision speed captures the time required to move from problem recognition or opportunity identification to commitment to action. Decision quality captures the extent to which a decision is informed, coherent, feasible, analytically grounded, and aligned with organizational objectives.

1.2. Decision Speed, Decision Quality, and Organizational Performance

Decision speed has become a major construct in strategic management because firms increasingly operate in environments where opportunities are temporary and information becomes rapidly obsolete. Bourgeois & Eisenhardt (1988) showed that high-velocity environments create pressure for rapid strategic action. Eisenhardt (1989) later demonstrated that fast strategic decision-makers are not necessarily less analytical. In her study of microcomputer firms, the fastest decision-makers used more real-time information, considered more alternatives, and relied on active conflict resolution. This finding challenged the traditional assumption that speed necessarily reduces decision quality.

Judge & Miller (1991) refined this argument by showing that decision speed does not have a universal performance effect. Its value depends on environmental conditions. In their comparative study, speed was more strongly associated with performance in high-velocity contexts than in more stable sectors. Baum & Wally (2003) provided one of the most influential empirical tests of this relationship. Their four-year study of 318 CEOs showed that strategic decision speed predicted subsequent firm growth and profitability and mediated the relationship between environmental and organizational characteristics and performance.

However, speed may also create risks. Perlow et al. (2002) introduced the concept of the “speed trap” to describe situations in which the organizational pursuit of ever-faster decisions becomes self-reinforcing and dysfunctional. When decision-makers overvalue speed, they may ignore weak signals, reduce analytical depth, overload teams, and commit prematurely to poorly understood actions. Speed can therefore improve performance when it supports timely action, but it can undermine performance when it weakens interpretation, coordination, and implementation.

Decision quality offers the complementary side of this debate. Fredrickson & Mitchell (1984) studied strategic decision comprehensiveness and showed that the value of extensive analysis depends on environmental conditions. Dean & Sharfman (1996) found that procedural rationality improves strategic decision effectiveness, while political behavior may reduce it. Priem et al. (1995) also showed that rationality in strategic decision processes can improve performance when environmental dynamism is taken into account. These studies suggest that decision quality matters because strategic decisions require relevant information, credible analysis, and implementable choices.

Yet high-quality decisions are not necessarily slow decisions. This view is consistent with Dane & Pratt (2007), who argue that intuition may operate as a rapid, holistic, and experience-based form of judgment rather than as a merely irrational shortcut. A decision may therefore be rapid and high quality when it is based on relevant information, experienced judgment, clear authority, and organizational routines that accelerate interpretation. Conversely, a slow decision may be low quality if it accumulates irrelevant data, avoids commitment, or reflects political compromise rather than strategic coherence. The central issue is therefore not whether organizations should decide fast or carefully, but how they can combine speed and quality in ways that improve performance.

1.3. Environmental and Organizational Contingencies

The relationship between decision speed, decision quality, and performance depends strongly on context. Environmental dynamism and munificence are particularly important. Environmental dynamism refers to the instability and unpredictability of markets, technologies, competitors, and regulations. In dynamic environments, delays may be costly because information becomes obsolete and opportunities disappear quickly. Environmental munificence refers to the availability of resources and growth opportunities. In munificent environments, organizations may possess greater slack, more experimentation capacity, and stronger ability to recover from decision errors.

Dess & Beard (1984) provided a foundational conceptualization of task environments by distinguishing dimensions such as dynamism, munificence, and complexity. Baum & Wally (2003) later integrated dynamism and munificence into a model linking environmental conditions, organizational structure, decision speed, and firm performance. Their findings suggest that decision speed is not merely an individual managerial preference; it is partly shaped by environmental and organizational conditions.

More recent work reinforces this contingency perspective. Shepherd et al. (2021) examine the relationship between strategic decision speed and decision quality across multiple environmental contexts. They show that environmental munificence plays a critical moderating role. In resource-rich environments, speed may support quality because organizations have greater access to information, slack, and recovery capacity. In low-munificence environments, rapid decisions may become riskier because mistakes are more costly and less easily reversible.

Organizational structure also matters. Centralized strategic authority may accelerate commitment by reducing negotiation and ambiguity. However, operational decentralization may improve decision quality by incorporating frontline information and increasing implementation commitment. Formalized routines can stabilize information flows, while informal non-routine processes may support creativity, intuition, and adaptation. This logic is consistent with Baum & Wally (2003), who found that centralization, decentralization, formalization, and informalization influence decision speed and, through it, firm performance.

These arguments are particularly relevant for organizations in Cameroon. Many firms and public institutions operate under resource constraints, infrastructural limitations, administrative delays, and institutional complexity. These conditions increase the need for fast decisions but also raise the cost of poor-quality choices. The performance effect of decision speed may therefore depend on whether organizations possess the structures, routines, and information channels needed to preserve decision quality under pressure.

1.4. Dynamic Capabilities, Learning, and Technology-Enabled Decision Processes

Dynamic capabilities theory provides a robust lens for explaining how organizations may reconcile decision speed and decision quality under changing environmental conditions. Teece et al. (1997) define dynamic capabilities as the firm’s ability to integrate, build, and reconfigure internal and external competences in response to rapidly changing environments. Eisenhardt & Martin (2000) further argue that dynamic capabilities are not vague organizational attributes, but identifiable processes such as strategic decision-making, product development, alliance formation, and resource reconfiguration. From this perspective, fast and high-quality decisions depend on the organization’s capacity to sense relevant changes, seize emerging opportunities, and reconfigure resources before competitors do so. Speed is therefore associated with timely sensing and rapid commitment, while quality depends on interpretation, coherence, and implementation feasibility.

