When Data Disagrees: The Role of Communicative Cues in Strategic Decision Evaluation Paul R Castleberry Jr. Communication Department, Weber State University Dr. Aaron Atkins September 30, 2026 _______________________________ (signature) Dr. Aaron Atkins, Committee Chair _______________________________ (signature) Dr. Michael Ault, Committee Member 2 Abstract Organizations rely on analytic evidence to guide strategic decision-making, yet recommendations may still be evaluated favorably when available data contradict a manager’s intuitive judgment or gut instinct. This study examines whether perceived presenter expertise and narrative coherence influence perceived decision value when analytic evidence conflicts with a strategic business recommendation. Using a 2 × 2 between-subjects experimental vignette design, 260 participants with organizational or business experience evaluated a fictional recommendation in which presenter expertise and narrative coherence were manipulated while the recommendation and contradictory evidence remained constant. Results indicate that perceived presenter expertise and narrative coherence were both statistically significant positive predictors of perceived decision value. A significant interaction showed that the effect of one cue depended on the level of the other. Contrary to the predicted additive pattern, high expertise combined with high narrative coherence did not produce the highest perceived decision value. Instead, strengthening either cue was associated with higher decision value when the other was weak, suggesting a plausible compensatory relationship. These findings extend the Heuristic-Systematic Model to the evaluation of organizational decisions and demonstrate how communicator characteristics and message structure can influence judgments when analytic evidence and recommended action conflict. Keywords: heuristic-systematic model, perceived expertise, narrative coherence, organizational decision-making, perceived decision value. 3 Table of Contents Introduction and Statement of the Problem ................................................................. 5 Social and Scholarly Rationale ........................................................................................... 7 Literature Review ...................................................................................................... 11 Communication and Organizational Attention ................................................................ 11 The Heuristic–Systematic Model of Information Processing ........................................... 13 Perceived Expertise and Influence in Group Decision-Making........................................ 16 Narrative Cognition and Decision Interpretation ............................................................ 18 Communication, Power, and Voice in Decision Contexts ................................................. 20 Research Questions .......................................................................................................... 21 Method ...................................................................................................................... 22 Research Design ............................................................................................................... 22 Participants ...................................................................................................................... 23 Procedure ......................................................................................................................... 25 Measures........................................................................................................................... 25 Data Analysis .................................................................................................................... 27 Results....................................................................................................................... 29 Preliminary Analyses........................................................................................................ 29 4 Manipulation Checks........................................................................................................ 29 Correlations Among Study Variables............................................................................... 29 Perceived Expertise and Narrative Coherence as Predictors of Decision Value .............. 30 Experimental Effects of Presenter Expertise and Narrative Coherence .......................... 31 Supplemental Analysis of Managerial Experience ........................................................... 34 Discussion ................................................................................................................. 34 Perceived Presenter Expertise and Decision Value .......................................................... 35 Narrative Coherence and Decision Value ......................................................................... 37 The interaction between expertise and narrative coherence ............................................ 39 Theoretical Implications ................................................................................................... 41 Influence of Managerial Experience ................................................................................. 42 Practical Implications ....................................................................................................... 43 Limitations and future research ....................................................................................... 45 Conclusion ................................................................................................................ 46 References ................................................................................................................. 48 Appendix A................................................................................................................ 56 Appendix B................................................................................................................ 60 Appendix C................................................................................................................ 65 5 Introduction and Statement of the Problem Data-driven decision-making, backed by advanced analytics, decision support structures, and business intelligence platforms/dashboards, is becoming increasingly important in organizations and is emphasized as a dominant organizational logic and strategic imperative for improving efficiency, forecasting accuracy, and establishing/maintaining a competitive advantage within organizational settings (Davenport & Harris, 2007; Shmueli & Koppius, 2011). Strategic decisions for organizations rely on empirical evidence rather than intuition alone, as firms increasingly institutionalize analytics into formal decision-making processes and executive reporting structures. Organizational decision-making, however, often occurs when analytical data and managerial judgment are misaligned, particularly under conditions of uncertainty, ambiguity, or conflicting performance signals (Kahneman, 2011; Mintzberg et al., 1976). In such cases, decision-makers may discount, reinterpret, or selectively attend to analytical evidence, leading to outcomes where final decisions deviate from data-driven recommendations and reflect subjective interpretations rather than systematic analysis (Bazerman & Moore, 2013; March, 1994). When available metrics or empirical data support a data-driven decision, strategic recommendations may persist; however, even when empirical evidence does not support a proposed course of action, recommendations may still be maintained or justified by decision makers within the organization through alternative interpretations or communicative framing. Organizational scholars have noted that strategic decisions are made not only on objective information but also through communication dynamics, the formation of interpretations, and the varying social influences within the organizational environment (Maitlis & Christianson, 2014; Weick, 1995). When evaluators are faced with analytical evidence that conflicts with their 6 proposed course of action, they interpret not only the data but the communicative context surrounding the presented recommendation. These interpretations may also be shaped by perceptions of the communicator, including whether the presenter is viewed as a credible expert or authority, as well as by perceptions of the coherence of the message itself. Such cues may influence how a recommendation is evaluated even when contradictory evidence is available. The Elaboration Likelihood Model (ELM) and Heuristic-Systematic Model (HSM) provide related explanations for this process, with peripheral cues in ELM and heuristic cues in HSM describing conceptually similar mechanisms for evaluating persuasive information (Petty & Cacioppo, 1986; Chaiken, 1980). Research on persuasion and social cognition suggests individuals rarely evaluate information solely through analytic reasoning, as decision-making is often shaped by cognitive limitations and reliance on simplifying strategies such as heuristics and biases (Kahneman, 2011; Tversky & Kahneman, 1974). In organizational contexts, decision-makers may evaluate recommendations through a combination of systematic processing of evidence and heuristic cues embedded in communication, including perceptions of the credibility and clarity of how the evidence is presented (Bazerman & Moore, 2013; Chaiken, 1980; Chaiken et al., 1989; Kahneman, 2011). Presenters’ or communicators’ individual characteristics, such as perceived expertise and credibility, can influence how persuasive messages are evaluated, interpreted, and accepted (Hovland & Weiss, 1951; Pornpitakpan, 2004). Narrative coherence, defined as the extent to which information is presented in a clear and internally consistent manner that allows individuals to construct a meaningful interpretation of events, can also shape how individuals take complex information, organize it, and then interpret situations within an organizational context (Kaplan & Orlikowski, 2013; Weick, 1995). 7 Taken together, the insights suggest that interpretive or evaluative judgments about strategic decisions may be shaped by both analytic evidence and communicative perceptions associated with the presented recommendation. There is, however, little research examining how such communicative cues influence judgments when analytical data conflict with a proposed decision by an expert, authority, manager, or any other decision maker within the organization. The present study examines how communicative perceptions shape the selected judgments in such situations. This research will specifically investigate whether perceived presenter expertise and narrative coherence influence perceived decision value when empirical data conflict with the decision-maker’s recommended proposal. For this study, the researcher uses the term “evaluators” to refer to organizational members responsible for assessing or responding to strategic recommendations, including employees, managers, and other decisionmakers within the organization. Rather than focusing solely on the original decision-makers who proposed the action, this study centers on how evaluators interpret, assess, and ultimately judge the value of a proposed decision when faced with conflicting or contradictory analytical evidence. Social and Scholarly Rationale Understanding how communicative perceptions shape decision-making processes within organizations highlights important implications for professional workplace communication practices and for communication scholarship. On paper, organizations strongly emphasize evidence-based decision-making, which encourages the systematic analysis of empirically supported data (Pfeffer & Sutton, 2006). 8 However, in real-world professional workplace environments, decision-making is often characterized by ambiguity, incomplete or context-free information, and subjective interpretation. Thus, individuals within the organization must make sense of information of varying degrees of complexity through communication and social interaction (Weick et al., 2005). For evaluators, this sensemaking process introduces interpretive uncertainty. Individuals must rely on cues such as how clearly a recommendation is communicated and how credible the presenter appears, rather than solely on the data itself. As a result, evaluators may arrive at different conclusions from the same set of information. They may experience reduced confidence in their own judgments. Evaluators may defer to communicative signals over analytical evidence when evaluating strategic decisions (Chaiken, 1980; Kahneman, 2011; March, 1994; Weick et al., 2005). Central to how individuals within organizations evaluate issues and make strategic decisions is a structured communication process. Research on attention-based views of organizations, particularly work examining how communication channels and strategic narratives shape organizational attention, suggests that communication influences how organizational members prioritize issues and opportunities (Ocasio, 1997; Ocasio et al., 2018; Kaplan, 2008; Joseph & Ocasio, 2012). Organizational members interpret information presented through meetings, presentations, and strategic narratives, with communication serving as the primary mechanism for constructing and negotiating meaning within organizations (Weick, 1995; Maitlis, 2005). Through this process, individual interpretations can contribute to shared understanding of strategic issues as organizational members collectively make sense of information and develop coherent narratives that guide action (Weick et al., 2005; Cornelissen, 2012). 9 Persuasion research, particularly studies examining source credibility, message framing, and dual-process modeling, further suggest selected judgments about presented information are also influenced by varying heuristic cues within a communication context (Hovland & Weiss, 1951; Eagly & Chaiken, 1993). The Heuristic-Systematic Model suggests individuals within organizations tend to rely on specific cues, such as the perceived presenter expertise or the construction of messages, to evaluate information, especially when decision-makers’ cognitive capacity for deep analysis is limited (Chaiken, 1980; Chaiken et al., 1989). These varying cues can affect not only how messages are accepted but also how individuals within organizations interpret the evidence presented to them. Heuristic cues such as source expertise or message clarity can bias the direction and depth of systematic processing, shaping how information is understood, weighted, and ultimately integrated into judgment (Chaiken & Maheswaran, 1994; Chen & Chaiken, 1999). In the present study, these frameworks are not used to examine persuasion outcomes but rather to understand how communicative cues embedded within messages shape evaluative judgments. Research into narratives also highlights the importance of storytelling in the way decision-makers in organizations analyze complex data. Storytelling is the process of structuring information into a sequence of events with causal relationships, actors, and outcomes, making complex information more understandable and meaningful (Bruner, 1991; Fisher, 1984). Selected decision-makers often put together narratives to organize ideas, to understand how relationships are formed, and to help them select strategies to implement in various contexts (Boje, 1995; Sonenshein, 2010; Kaplan & Orlikowski, 2013). Narrative coherence, defined as the extent to which a narrative is internally consistent, logically structured, and aligns events in a way that allows individuals to follow and interpret the message easily, can provide structure for 10 how individuals within the organization evaluate the feasibility, outcomes, and credibility of the strategic actions presented to them (Fisher, 1984; Green & Brock, 2000). Narratives influence how individuals organize, interpret, and evaluate information by increasing message engagement and enabling them to process information more intuitively and holistically, thereby shaping both the interpretation and acceptance of the message (Green & Brock, 2000; Slater & Rouner, 2002). In this study, narrative coherence is not examined as a mechanism of narrative persuasion but as a structural feature of messages that may function as a heuristic cue shaping evaluative judgments. These insights provide relatively little on how communicative cues influence decisionmakers’ choices in complex strategic initiatives, including when empirically supported data appear to contradict their recommendations. The studies to date tend to focus either on persuasion in a controlled laboratory setting or on narrative processes in broader organizational settings. As a result, the mechanisms through which communicative perceptions shape evaluative judgments when analytic data contradict a recommended course of action remain underexamined. This study addresses this gap by examining how perceived presenter expertise and narrative coherence influence perceived decision value as evaluated by organizational members (evaluators), including employees, managers, and other decision-makers responsible for assessing strategic recommendations, when empirically supported analytical data contradict a decision-maker’s proposed course of action. The focus is not on the original decision-maker, but on how these evaluators interpret, assess, and assign value to a proposed decision under conditions of analytical contradictions. 11 Literature Review Understanding how perceived presenter expertise and narrative coherence shape decision evaluation requires a framework that accounts for both careful consideration of evidence and reliance on communicative cues. The present study, therefore, draws primarily on the HeuristicSystematic Model (HSM; Chaiken, 1980; Chaiken et al., 1989), which provides a framework for understanding how individuals evaluate information through both systematic analysis and heuristic processing. Related models provide complementary perspectives on effortful and cuebased evaluation. Petty and Cacioppo’s (1986) Elaboration Likelihood Model (ELM) and Slater and Rouner’s (2002) Extended Elaboration Likelihood Model (E-ELM) are considered here as related frameworks but are not directly tested in the present study. Communication and Organizational Attention Organizational communication research has increasingly emphasized the role of communication in shaping how individuals interpret strategic decisions and issues (Weick, 1995). Rather than serving as a conduit for information, communication structures how individuals within organizational settings prioritize complex problems and opportunities (Ocasio, 1997). The attention-based view suggests that decision-makers within organizations depend on how individuals allocate attention to issues, defined as the process by which organizational members selectively focus on certain problems, opportunities, and information while ignoring others due to cognitive limits and structural influences in their environment (Ocasio, 1997; Simon, 1947). Attention is organized and shaped by specific communication channels and organizational rituals, such as recurring meetings, reporting structures, and formalized decisionmaking processes that guide how information is shared and prioritized, and various social 12 interactions between individuals within the hierarchy, which determine what decision-makers see and how they interpret the information presented to them, rather than occurring at random (March & Olsen, 1976; Ocasio, 1997). Ocasio et al. (2018) argue that communication process structures are central in shaping patterns of organizational attention through individuals’ use of specific language and through framing discourse that directs attention to specific issues, thereby diverting attention