Organizational learning extends this argument by explaining how firms develop decision routines that allow them to act quickly without necessarily reducing analytical quality. Levitt & March (1988) conceptualize organizational learning as routine-based, history-dependent, and target-oriented. March (1991) later shows that organizations must balance exploitation of existing knowledge with exploration of new possibilities. This distinction is crucial for decision-making. Excessive exploitation may accelerate decisions but generate rigidity, while excessive exploration may improve analysis but delay commitment. Zollo & Winter (2002) add that dynamic capabilities evolve through experience accumulation, knowledge articulation, and knowledge codification. These mechanisms help organizations transform repeated decision experiences into more reliable routines, thereby reducing cognitive load and improving both speed and quality.

However, decision routines do not always improve performance. Gavetti & Levinthal (2000) argue that organizations rely not only on experiential learning, but also on cognitive representations that shape how managers imagine possible futures. This implies that decision quality depends on both past experience and forward-looking reasoning. When routines are adaptive, they help managers interpret weak signals and decide rapidly. When routines become rigid, they may produce path dependence, premature closure, or misinterpretation of novel situations. Thus, organizational learning contributes to decision effectiveness only when firms maintain a balance between accumulated experience, analytical reflection, and openness to environmental feedback.

The growing use of digital technologies has renewed this debate. Business intelligence, big data analytics, and artificial intelligence may increase decision speed by improving data access, reducing information-processing delays, and supporting predictive analysis. Brynjolfsson & McElheran (2016) show that data-driven decision-making has spread rapidly in manufacturing and is associated with structured management practices. Ghasemaghaei & Calic (2020) demonstrate that big data characteristics may influence innovation performance, but also caution that more data does not automatically mean better performance. Shrestha et al. (2019) further show that AI-based decision-making differs from human decision-making in terms of search space, interpretability, number of alternatives, decision speed, and replicability. These studies suggest that digital technologies can strengthen the speed–quality relationship only when organizations possess reliable data, analytical capabilities, interpretive competence, and appropriate governance mechanisms.

This argument is particularly important for emerging economies such as Cameroon. In developed contexts, advanced analytics may help organizations reduce the tension between rapid action and analytical depth. In Cameroon, however, digital infrastructure, data reliability, analytical skills, and managerial routines vary significantly across sectors. As a result, technology-enabled decision-making cannot be assumed to automatically improve either speed or quality. Its effect depends on the organization’s dynamic capabilities, learning routines, digital maturity, and institutional environment. The present study therefore examines whether Cameroonian organizations can transform information, experience, and technology into decision processes that are both timely and strategically sound.

1.5. Emerging Economy Context, Cameroon, and Research Gap

Most research on strategic decision-making has been conducted in developed economies, high-technology industries, or large formal organizations. This creates an important boundary condition problem. Theories developed in contexts characterized by stable institutions, strong infrastructures, reliable data systems, and deep capital markets may not fully explain decision-making in emerging economies. Bruton et al. (2010) show that institutional theory is essential for understanding strategic and entrepreneurial behavior in such contexts, because institutions shape opportunities, constraints, legitimacy, and resource access.

Emerging economies are often characterized by institutional voids, resource scarcity, weaker formal infrastructures, and stronger reliance on informal networks. These conditions may alter both the speed and quality of strategic decisions. In such environments, managers may rely not only on formal analysis, but also on relational knowledge, experience, social networks, and adaptive judgment. As a result, decision-making may combine formal procedures and informal coordination mechanisms.

Cameroon offers a theoretically relevant context for examining these issues. Its organizations operate across diverse sectors, including agriculture, logistics, telecommunications, healthcare, higher education, public administration, SMEs, large domestic firms, and multinational subsidiaries. These organizations face different levels of environmental dynamism, resource availability, bureaucratic constraint, technological readiness, and stakeholder pressure. This diversity makes Cameroon a useful setting for testing whether established theories of strategic decision-making hold under conditions of institutional complexity and resource constraint.

The literature reveals three main gaps. First, decision speed and decision quality are often examined separately, although their performance effects are likely to be interdependent. Second, African contexts remain underrepresented in strategic decision-making research, despite their theoretical relevance for understanding decision-making under institutional and resource constraints. Third, few studies compare the speed–quality–performance relationship across sectors within the same national context.

This study addresses these gaps by examining how decision speed and decision quality jointly affect organizational performance in Cameroon. It extends Baum & Wally (2003) by integrating decision quality more explicitly into the decision speed–performance relationship. It also extends Fredrickson & Mitchell (1984), Dean & Sharfman (1996), and Shepherd et al. (2021) by testing whether the performance value of decision speed depends on decision quality and environmental conditions in an emerging African economy. The guiding research question is therefore: How do decision speed and decision quality jointly and comparatively affect organizational performance across different sectors in Cameroon?

2. Methodology

2.1. Research Design and Empirical Setting

This study adopts a cross-sectional comparative survey design to examine how decision-making speed and decision-making quality affect organizational performance in Cameroon. This design is appropriate because the research objective is to compare decision-making patterns across heterogeneous organizations operating within the same national environment. It also enables the study to test theoretically specified relationships among latent constructs while capturing variation across organizational size, legal status, geographical location, and decision-making style.