from others. Communication also constructs how individuals within organizations collectively analyze and interpret strategic decisions. This is done through meetings, presentations, structured collaboration, social team-building, and informal conversations, in which individuals within the organization construct a shared interpretation of complex decisions and strategies. Research on strategic communication practices shows that discursive processes, such as rhetorical persuasion, help decision-makers determine how strategic agendas are constructed and how they analyze competing strategic proposals (Dutton et al., 1997; Mantere & Vaara, 2008). Language and discourse can shape not only what information decision-makers consider but also how they interpret its meaning and implications. Changes in vocabulary or framing can redirect organizational attention toward particular aspects of an issue, shaping which problems, explanations, or potential courses of action receive greater consideration (Ocasio & Joseph, 2008). Communication processes can also influence how organizational members interpret and evaluate strategic recommendations when available information is ambiguous or contested (Maitlis & Christianson, 2014; Weick 1995). Under such conditions, evaluators are more likely to rely on communicative cues, such as language framing, information clarity, and perceptions of source credibility, to guide their judgments, as ambiguity increases dependence on interpretive processes and heuristic evaluation (Chaiken, 1980; Kahneman, 2011; Weick, 1995). 13 This perspective suggests that communicative cues influence how evaluators interpret analytic evidence in decision-making contexts by shaping both the direction and depth of cognitive processing used to evaluate information (Chaiken & Maheswaran, 1994; Chen & Chaiken, 1999). When analytic data appear to contradict a proposed course of action, decisionmakers must interpret both the data and the communication cues surrounding the recommendation, as ambiguity in evidence increases reliance on interpretive processes and socially constructed meaning (Weick, 1995; March, 1994). Understanding how communicative cues shape these interpretations requires examining the cognitive processes that decision-makers use to evaluate information, particularly the interaction between systematic processing of analytic evidence and heuristic cues such as source credibility and message coherence, as outlined in dual-process models of persuasion (Chaiken, 1980; Eagly & Chaiken, 1993; Petty & Cacioppo, 1986). These organizational communication processes establish the context in which communication cues may shape the evaluation of decisions. HSM provides a more specific framework for explaining how evaluators may use those cues alongside their consideration of analytic evidence. The Heuristic–Systematic Model of Information Processing The Heuristic–Systematic Model (HSM) provides a theoretical framework for understanding how individuals evaluate information in persuasive situations. According to HSM, individuals process information through two primary modes: systematic processing, which involves careful and effortful evaluation of the content and evidence presented, and heuristic processing, which relies on accessible cues or simple decision rules to form judgments with less cognitive effort. (Chaiken, 1980; Chaiken et al., 1989). Unlike models that emphasize distinct processing routes, HSM allows for simultaneous use of heuristic and systematic processing, 14 making it well-suited for examining how communicative cues shape evaluation under conditions of ambiguity. Systematic processing involves careful, effortful analysis of message content, including the evaluation of evidence and the use of logical reasoning, as individuals engage in deliberate cognitive effort to assess the validity of arguments (Chaiken, 1980; Chaiken et al., 1989; Petty & Cacioppo, 1986). Heuristic processing relies on cognitive shortcuts or cues that allow individuals to make judgments without extensive thought processes, particularly under conditions of limited cognitive capacity or motivation (Chaiken, 1980; Kahneman, 2011). These cues may include physical communicator characteristics, social consensus, message structure, or other signals that indicate a message’s credibility, which individuals use as proxies for evaluating information when systematic analysis is reduced or incomplete (Chaiken & Maheswaran, 1994; Metzger et al., 2010). Importantly, the Heuristic-Systematic Model does not treat these two modes of processing as mutually exclusive. Individuals often use heuristic cues alongside systematic analysis when interpreting information. This is applicable in organizational contexts where evaluators must assess strategic recommendations under conditions of ambiguity or conflicting analytic evidence (Chaiken, 1980; Chen & Chaiken, 1999). Heuristic cues can shape how individuals interpret evidence, influence the depth of systematic processing, or bias the conclusions drawn from analytic reasoning, as evaluators may rely on cues such as presenter expertise or the clarity of a recommendation to guide how they engage with and make sense of the underlying data (Chaiken & Maheswaran, 1994; Eagly & Chaiken, 1993; Petty & Cacioppo, 1986). 15 Source characteristics are among the most widely studied heuristic cues within persuasion research. Early work by Hovland and Weiss (1951) demonstrated that communicator credibility significantly influences the acceptance of persuasive arguments, a finding consistently supported and extended in subsequent research on source effects and persuasion (Eagly & Chaiken, 1993; McGuire, 1985; Petty & Cacioppo, 1986). Although individuals may learn the factual content of a message regardless of the communicator’s credibility, their willingness to accept the message’s conclusion is strongly influenced by the perceived expertise of the source, which in turn shapes perceptions of trustworthiness. Research further demonstrates that source expertise can serve as a heuristic cue that directly influences judgment, particularly under conditions of low motivation or limited ability to process information, thereby reinforcing its role in shaping evaluative outcomes (Chaiken, 1980; Metzger et al., 2010; Pornpitakpan, 2004). Research has consistently confirmed the importance of source expertise as a heuristic cue influencing persuasion and judgment (Pornpitakpan, 2004; Eagly & Chaiken, 1993; Petty & Cacioppo, 1986). When individuals perceive a communicator as knowledgeable or competent in their field, they may rely on that perception as a cognitive shortcut to evaluate the message’s validity, particularly when motivation or ability to process information systematically is limited (Chaiken, 1980; Chen & Chaiken, 1999). In complex decision-making contexts where evaluating all available information may be cognitively demanding, these specific heuristic cues can play an important role in shaping judgments, as individuals defer to perceived expertise to guide interpretation and reduce cognitive effort (Kahneman, 2011; Metzger et al., 2010). Within organizational settings, decision-makers frequently encounter situations in which analytic evidence is complex or ambiguous. In these contexts, evaluators within the organization, including employees, managers, and other decision-makers responsible for assessing strategic 16 recommendations, may rely on heuristic cues such as perceived expertise to interpret and evaluate the evidence presented when ambiguity limits the ability to engage in fully systematic analysis (Chaiken, 1980; Chen & Chaiken, 1999; Kahneman, 2011). Among the heuristic cues relevant to the evaluation of organizational decisions, perceived source expertise is important because it provides evaluators with information about whether a communicator appears knowledgeable and credible. Perceived Expertise and Influence in Group Decision-Making Research on group dynamics and social influence further highlights the importance of perceived expertise in shaping strategic decision outcomes. Within groups, influence is often granted to individuals who appear to possess superior knowledge or experience related to the task at hand based on observable or contextual cues such as professional title, credentials, appearance status, or behavior (Berger et al., 1972; Lord, 1985; Ridgeway, 1987). Additional research demonstrates that individuals perceived as more competent or expert are more likely to emerge as influential within group decision-making processes, even when their actual competence is not significantly higher than that of other members (Driskell & Mullen, 1990; Anderson & Kilduff, 2009). Such cues can function as heuristic signals that evaluators use to infer competence when complete information about an individual’s actual expertise is unavailable. However, perceptions of competence, defined as individuals’ judgments about another person’s knowledge, skill, and capability to perform effectively in a given context, do not always reflect actual ability (Fiske et al., 2007; Lord & Maher, 1991). Research conducted on trait dominance suggests that individuals who display assertive or confident behavior may be perceived as more competent and influential even when their actual expertise does not differ 17 from that of other group members (Anderson & Kilduff, 2009). Dominant individuals tend to exhibit behavioral cues associated with competence, such as speaking more frequently, using assertive language, and displaying confident nonverbal behavior through posture, gestures, and other visible cues (Carli et al., 1995; Driskell et al., 1993). These signals can lead group members to infer expertise and grant influence accordingly within strategic decision-making processes, as individuals rely on observable cues to form competence judgments in the absence of complete information (Berger et al., 1972; Fiske et al., 2007). From a heuristic-systematic perspective, these perceived competence signals function as heuristic cues that evaluators may use to guide judgment when analytic evaluation is constrained, reinforcing the role of perceived expertise in shaping how information is interpreted and valued (Chaiken, 1980; Chen & Chaiken, 1999). An important note is that members can only evaluate competence based on the information available to them. As a result, influence within groups is often shaped by perception rather than objective expertise (Lord, 1985). Individuals who appear knowledgeable or confident may therefore exert disproportionate influence over group decision-making of strategic outcomes, as evaluators rely on observable cues to form judgments about credibility and expertise in the absence of complete information (Berger et al., 1972; Fiske et al., 2007). At the organizational level, these perception-based judgments can aggregate to shape collective decision outcomes, as groups may align around individuals perceived as more competent, even when those perceptions are not fully supported by objective evidence, potentially leading to biased or suboptimal strategic choices (Anderson & Kilduff, 2009; Driskell & Mullen, 1990). These findings suggest that perceptions of a communicator's expertise influence how decision-makers interpret strategic recommendations in organizational contexts. When these 18 individuals are perceived as highly knowledgeable or experienced, evaluators may be more inclined to interpret ambiguous evidence in ways that support the proposed decision. Communicator characteristics, however, represent only one source of evaluation cues. The structure of the message itself may also shape how evaluators organize and interpret conflicting information. Narrative Cognition and Decision Interpretation Another important factor influencing decision-making processes is the role narrative, defined as a structured account that organizes events, actors, and causal relationships into a coherent story, which plays in organizing and interpreting information (Bruner, 1991; Fisher, 1984). Research in cognitive psychology suggests that individuals often interpret complex information by constructing coherent stories that link motivations and outcomes, thereby making sense of ambiguous or incomplete data (Pennington & Hastie, 1992; Schank & Abelson, 1977). These narrative structures help individuals simplify complexity and evaluate the plausibility of competing (Weick, 1995). Within the present study, these narrative processes are not examined as immersive or persuasive experiences, but as structural features that influence how evaluators interpret and assign value to strategic recommendations. The story model of decision-making proposes that individuals evaluate evidence by constructing narrative representations that explain what happened and why (Pennington & Hastie, 1992). For example, in legal decision-making, jurors often build a chronological story of events, linking motives, actions, and consequences, and then assess whether the available evidence fits that story, rather than evaluating each piece of evidence in isolation (Pennington & Hastie, 1992, 1993). As such, decision-makers organize information into causal narratives, defined as structured interpretations that link events through cause-and-effect relationships to 19 produce a coherent explanation of outcomes (Abbott, 2004; Weick, 1995). These narratives function as cognitive frameworks that guide interpretation, allowing individuals to fill in gaps and make sense of ambiguity while also prioritizing certain information. Evidence that fits within a coherent story is more likely to be accepted because it reinforces the existing narrative structure, whereas evidence that does not align with the emerging narrative may be discounted or ignored altogether, particularly when it disrupts causal continuity or perceived plausibility (Pennington & Hastie, 1992; Hastie & Pennington, 2000). This narrative-based process has been widely observed in legal decision-making contexts, where jurors interpret evidence by constructing stories that explain the events of a case (Pennington & Hastie, 1986, 1992; Hastie & Pennington, 2000). Jurors evaluate competing narratives and ultimately select the explanation that best fits the available evidence and aligns with their expectations about human behavior (Pennington & Hastie, 1992). Importantly, this process directly shapes decision outcomes, as verdicts tend to favor the side whose narrative is more coherent, complete, and causally plausible, even when the individual pieces of evidence are ambiguous or conflicting (Pennington & Hastie, 1992, 1993). As a result, narrative coherence can influence not only how evidence is interpreted but also which conclusions are ultimately accepted, guiding final judgments by selecting the most compelling story rather than by isolated evaluation of facts (Hastie & Pennington, 2000). Narrative processes also play an important role in organizational contexts. Individuals frequently use narratives to interpret complex strategic decisions and organizational situations, constructing meaning by linking actions and outcomes into coherent accounts (Weick, 1995; Sonenshein, 2010; Kaplan & Orlikowski, 2013). Through these narratives, organizational members develop shared understandings of reality that guide interpretation and action. Strategic 20 proposals are often presented through narratives that connect past events, present challenges, and anticipated future outcomes. This suggests that evaluators do not assess strategic recommendations solely on the basis of analytic evidence; rather, they interpret that evidence within the context of the narratives used to present it. As a result, the coherence of that narrative may shape how decision-makers evaluate the credibility, feasibility, and overall value of a proposed course of action, especially when analytic data is complex or in tension with existing beliefs. Narrative coherence can therefore influence how individuals interpret and evaluate strategic recommendations. A proposal presented through a clear and logically structured narrative may appear more plausible and persuasive because it provides a coherent explanation that helps evaluators make sense of complex information. This is central to the present study, as it suggests that when analytic data contradict a decision-maker’s preferred recommendation, evaluators may rely not only on the data itself but also on the coherence of the narrative used to present that recommendation. In such situations, a well-constructed narrative may shape how conflicting evidence is interpreted, potentially increasing the perceived value of a recommendation even when the underlying data challenges it. Communication, Power, and Voice in Decision Contexts Communication dynamics within groups may further influence how decision proposals are evaluated. Research on leadership communication suggests that power dynamics shape patterns of participation, voice, and information sharing within teams, often determining which ideas are heard and which are dismissed (Fairhurst, 2007; Yukl, 2013). Individuals in positions of authority are more likely to control the flow of discussion and influence how others interpret information, thereby shaping collective sensemaking processes (Fairhurst, 2011; Weick et al., 21 2005). As a result, evaluators within group settings may not assess proposals in isolation but within a communicative environment structured by power, where certain perspectives are privileged, and others are constrained, influencing how strategic recommendations are interpreted and evaluated. Studies have shown that leaders who experience heightened feelings of power may dominate group discussions and reduce other team members' willingness to voice dissenting perspectives (See et al., 2011). When leaders dominate communication, other participants may perceive that their input is less valued, leading to reduced information sharing and lower-quality group decision-making. These findings highlight the importance of communication patterns in shaping how information is interpreted and discussed within decision contexts. When communication dynamics limit the exchange of alternative perspectives, decision-makers may rely more heavily on heuristic cues or dominant narratives when evaluating proposals. These dynamics further reinforce the role of communicative cues in shaping evaluative judgments, particularly when alternative perspectives are limited, and individuals rely more heavily on message features to guide interpretation. Research Questions Taken together, research on heuristic information processing, source expertise, narrative cognition, and organizational communication suggests that communicative cues may shape how evaluators judge strategic recommendations when analytic evidence conflicts with the recommended course of action. The present study focuses specifically on perceived presenter expertise and narrative coherence, both independently and in combination. Accordingly, the following research questions and hypothesis were proposed: 22 RQ1: How does perceived presenter expertise influence perceptions of decision value when analytic data contradict a decision-maker’s recommended business action? RQ2: How does narrative coherence influence perceptions of decision value when analytic data contradict a decision-maker’s recommended business action? RQ3: How do perceived expertise and narrative coherence interact to influence perceptions of decision value when analytic data contradict a decision-maker’s recommended business action? H1: Perceived presenter expertise and narrative coherence will interact such that strategic recommendations presented by high-expertise communicators and supported by highly coherent narratives will be evaluated as having greater decision value than other combinations, particularly when analytic data contradict the recommendation. Method Research Design This study employed a scenario-based survey using a 2 × 2 between-subjects factorial experimental vignette design. Vignette-based designs are commonly used in organizational and decision-making research because they allow researchers to simulate realistic judgment situations while maintaining experimental control over specific characteristics of the scenario (Aguinis & Bradley, 2014; Atzmüller & Steiner, 2010). The study manipulated two communicative attributes: presenter expertise (high vs. low) and narrative coherence (high vs. low). The design allowed for examination of the independent and combined effects of these attributes on perceived decision value. 