The empirical setting is Cameroon, an emerging African economy characterized by institutional complexity, infrastructural constraints, regulatory pressures, and increasing technological adoption. These conditions provide a relevant context for examining strategic decision-making because organizations must often make decisions under uncertainty, time pressure, and resource constraints. In such environments, the tension between rapid decision-making and high-quality decision-making becomes particularly salient.

The study is positioned within strategic decision process research, which argues that decision outcomes depend not only on the content of strategic choices, but also on the process through which these choices are made (Rajagopalan et al., 1993; Papadakis et al., 1998; Shepherd & Rudd, 2014). It also draws on prior work linking decision speed to firm performance (Eisenhardt, 1989; Judge & Miller, 1991; Baum & Wally, 2003), and on research emphasizing decision quality, procedural rationality, and decision effectiveness (Fredrickson & Mitchell, 1984; Dean & Sharfman, 1996; Priem et al., 1995).

2.2. Sample and Data Collection

Data were collected from 297 respondents working in organizations located in different regions of Cameroon. The sample includes respondents from private companies, public institutions, non-profit organizations, sole proprietorships, partnerships, cooperatives, and branches of foreign companies. This diversity supports the comparative ambition of the study by allowing the analysis to capture multiple organizational configurations within a single institutional environment.

The survey instrument was administered to respondents capable of providing informed assessments of organizational decision-making practices and performance. The questionnaire included items measuring decision speed, decision quality, environmental and technological context, and operational performance. Respondents also provided demographic and organizational information, including sex, age, education, region, company size, number of branches, legal status, and dominant decision-making style.

Because the available data do not indicate a national probability sampling frame, the sample should be interpreted as a heterogeneous cross-sectional organizational sample rather than a statistically representative sample of all Cameroonian organizations. This limitation is consistent with many strategy-process studies conducted in emerging economy contexts, where formal sampling frames are often incomplete or difficult to access. Nevertheless, the diversity of the sample increases the analytical relevance of the study and permits meaningful comparison across organizational categories.

Table 1. Sample profile

Source: Authors’ survey data.

The sample shows a relatively balanced gender distribution, with 52% female and 48% male respondents. Respondents aged 32 years and above represent the largest group, suggesting that the data include a significant proportion of experienced individuals. The educational profile is also relatively strong, with 56% of respondents holding advanced or higher qualifications. From an organizational perspective, the sample includes small, medium-sized, and large organizations, which allows the study to capture variation in decision-making complexity, formalization, and resource availability. The predominance of private companies and public institutions is analytically useful because these two categories are likely to differ in decision authority, procedural constraints, and performance priorities.

2.3. Measures

The questionnaire measured four main latent constructs: decision-making speed, decision-making quality, environmental and technological context, and operational performance. Items were measured using multi-item scales. The use of multi-item constructs is appropriate because decision speed, decision quality, context, and performance are multidimensional phenomena that cannot be captured reliably through single indicators.

Decision-making speed refers to the perceived rapidity with which an organization identifies a decision issue, evaluates options, and commits to action. This construct is grounded in prior work on strategic decision speed and firm performance (Eisenhardt, 1989; Judge & Miller, 1991; Baum & Wally, 2003). Decision-making quality refers to the perceived rigor, relevance, coherence, and effectiveness of organizational decisions. This construct draws on research on procedural rationality, decision comprehensiveness, and decision effectiveness (Fredrickson & Mitchell, 1984; Dean & Sharfman, 1996; Priem et al., 1995). Environmental and technological context captures perceived market velocity, regulatory conditions, socio-cultural influences, technology adoption, and technological challenges. Operational performance is measured as a multidimensional construct including financial performance, customer satisfaction, employee productivity, innovation, adaptability, and decision effectiveness, consistent with the multidimensional view of organizational performance proposed by Venkatraman & Ramanujam (1986) and Richard et al. (2009).

Table 2. Constructs, conceptual definitions, indicators, and theoretical sources

Source: Authors’ construction based on the literature and survey items.

The retained measurement approach is consistent with prior strategy research, where latent constructs are usually operationalized through multiple indicators to capture both conceptual breadth and empirical reliability. The constructs are specified as reflective because the indicators are treated as observable manifestations of underlying latent variables. This specification is consistent with the subsequent use of reliability coefficients, indicator loadings, composite reliability, average variance extracted, and discriminant validity tests.

2.4. Control Variables and Bias Considerations

Several demographic and organizational variables were collected to contextualize the analysis and support robustness checks. These include respondent sex, age, educational level, region, company size, number of branches, legal status, and dominant decision-making style. These variables are theoretically relevant because prior research shows that organizational size, structure, managerial background, and decision authority can influence decision processes and performance outcomes (Hambrick & Mason, 1984; Papadakis et al., 1998; Baum & Wally, 2003).

Company size is particularly important because larger organizations may possess more formalized procedures, greater resources, and more complex decision structures. The number of branches captures organizational complexity and coordination requirements. Legal status distinguishes private, public, non-profit, cooperative, and foreign-linked organizations, which may differ in autonomy, regulatory exposure, and performance objectives. Decision-making style captures whether decisions are mainly autocratic, participative, democratic, directive, conceptual, analytical, or behavioral. This variable is important because centralized and participative decision processes may have different implications for speed and quality.