23 Participants were presented with a fictional organizational scenario in which a strategic business recommendation was accompanied by analytic data that contradicted the recommended course of action. Presenter expertise was manipulated through information about the presenter's professional background, experience, organizational role, and familiarity with the issue. In the high-expertise condition, the presenter was characterized as having substantial relevant experience and subject-matter knowledge. In the low-expertise condition, the presenter was characterized as having more limited experience and familiarity with the issue. Narrative coherence was manipulated through the structure, clarity, and logical consistency of the explanation accompanying the recommendation. In the high-coherence condition, the recommendation was presented through a clear, logically ordered explanation that connected the available information to the proposed action. In the low-coherence condition, the recommendation was presented with weaker connections, less consistent structure, and a more fragmented explanation. The core organizational context, contradictory analytic data, and strategic recommendations were held constant across conditions. Only the cues associated with presenter expertise and narrative coherence were varied. Participants were randomly assigned to one of four experimental conditions: high expertise/high narrative coherence, high expertise/low narrative coherence, low expertise/high narrative coherence, or low expertise/low narrative coherence. Participants Participants were recruited through convenience sampling from individuals with exposure to organizational or business environments. Recruitment included graduate students and business professionals reached through academic and professional networks. Individuals 24 were eligible to participate if they were at least 18 years old and had experience working in an organizational or business environment. Participation was voluntary, and no incentives were provided. Data screening was conducted prior to analysis. Responses that did not progress sufficiently through the survey to reach the experimental stimulus were removed. After removing incomplete responses, 260 usable responses remained. Age responses were subsequently examined to verify eligibility. Two participants who reported being 16 years old were excluded. One participant reported an age of 0; because this value was implausible, the age response was treated as missing, while the participant’s otherwise usable responses were retained. For the factorial analysis, 257 participants had complete data and were distributed across the four experimental conditions as follows: 62 in the low-expertise/low-coherence condition, 67 in the low-expertise/high-coherence condition, 62 in the high-expertise/low-coherence condition, and 66 in the high-expertise/high-coherence condition. Sample sizes varied slightly across other analyses because of item-level missing data. Among participants with valid age data following eligibility screening, age ranged from 18 to 89 years (M = 36.90, SD = 16.23). Full-time work experience ranged from 0 to 56 years (M = 15.33, SD = 15.60). Of the 237 participants who reported managerial or supervisory experience, 59 (24.9%) currently held such a role, 80 (33.8%) reported previous managerial or supervisory experience, and 98 (41.4%) reported no managerial or supervisory experience. Participants represented a range of organizational roles and industries. Among the 237 participants providing demographic information, 83 (35.0%) identified as students, 48 (20.3%) as individual contributors, 19 (8.0%) as managers, 45 (19.0%) as executives, and 42 (17.7%) selected another role. Participants represented business or consulting (42.6%), manufacturing 25 (8.0%), technology (12.2%), healthcare (6.8%), education (14.8%), and other industries (15.6%). Regarding gender, n= 49.8% identified as male, n= 47.7% as female, n= 0.8% as nonbinary, and n= 1.7% preferred not to answer. Procedure The study was administered through Qualtrics. Participants first reviewed the informedconsent information and indicated their willingness to participate before proceeding. Eligible participants were then randomly assigned to one of the four experimental vignette conditions. Each participant reviewed a fictional strategic business recommendation that conflicted with the accompanying analytic evidence. After reviewing the vignette, participants evaluated the recommendation and completed measures of perceived decision value, perceived presenter expertise, narrative coherence, systematic processing, and heuristic processing. Participants also completed exploratory evaluation items and demographic and professional-experience questions. Measures Perceived decision value (PDV) served as the primary dependent measure, representing the extent to which participants evaluated the proposed decision as strategically valuable and worthy of organizational support. Because an established measure designed specifically to assess PDV in the organizational context examined here was not identified, a five-item study-specific measure was developed. The measure was conceptually adapted from perceived usefulness measurement, Davis's (1989) emphasis on perceived usefulness in terms of performance, effectiveness, and practical value, and tailored to evaluating an organizational decision rather than the usefulness of an information system. Items assessed whether the proposed decision was strategically sound, worthy of implementation, beneficial to the organization, well justified, and useful for decision-making. Responses were measured on a seven-point Likert-type scale, with 26 higher scores representing greater PDV. The five-item measure demonstrated reliability (Cronbach's α = .935). Items were averaged to create the PDV composite when at least four of the five items had valid responses. Perceived presenter expertise was measured using four items assessing whether the presenter appeared knowledgeable, experienced, competent, and qualified. The measure was adapted from source-credibility and expertise research associated with Ohanian (1990), with conceptual support from McCroskey and Teven (1999). Responses were measured using a seven-point Likert-type scale, with higher scores indicating greater perceived expertise. The four-item measure demonstrated internal consistency (α = .943). Items were averaged to create Expertise_mean when at least three valid responses were available. Narrative coherence was measured using five items assessing the clarity, logical structure, organization, completeness, and connection between the evidence and recommendation. The measure was theoretically grounded in Fisher's (1984) conceptualization of narrative coherence and adapted to the organizational decision scenario used in this study. Responses were measured using a seven-point Likert-type scale, with higher scores representing greater perceived narrative coherence. The five-item measure demonstrated internal consistency (α = .922). Items were averaged to create NarrativeCoherence_mean when at least four valid responses were available. Systematic processing was measured using five items assessing the extent to which participants carefully considered the proposal, connected the information to prior knowledge, evaluated the relationship between the evidence and recommendation, considered the organizational importance of the decision, and assessed the recommendation's alignment with the available data. The measure was grounded primarily in Smerecnik et al. (2012), with additional 27 theoretical and measurement support from HSM-related risk-information processing research (Griffin et al., 1999, 2002; Kahlor et al., 2003; Trumbo, 2002). The five-item measure demonstrated good consistency (α = .881). Items were averaged to create Systematic_mean when at least four valid responses were available. Heuristic processing was assessed using five items measuring relatively low-effort information processing, including skimming, limited consideration of the arguments, reliance on overall impressions, and evaluation of the recommendation without extensive consideration of the supporting evidence. Higher scores represented greater self-reported heuristic processing. The five-item measure demonstrated internal consistency (α = .855). Items were averaged to create Heuristic_mean when at least four valid responses were available. Four additional items separately assessed the extent to which participants reported being influenced by presenter expertise, explanation clarity or coherence, analytic evidence, and overall presentation quality. Because these items were intended to represent distinct sources of evaluative influence rather than indicators of a single construct, they were retained as separate exploratory measures and were not combined into a composite score. Three organizationalattitude items similarly assessed distinct attitudes concerning senior leadership, willingness to challenge managerial decisions, and the role of experience and organizational position in decision quality. Data Analysis Statistical analyses were conducted using IBM SPSS Statistics. Prior to RQ testing, the data were screened for eligibility, missing responses, distributional characteristics, and potential outliers. Internal consistency was assessed