Several potential biases were considered. First, because the study uses self-reported survey data, common method bias may arise. To reduce this risk, the questionnaire separated items across constructs and included both process-oriented and outcome-oriented measures. Second, the sample is cross-sectional, which limits causal inference. The study therefore interprets structural paths as theoretically grounded associations rather than definitive causal effects. Third, regional representation is uneven, with stronger representation from some regions than others. This requires caution when generalizing the findings to the entire national population of organizations.

2.5. Data Analysis Strategy

The empirical analysis proceeds in three stages. First, descriptive statistics are used to profile respondents and organizations and to summarize the distribution of the main variables. Means, standard deviations, frequencies, percentages, and one-sample t-tests are used to evaluate whether observed responses differ meaningfully from the neutral midpoint of the measurement scales. This stage provides a preliminary understanding of decision speed, decision quality, environmental and technological conditions, and operational performance.

Second, the measurement model is assessed to verify whether the retained indicators reliably measure their corresponding latent constructs. Internal consistency reliability is examined through Cronbach’s alpha, composite reliability rho_a, and composite reliability rho_c. Indicator reliability is assessed through outer loadings. Convergent validity is evaluated using average variance extracted. Discriminant validity is assessed using the Fornell & Larcker (1981) criterion, cross-loadings, and the heterotrait-monotrait ratio proposed by Henseler et al. (2015). These tests are necessary before estimating the structural model because unreliable or poorly differentiated constructs may distort the interpretation of structural relationships.

Third, the structural model is estimated using Partial Least Squares Structural Equation Modeling. PLS-SEM is appropriate for this study for three reasons. First, the research examines multiple latent constructs and mediation pathways. Second, the study is prediction-oriented and seeks to explain operational performance through decision-making and contextual variables. Third, the latent variable distributions show departures from normality, making PLS-SEM preferable to covariance-based SEM in this context. The use of PLS-SEM follows established methodological recommendations in management research (Chin, 1998; Hair et al., 2019; Hair et al., 2022).

The structural analysis examines direct effects, indirect effects, total effects, and mediation effects. Specifically, the model tests whether decision-making speed and decision-making quality influence operational performance directly and indirectly through environmental and technological context. Bootstrapping is used to assess the significance of path coefficients and mediation effects. Model assessment relies on path coefficients, standard errors, t-statistics, p-values, bias-corrected confidence intervals, R², adjusted R², Q²predict, RMSE, and MAE. This combination of explanatory and predictive criteria strengthens the empirical rigor of the analysis and aligns the study with contemporary standards in strategy and organizational research.

The analytical model can be summarized as follows:

Figure 1. Analytical Model of Decision-Making Processes, Environmental and Technological Context, and Operational Performance (figure — voir PDF)

Source: Authors

The analytical model developed in this study examines how decision-making speed and decision-making quality influence operational performance, both directly and indirectly through the environmental and technological context. The model is grounded in the strategic decision-making literature, which suggests that organizational outcomes depend not only on the content of decisions, but also on the process through which decisions are made (Dean & Sharfman, 1996; Baum & Wally, 2003). In this model, decision-making speed refers to the organization’s capacity to identify issues, process information, and commit rapidly to action. Fast decisions may improve performance by enabling organizations to respond quickly to market changes, technological disruptions, regulatory pressures, and competitive threats. However, speed alone may be insufficient if it is not supported by relevant information, coordination, and contextual awareness.

Decision-making quality captures the extent to which decisions are informed, coherent, analytically grounded, and aligned with organizational objectives. High-quality decisions are expected to enhance performance because they reduce strategic errors, improve resource allocation, and increase the likelihood of effective implementation. Compared with speed, quality may provide a more stable basis for performance when organizations face uncertainty and complexity. The environmental and technological context occupies a mediating position in the model. It represents the organization’s ability to interpret market conditions, adapt to regulatory and socio-cultural pressures, and integrate technological tools into decision processes. Drawing on the dynamic capabilities perspective, this context reflects the organization’s capacity to sense environmental changes, seize opportunities, and reconfigure resources in response to external demands (Teece et al., 1997).

Accordingly, the model proposes two indirect relationships. First, decision-making speed may improve operational performance by strengthening the organization’s responsiveness to environmental and technological conditions. Second, decision-making quality may enhance performance by improving the organization’s capacity to interpret and adapt to its context. In addition to these mediated relationships, the model also estimates the direct effects of decision-making speed and decision-making quality on operational performance. This structure allows the study to determine whether the environmental and technological context partially mediates the relationship between decision-making processes and organizational performance. In other words, the model tests whether speed and quality affect performance directly, or whether their effects are partly transmitted through the organization’s capacity to adapt to environmental and technological conditions.

3. Findings

3.1. Preliminary Descriptive Evidence and Construct Correlations

Before testing the structural relationships, we examined the descriptive properties of the latent constructs included in the model: decision-making speed, decision-making quality, environmental and technological context, and operational performance. The results show that all constructs have mean values slightly above the scale midpoint, suggesting moderately positive perceptions of organizational decision processes and performance among respondents.

Decision-making speed records a mean of 3.218, while decision-making quality records a mean of 3.140. Environmental and technological context has the highest mean value, 3.311, followed closely by operational performance, 3.302. These results suggest that respondents perceive their organizations as moderately responsive to environmental and technological demands, while also reporting positive, although not exceptional, performance outcomes.