using Cronbach's alpha for the multi-item measures before calculating composite variables. Distributional assumptions were evaluated using 28 descriptive statistics, skewness and kurtosis, Shapiro-Wilk tests, and visual inspection of the distributions. Although Shapiro-Wilk tests indicated statistically significant departures from normality for the primary composite variables, visual inspection and skewness and kurtosis values indicated that departures were not sufficient to warrant the use of nonparametric analyses. Therefore, the planned analyses were retained. Independent-samples t tests were used as manipulation checks to determine whether participants assigned to the high- and low-expertise conditions differed in perceived presenter expertise and whether participants assigned to the high- and low-narrative-coherence conditions differed in perceived narrative coherence. Pearson correlations were used to examine bivariate relationships among the primary study variables. Multiple linear regression was used to examine the relationships between perceived presenter expertise, narrative coherence, and perceived decision value. A 2×2 factorial ANOVA was conducted to examine the effects of experimentally manipulated presenter expertise and narrative coherence on perceived decision value and to test the interaction between the two variables. When a significant interaction was identified, followup comparisons were conducted to examine how the effect of presenter expertise differed across levels of narrative coherence and how the effect of narrative coherence differed across levels of presenter expertise. A supplemental analysis examined whether perceptions of the experimental stimuli differed according to managerial experience. Participants reporting current or previous managerial or supervisory experience were combined into a single managerial-experience group and compared with participants reporting no managerial or supervisory experience using independent-samples t tests. 29 Results Preliminary Analyses Descriptive statistics were also examined for the primary study variables. PDV had a mean of 4.43 (SD = 1.43), perceived presenter expertise had a mean of 4.90 (SD = 1.41), narrative coherence had a mean of 4.60 (SD = 1.37), systematic processing had a mean of 5.45 (SD = 0.94), and heuristic processing had a mean of 3.75 (SD = 1.33). Manipulation Checks Both experimental manipulations operated as intended. Participants assigned to the highexpertise condition perceived the presenter as significantly more expert (M = 5.39, SD = 1.14) than participants in the low-expertise condition (M = 4.42, SD = 1.48), t(245) = 5.76, p < .001. Participants in the high-narrative-coherence condition also rated the narrative as significantly more coherent (M = 4.90, SD = 1.20) than participants in the low-coherence condition (M = 4.30, SD = 1.46), t(247) = 3.55, p < .001. Correlations Among Study Variables Pearson correlations showed strong positive relationships between PDV and both perceived presenter expertise, r = .708, p < .001, and narrative coherence, r = .675, p < .001. Perceived presenter expertise and narrative coherence were also positively related, r = .675, p < .001. Heuristic processing had a smaller positive relationship with PDV, r = .302, p < .001, whereas systematic processing was not significantly related to PDV, r = -.044, p = .491. Systematic and heuristic processing were negatively related, r = −.504, p < .001. Table 1 presents descriptive statistics, internal consistency estimates, and correlations among the primary study variables. This inverse relationship may indicate that participants who reported greater effortful evaluation of the available information tended to report less reliance on heuristic cues or 30 general impressions, although the correlational nature of the data does not establish that one processing mode displaced the other. Table 1 Descriptive Statistics, Internal Consistency, and Correlations Among Study Variables Variable M SD α 1 2 3 4 1. Perceived 4.43 1.43 .935 — Decision Value 2. Presenter 4.90 1.41 .943 .708*** — Expertise 3. Narrative 4.60 1.37 .922 .675*** .675*** — Coherence 4. Systematic 5.45 .94 .881 −.044 .047 .054 — Processing 5. Heuristic 3.75 1.33 .855 .302*** .231*** .303*** −.504* Processing Note. M = mean; SD = standard deviation; α = Cronbach's alpha. p < .001. 5 — Perceived Expertise and Narrative Coherence as Predictors of Decision Value RQ1 examined the relationship between perceived presenter expertise and perceived decision value when analytic data contradicted the recommended business action. RQ2 examined the relationship between narrative coherence and perceived decision value. To address these questions, a multiple linear regression was conducted with perceived presenter expertise and narrative coherence entered as predictors of PDV. The model was statistically significant, F(2, 242) = 159.85, p < .001, explaining 56.9% of the variance in PDV, R² = .569 (adjusted R² = .566). Perceived presenter expertise was a significant positive predictor, B = .464, SE = .059, β = .452, t = 7.92, p < .001, 95% CI [.349, .580]. Narrative coherence was also a significant positive predictor, B = .389, SE = .060, β = .371, t = 6.50, p < .001, 95% CI [.271, .507]. The results answered RQ1 by demonstrating that perceived presenter expertise was positively associated with perceived decision value. Participants who perceived the presenter as having greater expertise also tended to evaluate the recommendation as having greater decision value. RQ2 was also answered, as greater narrative coherence was positively associated with perceived decision value. Participants who perceived the recommendation as more coherent also tended to 31 evaluate it more favorably. Perceived presenter expertise had the larger standardized coefficient (β = .452) compared with β = .371, indicating that it was the stronger predictor within the model. Regression results for perceived presenter expertise and narrative coherence as predictors of PDV are presented in Table 2. Table 2 Multiple Linear Regression Predicting Perceived Decision Value Predictor B SE β t p 95% CI Presenter .464 .059 .452 7.92 < .001 [.349, .580] Expertise Narrative .389 .060 .371 6.50 < .001 [.271, .507] Coherence Note. Dependent variable = Perceived Decision Value. R² = .569; adjusted R² = .566. Overall model: F(2, 242) = 159.85, p < .001. Experimental Effects of Presenter Expertise and Narrative Coherence RQ3 examined whether experimentally manipulated presenter expertise and narrative coherence interacted in their effects on perceived decision value. H3 predicted that recommendations combining high presenter expertise with high narrative coherence would be evaluated as having greater decision value than the other combinations of presenter expertise and narrative coherence. A 2x2 factorial ANOVA was conducted with presenter expertise and narrative coherence as the independent variables and PDV as the dependent variable. Levene’s test was nonsignificant, F(3,253) = 1.32, p = .268. The overall model was statistically significant, F(3, 253) = 8.55, p < .001, R² = .092 (adjusted R² = .081). There was a statistically significant main effect of presenter expertise, F(1, 253) = 4.95, p = .027, ηp² = .019, and a statistically significant main effect of narrative coherence, F(1, 253) = 14.38, p < .001, ηp² = .054. Most importantly for RQ3, the interaction between presenter expertise and narrative coherence was statistically significant, F(1, 253) = 6.71, p = .010, ηp² = .026. Results of the factorial ANOVA 32 examining the effects of presenter expertise and narrative coherence on PDV are presented in Table 3. Table 3 Factorial ANOVA of Presenter Expertise and Narrative Coherence on Perceived Decision Value Effect F df p ηp² Presenter Expertise 4.95 1, 253 .027 .019 Narrative Coherence 14.38 1, 253 < .001 .054 Expertise × Narrative 6.71 1, 253 .010 .026 Coherence Note. Dependent variable = Perceived Decision Value. ηₚ² = partial eta squared. The pattern of the interaction was examined through follow-up comparisons. When narrative coherence was low, high presenter expertise resulted in significantly greater PDV than low presenter expertise, MD = .825, SE = .247, p < .001, 95% CI [.339, 1.311]. When narrative coherence was high, however, the difference between high and low presenter expertise was not statistically significant, MD = -.063, SE = .238, p = .7993, 95% CI [-.532,.046]. Narrative coherence showed a similar pattern. When presenter expertise was low, high narrative coherence resulted in significantly greater PDV than low narrative coherence, MD = 1.094, SE = .242, p < .001, 95% CI [.617, 1.570]. When presenter expertise was high, the difference between high and low narrative coherence was not statistically significant, MD = .206, SE = .243, p = .397, 95% CI [-.272, .684]. Looking at the four conditions together makes the interaction clearer. The lowexpertise/low-coherence condition produced the lowest PDV (M = 3.71, SD = 1.52). PDV increased when either cue was strengthened: the high-expertise/low-coherence condition had a mean of 4.50 (SD = 1.29), while the low-expertise/high-coherence condition had a mean of 4.77 (SD = 1.24). The high-expertise/high-coherence condition had a mean of 4.71 (SD = 1.45). The interaction between presenter expertise and narrative coherence is illustrated in Figure 1. 