The distributions show negative skewness for all constructs, indicating that responses are slightly concentrated toward higher values. The Cramér-von Mises tests are significant, confirming departures from normality. This result supports the methodological choice of PLS-SEM, which is appropriate for prediction-oriented models and does not require strict multivariate normality assumptions (Hair et al., 2019).

Table 3. Descriptive statistics and latent variable correlations

Note: **p < .01.

Source: Authors’ survey data.

The correlation matrix reveals positive and statistically significant relationships among all latent constructs. The strongest correlation is between environmental and technological context and operational performance (r = .741, p < .01), followed by decision-making quality and operational performance (r = .727, p < .01). Decision-making speed is also positively correlated with operational performance (r = .670, p < .01), although its association is weaker than that of decision-making quality. This preliminary evidence suggests that both speed and quality matter, but that decision quality and contextual adaptation may play a stronger role in explaining performance.

3.2. Measurement Model Assessment

The measurement model was assessed before estimating the structural paths. This step is essential because conclusions about structural relationships are meaningful only if the constructs are measured reliably and validly. We examined indicator loadings, Cronbach’s alpha, composite reliability, and average variance extracted.

All retained indicators load above the recommended .70 threshold. Decision-making speed has loadings ranging from .743 to .858. Decision-making quality has loadings ranging from .756 to .828. Environmental and technological context has loadings ranging from .730 to .853. Operational performance has loadings ranging from .712 to .805. These results indicate satisfactory indicator reliability.

Internal consistency is also strong. Cronbach’s alpha values range from .880 to .936, while composite reliability values exceed .90 for all constructs. These values are above the commonly recommended threshold of .70 and indicate that the items consistently measure their respective constructs. Convergent validity is also supported because all AVE values exceed .50, consistent with Fornell & Larcker (1981).

Table 4. Measurement model reliability and convergent validity

Source: Authors’ survey data.

These findings indicate that the measurement model meets the requirements of reliability and convergent validity. The operational performance construct has the lowest AVE (.588), but this value remains clearly above the minimum threshold. The results therefore provide a sound empirical basis for estimating the structural model.

3.3. Discriminant Validity

Discriminant validity was assessed using the heterotrait-monotrait ratio and the Fornell-Larcker criterion. The HTMT criterion is particularly useful because it provides a rigorous test of whether constructs that are conceptually related remain empirically distinct (Henseler et al., 2015).

All HTMT values are below the conservative threshold of .85. The highest value is observed between decision-making speed and decision-making quality (HTMT = .822). This result is theoretically plausible because speed and quality are both dimensions of strategic decision-making. However, the value remains below the recommended threshold, indicating that the two constructs are related but distinct.

Table 5. Discriminant validity assessment

Panel A. HTMT ratios

Panel B. Fornell-Larcker criterion

Note: Diagonal values in Panel B represent the square root of AVE.

Source: Authors’ survey data.

The Fornell-Larcker criterion also supports discriminant validity. For each construct, the square root of AVE is higher than its correlations with other constructs. Cross-loadings further confirm that each indicator loads more strongly on its assigned construct than on competing constructs. Taken together, these results indicate that the four constructs are sufficiently distinct for structural analysis.

3.4. Structural Model Assessment

The structural model evaluates the relationships between decision-making speed, decision-making quality, environmental and technological context, and operational performance. The model explains 56.5% of the variance in environmental and technological context and 63.8% of the variance in operational performance. These values indicate moderate to substantial explanatory power.

Predictive relevance is also strong. Q²predict values are positive and high for both endogenous constructs. This suggests that the model has predictive value beyond in-sample explanation. The RMSE and MAE values also indicate acceptable prediction error.

Table 6. Explanatory and predictive power of the structural model

Source: Authors’ survey data.

The explanatory power of the model is particularly relevant for the study’s theoretical argument. It shows that decision-making processes and contextual adaptation jointly explain a substantial proportion of organizational performance. This result supports the view that performance does not depend only on internal decision speed or decision quality, but also on how these decision processes strengthen the organization’s fit with its environmental and technological context.

3.5. Direct, Indirect, Total, and Mediation Effects

The direct effects are all positive and statistically significant. Decision-making speed has a significant effect on environmental and technological context (β = .304, p < .001) and operational performance (β = .172, p = .008). This confirms that faster decision-making is associated with greater contextual responsiveness and higher operational performance. However, the direct effect of speed on performance is relatively weak.

Decision-making quality has a stronger effect on both environmental and technological context (β = .499, p < .001) and operational performance (β = .311, p < .001). This indicates that decision quality is a more powerful predictor than speed. Environmental and technological context also has a significant effect on operational performance (β = .400, p < .001), confirming its central role in the model.

Table 7. Direct effects

Source: Authors’ survey data.

The effect sizes provide additional nuance. Decision-making quality has the largest effect on environmental and technological context (f² = .262), while environmental and technological context has a meaningful effect on operational performance (f² = .192). Decision-making speed has a smaller direct effect on operational performance (f² = .034). This pattern suggests that speed is useful, but its performance value is more limited unless it contributes to contextual adaptation.

The model also tests whether environmental and technological context mediates the relationship between decision-making processes and operational performance. The indirect effect of decision-making speed on operational performance through environmental and technological context is positive and significant (β = .122, p < .001). The indirect effect of decision-making quality on operational performance through environmental and technological context is also positive and stronger (β = .200, p < .001).

Table 8. Indirect, total, and mediation effects

Note: **p < .01.

Source: Authors’ survey data.

The mediation results show that environmental and technological context partially mediates both relationships. This means that decision-making speed and decision-making quality influence operational performance directly, but also indirectly by improving the organization’s adaptation to environmental and technological conditions.

The total effects confirm the stronger role of decision-making quality. Decision-making quality has a total effect of .511 on operational performance, while decision-making speed has a total effect of .294. Thus, both constructs matter, but quality has a more substantial performance implication. This finding is consistent with the argument that speed creates value when it supports timely action, whereas quality creates value by improving interpretation, coherence, resource allocation, and implementation.

3.6. Importance-Performance Interpretation

The importance-performance map reinforces the structural results. Decision-making quality has the highest importance for operational performance, with a total effect of .511. However, its performance level remains moderate, suggesting that it represents the most important area for managerial improvement. Environmental and technological context has the second-highest importance, with a total effect of .400, and relatively stronger performance. Decision-making speed has lower importance, with a total effect of .294, although it remains a significant contributor to performance.

This pattern suggests that Cameroonian organizations should not pursue speed as an isolated objective. Rather, the results indicate that organizations obtain stronger performance benefits when they improve the quality of their decisions and strengthen their capacity to interpret and respond to environmental and technological conditions. Speed remains valuable, but its contribution is more limited and partly dependent on contextual adaptation.

Overall, the results provide strong support for the analytical model. First, the measurement model demonstrates high reliability, convergent validity, and discriminant validity. Second, the structural model explains a substantial proportion of the variance in operational performance. Third, both decision-making speed and decision-making quality have significant direct effects on operational performance. Fourth, environmental and technological context partially mediates both relationships. Finally, decision-making quality emerges as the strongest predictor of operational performance, both directly and through contextual adaptation.

These findings suggest that, in the Cameroonian organizational context, performance is not driven by speed alone. Rather, organizations perform better when decision processes combine sufficient rapidity, high-quality analysis, and adaptive alignment with environmental and technological conditions.

4. Discussion

4.1. Overview of the Main Findings

This study examined how decision-making speed and decision-making quality influence operational performance in Cameroonian organizations, and whether this relationship is mediated by the environmental and technological context. Three central findings emerge from the empirical analysis.

First, both decision-making speed and decision-making quality positively affect operational performance. However, their effects are not equivalent. Decision-making quality exerts the strongest total effect on operational performance (β = .511), while decision-making speed has a smaller, though significant, total effect (β = .294). Second, environmental and technological context significantly predicts operational performance (β = .400), indicating that organizations perform better when they are able to interpret, adapt to, and respond to their external and technological environment. Third, environmental and technological context partially mediates the effects of both decision-making speed and decision-making quality on operational performance. This indicates that decision processes influence performance not only directly, but also by strengthening organizational adaptability.

These results support the broader argument that organizational performance does not depend simply on deciding quickly. Rather, performance depends on the extent to which speed and quality are embedded in adaptive capabilities that allow organizations to align their actions with environmental and technological demands. This finding is particularly important in the Cameroonian context, where organizations often operate under resource constraints, institutional complexity, technological challenges, and changing market conditions.

Table 9. Summary of central findings and theoretical interpretation

Source: Authors’ interpretation based on empirical results.

4.2. Decision Speed Matters, but Its Effect Is Conditional

The positive effect of decision-making speed on operational performance supports a central proposition in the strategic decision-making literature: organizations that decide quickly may respond earlier to market changes, technological shifts, regulatory pressures, and competitive threats. This result is consistent with Eisenhardt (1989), who showed that fast decision-makers in high-velocity environments can outperform slower competitors. It also aligns with Judge & Miller (1991), who argued that speed is particularly valuable when environmental conditions are unstable and opportunities are temporary.

The result also partially confirms Baum & Wally (2003), who found that strategic decision speed predicted subsequent firm growth and profitability. In the present study, decision speed significantly affects operational performance both directly and indirectly through environmental and technological context. This suggests that speed can be valuable in the Cameroonian context, especially when organizations must respond to changing markets, technological pressures, and institutional demands.

However, the size of the effect calls for caution. The direct effect of decision-making speed on operational performance is significant but relatively weak compared with the effect of decision-making quality. This finding suggests that speed alone is not sufficient to produce superior performance. Fast decisions may improve responsiveness, but they may also create risks when they are not supported by reliable information, adequate analysis, and implementation capacity.

This nuance is consistent with Perlow et al. (2002), who warned against the “speed trap.” Organizations may become trapped in a self-reinforcing cycle where faster decisions are valued for their own sake, even when they undermine reflection, coordination, and learning. The Cameroonian evidence therefore suggests that speed should be understood as a conditional capability rather than an unconditional advantage. Speed improves performance when it contributes to contextual adaptation, but it may become less useful when it is disconnected from decision quality.