33 Figure 1 Interaction Between Presenter Expertise and Narrative Coherence on Perceived Decision Value 7 Perceived Decision Value 6 5 4 3 2 1 Low Expertise High Expertise Presenter Expertise Low Narrative Coherence High Narrative Coherence Note. Values represent mean Perceived Decision Value scores across the four experimental conditions. Although the significant interaction addressed RQ3 by demonstrating that the effects of expertise and narrative coherence were interdependent, the pattern did not support H3 as originally predicted. The combination of high expertise and high narrative coherence did not produce the highest perceived decision value. Instead, the results showed that strengthening either expertise or narrative coherence was associated with higher PDV when the other cue was weak, while strengthening the second cue when one was already high produced no significant additional increase. This interaction represents the study's primary finding, suggesting that perceived expertise and narrative coherence may function in a compensatory rather than additive manner. A strong perception of expertise may compensate for a less coherent narrative, while a highly coherent narrative may compensate for lower perceived expertise. This finding is particularly important because it suggests that evaluations of strategic recommendations may 34 depend not on the accumulation of multiple strong communicative cues, but on whether at least one sufficiently strong cue is available to support the evaluator's judgment. Supplemental Analysis of Managerial Experience A post hoc analysis examined whether perceptions of the stimuli differed according to managerial or supervisory experience. Participants with current or previous managerial experience did not differ significantly from those without managerial experience in perceived presenter expertise, Welch's t(224.82) = 1.81, p = .072. However, participants without managerial experience rated the narrative as more coherent (M = 4.93, SD = 1.31) than those with managerial experience (M = 4.40, SD = 1.36), Welch's t(213.08) = 3.06, p = .003. Discussion The purpose of this study is to examine how communicative cues influence the evaluation of strategic business decisions when analytic data contradict a recommended course of action by an appointed decision maker within an organizational setting. This type of conflict can occur when, for example, an organizational leader recommends moving forward with a strategic initiative even though available performance metrics indicate that another course of action may be warranted. In these situations, evaluators must consider not only the available evidence but also the communication surrounding the recommendation. Specifically, the study examined perceived presenter expertise and narrative coherence as two communication characteristics that may shape perceived decision value (PDV), or the extent to which evaluators perceive a proposed decision as strategically valuable and worthy of organizational support. The findings demonstrate that perceived presenter expertise and narrative coherence were both statistically significant positive predictors of PDV. Perceived presenter expertise remained statistically significant when narrative coherence was included in the regression model, and 35 narrative coherence remained statistically significant when perceived presenter expertise was included. Findings further demonstrate that the effects of experimentally manipulated expertise and narrative coherence were not independent of one another. Instead, the influence of one cue depended partly on the level of the other. Taken together, the results indicate that how a recommendation was communicated and who appeared to be communicating it were significantly related to how participants evaluated the recommendation, even though the contradictory analytic evidence presented in the scenario remained constant across experimental conditions. Perceived Presenter Expertise and Decision Value RQ1 asked how perceived presenter expertise influences perceptions of decision value when analytic evidence contradicts a decision-maker’s recommended business action. The findings demonstrate a positive relationship between perceived presenter expertise and PDV. Participants who viewed the presenter as more knowledgeable, experienced, competent, and qualified also tended to evaluate the proposed decision more favorably. In the multiple linear regression, perceived presenter expertise was a statistically significant positive predictor of PDV (B = .464, SE = .059, β = .452, t = 7.92, p < .001, 95% CI [.349, .580]), after accounting for narrative coherence. The standardized coefficient for perceived presenter expertise (β = .452) was larger than that for narrative coherence (β = .371), making perceived expertise the primary predictor in this model. The finding is consistent with source credibility research suggesting that judgments about a communicator can influence how receivers evaluate the communicator’s message. Source credibility refers broadly to perceptions of characteristics that make a communicator appear believable or qualified, with expertise representing an important component of those judgments. 36 Foundational research established relationships between communicator credibility and message acceptance, while subsequent research identified perceived expertise as a measurable dimension of source credibility and continued to connect expertise and credibility with persuasive outcomes (Hovland & Weiss, 1951; Hovland et al., 1953; Ohanian, 1990; Pornpitakpan, 2004). The findings of this study extend that logic into a somewhat different workplace situation. Participants were not evaluating a recommendation in the absence of competing information. Instead, they were presented with analytic evidence in the fictional organizational scenario that contradicted the presenter’s recommended course of action. Even under those circumstances, perceptions of the presenter’s expertise remained strongly related to how much value participants assigned to the decision. This suggests that perceived expertise may function as an interpretive cue when evaluators are confronted with conflicting information rather than serving only as a general assessment of whether the communicator appears credible. The interpretation is consistent with the Heuristic-Systematic Model (HSM). HSM proposes that individuals can use heuristic cues alongside more systematic consideration of message content rather than relying exclusively on one form of processing (Chaiken, 1980; Chaiken et al., 1989). Prior research further suggests that source characteristics can influence how evidence is interpreted when information is ambiguous because heuristic cues can shape systematic processing rather than necessarily replacing it (Chaiken & Maheswaran, 1994; Chen & Chaiken, 1999). In an organizational setting, this creates an important distinction between having expertise and being perceived as having expertise. The present study measures the latter. Previous research on group decision-making has similarly suggested that influence can be granted to individuals who appear competent or knowledgeable, even when perceived 37 competence does not correspond perfectly with objective ability (Berger et al., 1972; Driskell & Mullen, 1990; Anderson & Kilduff, 2009). This study reinforces the importance of that distinction; the perceived expertise surrounding a recommendation may become part of how the recommendation itself is evaluated. Narrative Coherence and Decision Value RQ2 asked how narrative coherence influences perceptions of decision value when analytic evidence contradicts a decision-maker’s recommended business action. The findings demonstrate a similar positive relationship between narrative coherence and PDV. Participants who perceived the recommendation as clearer, more logically structured, better organized, and more complete also tended to assign greater value to the proposed decision. In the multiple linear regression, narrative coherence remained a statistically significant positive predictor of PDV after accounting for perceived presenter expertise (B = .389, SE = .060, β = .371, t = 6.50, p < .001, 95% CI [.271, .507]). Together, perceived presenter expertise and narrative coherence explained 56.9% of the variance in PDV (R² = .569). This finding is also consistent with research on narrative cognition. Although HSM serves as the primary theoretical framework for the present study, the literature reviewed for this research identified Pennington and Hastie’s Story Model as a useful complementary framework for understanding how individuals organize and evaluate complex or conflicting information. Whereas HSM helps explain how evaluators may rely on communicative cues alongside more systematic consideration of information, the Story Model focuses on how individuals construct organized accounts that connect events, motivations, evidence, and outcomes into an understandable whole. Rather than evaluating each piece of information in isolation, individuals may organize evidence into a narrative representation and assess how well the available 38 information fits that representation (Pennington & Hastie, 1992, 1993; Hastie & Pennington, 2000). The organizational context of this study makes that process relevant. Strategic decisions frequently involve incomplete, ambiguous, or competing information, requiring organizational members to construct interpretations rather than retrieve an objectively obvious answer. Narrative and sensemaking research suggest that organizational actors use structured accounts to connect past events, present circumstances, and anticipated outcomes when interpreting strategic situations (Weick, 1995; Sonenshein, 2010; Kaplan & Orlikowski, 2013). In this study, a logically structured explanation may have provided participants with an interpretive framework that made the recommendation appear more sensible, even though the analytic evidence presented in the scenario challenged it. The findings do not establish that narrative coherence led participants to ignore the contradictory analytic evidence. The study did not measure whether participants disregarded, remembered, or interpreted individual pieces of information from the scenario. The finding is narrower: narrative coherence was significantly associated with the value assigned to a recommendation presented alongside contradictory evidence. Specifically, narrative coherence was a statistically significant predictor of PDV (β = .371, p < .001), even after perceived presenter expertise was