4.3. Decision Quality as the Strongest Performance Driver

The strongest finding of the study is the central role of decision-making quality. Decision quality has the largest direct and total effect on operational performance. This result reinforces the argument that high-quality strategic decisions improve organizational outcomes by enhancing information use, reducing errors, improving coherence, and facilitating implementation. This finding is consistent with Fredrickson & Mitchell (1984), who showed that comprehensive strategic decision processes can improve performance under specific environmental conditions. It also supports Dean & Sharfman (1996), who demonstrated that procedural rationality improves decision effectiveness. Similarly, Priem et al. (1995) argued that rationality in strategic decision processes can enhance performance when environmental dynamism is considered.

In the present study, decision quality appears to matter more than speed because it strengthens both direct performance outcomes and contextual adaptation. A high-quality decision is not merely an analytically sophisticated decision. It is also a decision that is coherent, feasible, relevant, and implementable. This interpretation is consistent with Forbes (2007), who argued that decision comprehensiveness should not be reduced to the amount of information collected, but should be understood in relation to uncertainty, interpretation, and strategic relevance. This result is particularly meaningful for Cameroonian organizations. In environments marked by infrastructural limitations, uneven technological capabilities, and institutional constraints, poor decisions may be costly and difficult to reverse. Under such conditions, the quality of interpretation, resource allocation, and stakeholder alignment may be more important than speed alone. The findings therefore suggest that managers should not simply accelerate decisions. They should improve the informational, analytical, and implementation quality of decision processes.

4.4. Environmental and Technological Context as an Adaptive Mechanism

The results show that environmental and technological context plays a central mediating role. Both decision speed and decision quality influence operational performance partly through their effect on environmental and technological adaptation. This finding provides an important theoretical extension of prior work.

Baum & Wally (2003) showed that environmental and organizational characteristics influence decision speed and performance. The present study extends this argument by positioning environmental and technological context as a mediating mechanism. In other words, decision processes create value not only because they directly affect performance, but because they improve the organization’s ability to adapt to external and technological conditions.

This result is also consistent with dynamic capabilities theory. Teece et al. (1997) argued that firms achieve competitive advantage by sensing opportunities and threats, seizing strategic options, and reconfiguring resources. The present study suggests that decision-making quality and speed contribute to performance when they strengthen these adaptive capacities. Decision speed helps organizations act before opportunities disappear. Decision quality helps organizations interpret environmental signals correctly and select appropriate responses.

The stronger indirect effect of decision quality compared with decision speed is theoretically important. It suggests that adaptation is not merely a matter of moving fast. It depends more fundamentally on the quality of interpretation and response. In the Cameroonian context, where technological adoption is increasing but uneven, organizations may need high-quality decisions to translate technological possibilities into operational performance. Thus, technology does not automatically improve performance. It becomes valuable when it is embedded in decision processes that are coherent, informed, and strategically aligned.

4.5. Theoretical Contributions and Boundary Conditions

This study makes three main theoretical contributions. First, it contributes to the strategic decision-making literature by integrating decision speed and decision quality in a single empirical model. Prior studies have often examined speed and quality separately. Eisenhardt (1989), Judge & Miller (1991), and Baum & Wally (2003) emphasized the performance effects of decision speed. Fredrickson & Mitchell (1984), Dean & Sharfman (1996), and Priem et al. (1995) emphasized comprehensiveness, rationality, and decision quality. The present study brings these two streams together and shows that both matter, but that their effects differ in magnitude and mechanism.

Second, the study extends prior research by showing that environmental and technological context partially mediates the relationship between decision processes and performance. This finding refines the speed–performance logic. It suggests that decision speed improves performance not simply because fast action is valuable, but because speed may enhance contextual responsiveness. Similarly, decision quality improves performance not only because better decisions produce better outcomes, but because quality strengthens the organization’s ability to adapt to environmental and technological conditions.

Third, the study contributes to the internationalization of strategic decision-making theory by providing evidence from Cameroon. Most prior studies were conducted in developed economies, high-technology sectors, or Western institutional contexts. By examining Cameroonian organizations, this study tests the boundary conditions of established theories in an emerging African economy. The results suggest that the core assumptions of strategic decision-making theory remain relevant, but their relative importance may shift. In this context, quality appears more decisive than speed, and contextual adaptation is a key explanatory mechanism.

At the same time, the conclusions must be nuanced. The study uses a cross-sectional design, which limits causal inference. Although the theoretical model specifies directional relationships, the data do not fully establish temporal causality. It is possible that higher-performing organizations also develop better decision processes and stronger adaptive capabilities. Future research could use longitudinal data to examine whether decision speed and quality predict subsequent performance over time, as in Baum & Wally (2003).

Another boundary condition concerns measurement. The study relies on perceptual survey data. Although perceptual measures are common in strategic management research, especially when objective performance data are difficult to obtain, they may introduce common method bias. Future studies could combine survey responses with archival indicators such as sales growth, profitability, productivity, market share, or service delivery performance.

Finally, the study examines organizations across multiple sectors. This strengthens external relevance, but it may also mask sector-specific dynamics. Decision speed may be more valuable in telecommunications, logistics, and technology-intensive sectors, while decision quality and stakeholder alignment may matter more in public institutions, healthcare, education, and non-profit organizations. Future research should therefore test whether sectoral velocity, regulatory pressure, and organizational mission moderate the relationships identified in this study.

4.6. Managerial Implications

The findings have important implications for managers in Cameroon and similar emerging economies. First, managers should avoid treating speed as an isolated performance objective. Faster decisions can improve responsiveness, but speed without quality may generate errors, weak implementation, or stakeholder resistance. Managers should therefore design decision processes that accelerate action while preserving relevant analysis. Second, organizations should invest in decision quality. This requires better information systems, clearer evaluation criteria, stronger analytical capabilities, and more systematic review of decisions. The results show that decision quality has the strongest total effect on operational performance. Improving decision quality may therefore offer a higher performance return than simply accelerating decisions.

Third, managers should strengthen the organization’s environmental and technological adaptability. The mediating role of environmental and technological context indicates that performance depends on the capacity to translate decisions into adaptive responses. Organizations should therefore improve environmental scanning, regulatory monitoring, technological readiness, and internal learning routines. Fourth, leaders should recognize that the optimal balance between speed and quality may vary across contexts. In highly dynamic environments, speed may be necessary to avoid missed opportunities. In resource-constrained or highly regulated environments, decision quality may be more important because mistakes are costly and difficult to reverse. Managers should therefore calibrate decision processes to the nature of the decision, the velocity of the environment, and the organization’s implementation capacity.

Taken together, the results challenge a simple opposition between fast and high-quality decisions. The evidence suggests a more nuanced conclusion: speed matters, but quality matters more; and both matter most when they enhance environmental and technological adaptation. This interpretation advances strategic decision-making theory by showing that performance outcomes are not generated by decision speed or decision quality alone. They emerge from the alignment between decision processes, contextual responsiveness, and organizational capabilities. In the Cameroonian context, where organizations face uncertainty, institutional complexity, and uneven technological development, the strongest performance gains appear to come from decisions that are not only timely, but also analytically sound, contextually informed, and implementable. This is the main theoretical value of the study: it shifts the discussion from a speed-versus-quality trade-off toward a context-sensitive model of decision effectiveness.

Conclusion

This article examined how decision-making speed and decision-making quality affect operational performance in Cameroonian organizations, and whether the environmental and technological context mediates these relationships. Drawing on strategic decision-making theory and dynamic capabilities reasoning, the study sought to move beyond a simple opposition between fast and careful decisions. Its central argument was that performance depends not only on how rapidly organizations decide, but also on how well decisions are informed, interpreted, implemented, and aligned with environmental and technological conditions.

The empirical results provide three main conclusions. First, both decision-making speed and decision-making quality positively influence operational performance. However, decision-making quality exerts the stronger effect. Its total effect on operational performance is substantially higher than that of decision-making speed, indicating that performance gains depend more on the coherence, relevance, and implementability of decisions than on rapidity alone. Second, the environmental and technological context has a significant positive effect on operational performance. This suggests that organizations perform better when they are able to interpret market changes, respond to regulatory and socio-cultural constraints, and integrate technological tools into decision processes. Third, environmental and technological context partially mediates the effects of both decision-making speed and decision-making quality on operational performance. Thus, speed and quality improve performance not only directly, but also by strengthening the organization’s adaptive fit with its environment.

The study makes three theoretical contributions. First, it integrates two streams of strategic decision-making research that are often treated separately: the literature on decision speed and the literature on decision quality. By examining both constructs in the same model, the article shows that speed and quality are not interchangeable predictors of performance. They operate differently and with unequal strength. Second, the study contributes to a contingency view of decision-making by showing that the environmental and technological context is not merely a background condition. It is an explanatory mechanism through which decision processes are translated into performance outcomes. Third, the article extends strategic decision-making research to an underexplored African setting. Evidence from Cameroon suggests that established theories remain relevant, but their effects are shaped by institutional complexity, resource constraints, technological unevenness, and sectoral diversity.

The practical implications are also clear. Managers should avoid treating speed as an end in itself. Fast decisions can improve responsiveness, but their performance value remains limited when they are not supported by reliable information, analytical discipline, and implementation capacity. Organizations should therefore invest in decision quality by improving information systems, strengthening analytical routines, clarifying decision criteria, and institutionalizing review mechanisms. At the same time, managers should build adaptive capabilities that allow decisions to be connected to environmental scanning, regulatory monitoring, technological readiness, and stakeholder expectations. In contexts such as Cameroon, where uncertainty and constraints are high, the most effective organizations are likely to be those that combine timely action with informed judgment.

This study has limitations that open avenues for future research. First, its cross-sectional design limits the ability to establish causal relationships. Future studies should use longitudinal designs to examine whether decision speed and decision quality predict subsequent performance over time. Second, the study relies on perceptual survey data. Future research should combine managerial perceptions with objective indicators such as profitability, productivity, sales growth, customer retention, innovation output, or service delivery performance. Third, the multi-sectoral design improves contextual coverage but may conceal sector-specific dynamics. Future studies could compare sectors such as telecommunications, logistics, public administration, healthcare, education, and agro-industry to determine where speed or quality matters most. Fourth, future research could examine moderators such as environmental dynamism, organizational size, digital maturity, leadership style, decision centralization, and institutional pressure.

Overall, this article shows that the performance of organizations depends less on a universal preference for either speed or quality than on the capacity to align decision processes with contextual demands. In Cameroonian organizations, decision quality appears to be the stronger driver of performance, while decision speed remains valuable when it contributes to adaptive responsiveness. The broader implication is that strategic decision effectiveness should be understood as a configuration of timeliness, quality, and contextual adaptation, rather than as a simple trade-off between deciding fast and deciding well.

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