included in the regression model. The mechanism underlying that relationship remains an important question for future research, as this study cannot determine whether coherence increased the recommendation’s perceived plausibility, made the information easier to process, or altered how participants interpreted and weighted the contradictory evidence. Distinguishing among these explanations would clarify how narrative coherence 39 influences the evaluation of strategic decisions when recommendations conflict with analytic evidence. The interaction between expertise and narrative coherence RQ3 asked how perceived expertise and narrative coherence interact to influence perceptions of decision value when analytic evidence contradicts a decision-maker’s recommended business action. H3 predicted that recommendations combining high presenter expertise with high narrative coherence would be evaluated as having greater decision value than those combining low presenter expertise with high narrative coherence. Although the 2x2 factorial ANOVA identified a statistically significant interaction between presenter expertise and narrative coherence (F(1, 255) = 6.20, p = .013, ηp² = .024), the pattern did not support H3 as originally predicted. The high-expertise/high-narrative-coherence condition did not yield the highest PDV. Instead, the lowest PDV occurred when both expertise and narrative coherence were low. Strengthening either expertise or narrative coherence was associated with higher PDV when the other cue was weak, but combining two strong cues did not produce a statistically significant additional increase. The pattern suggests a plausible compensatory relationship between the two communicative cues. In this context, a compensatory relationship means that strength in one cue may partially offset weakness in the other when evaluators form a judgment about the recommendation. Consider, for example, an experienced executive presenting a strategic recommendation that is poorly organized or difficult to follow. Evaluators may still assign greater value to the recommendation because the executive’s perceived experience and knowledge provide a reason to give the recommendation credibility despite the weak explanation. Conversely, a recommendation delivered by a relatively inexperienced manager 40 may receive a stronger evaluation when the explanation is clear, logically structured, and connects the available information to the proposed action. The follow-up comparisons support this interpretation. When narrative coherence was low, high presenter expertise resulted in a significantly greater PDV than low presenter expertise, with a mean difference of 0.79 (p = .001). When narrative coherence was high, however, the difference between high- and low-presenter expertise was not statistically significant (p = .793). The reverse pattern also occurred. When presenter expertise was low, high narrative coherence resulted in significantly greater PDV than low narrative coherence, with a mean difference of 1.05 (p < .001). When presenter expertise was high, the difference between high and low narrative coherence was not statistically significant (p = .407). One plausible interpretation is that evaluators may not require every communicative cue surrounding a recommendation to be strong. Instead, a strong cue in one area may provide sufficient interpretive support for the recommendation that strengthening another cue produces diminishing returns. A highly expert presenter may give evaluators a reason to view a poorly structured recommendation more favorably because the presenter appears to possess the experience or knowledge necessary to support it. Conversely, a highly structured and understandable explanation may provide sufficient internal logic, making lower perceived expertise less salient because the recommendation itself appears to make sense. This interpretation is compatible with HSM’s central proposition that heuristic and systematic influences may coexist during judgment. HSM does not require individuals to use only one cue or one processing mode; heuristic information can operate alongside more effortful evaluation and can influence how other information is interpreted (Chaiken, 1980; Chaiken et al., 1989; Chen & Chaiken, 1999). The present findings may extend that reasoning by suggesting 41 that different communicative cues can also compensate for one another when evaluators form judgments under conditions of conflicting evidence. The compensatory explanation should, however, remain an interpretation rather than a definitive conclusion. This study demonstrates an interaction in which the statistical effect of one manipulated cue varied with the level of the other cue. It did not measure whether participants consciously substituted expertise for narrative coherence or narrative coherence for expertise. Future research could test this interpretation by varying additional communication cues, measuring participants’ reasoning following exposure to the recommendation, or examining whether the same interaction occurs across different types of strategic decisions. Theoretical Implications This study was motivated by a practical problem in organizational decision-making: the availability of analytic evidence does not necessarily ensure that the evidence will determine how a strategic recommendation is evaluated. When a recommendation conflicts with available data, decision-makers must interpret not only the evidence itself but also the communication through which that evidence and recommendation are presented. The present study examined whether two communicative perceptions, presenter expertise and narrative coherence, help explain how strategic recommendations are evaluated under those conditions. HSM provides a theoretical framework for examining this problem because it recodnizes that individuals can evaluate information through systematic consideration of evidence while also relying on heuristic cues (Chaiken, 1980; Chaiken et al., 1989). The findings demonstrate that perceived presenter expertise and narrative coherence were both associated with perceived decision value when participants evaluated a recommendation presented alongside contradictory analytic evidence. The significant interaction further suggests that these cues did not operate 42 independently or additively. Instead, the value associated with one cue depended on the strength of the other, with a strong cue appearing to compensate when the other was weak. The processing measures add complexity to this interpretation. Heuristic processing was positively correlated with PDV (r = .302, p < .001), whereas systematic processing was not significantly correlated with PDV (r = -.o44, p = .491). These findings do not establish that participants favored the recommendation because they relied on heuristic processing or that systematic consideration of the evidence did not occur. HSM allows heuristic and systematic processing to operate concurrently, and prior research suggests that their relationships with judgment can vary across decision contexts (Trumbo, 2002). The present findings may therefore indicate that the specific communicative perceptions examined in this study were more closely connected to decision value than participants’ broader self-reports of how they processed the information. One plausible explanation is that communicative cues shaped evaluation without participants being fully aware of the extent to which those cues influenced their judgments. Another is that the self-report processing measures did not capture the precise cognitive processes occurring while participants evaluated the vignette. The present study cannot distinguish between these explanations, but the findings suggest that understanding strategic decision evaluation requires attention not only to the analytic evidence available to decision-makers, but also to the communicative context through which that evidence is interpreted. Influence of Managerial Experience The supplemental managerial-experience analysis adds another layer to the findings. Participants with and without managerial experience did not significantly differ in perceived presenter expertise (Welch's t(224.82) = 1.81, p = .072). They did, however, differ in their 43 perceptions of narrative coherence. Participants without managerial experience rated the narrative as more coherent (M = 4.93, SD = 1.31) than participants with current or previous managerial experience (M = 4.40, SD = 1.36, Welch's t(213.08) = 3.06, p = .003). One plausible explanation is that managerial experience changes the standards evaluators apply to strategic explanations. Individuals who have participated in organizational decisionmaking may expect stronger logical connections, clearer justifications, or greater alignment between evidence and recommendations before evaluating an explanation as well-structured. Organizational sensemaking research supports the broader idea that interpretation is shaped by experience and existing frameworks through which organizational actors make sense of events (Weick, 1995; Weick et al., 2005). The interpretation of this finding is limited because managerial experience was examined in a supplemental analysis rather than as part of the original research question or hypothesis. Managerial experience was also not experimentally assigned, and the comparison does not establish why the groups differed. Managerial experience may overlap with age, years of work experience, organizational role, education, or other characteristics that were not isolated in this comparison. Future research could examine managerial experience as a primary variable and account for related characteristics to determine whether professional experience systematically influences how organizational members evaluate the narrative structure of strategic recommendations. Practical Implications For organizational communication, the results suggest that presenting accurate analytic information may not, by itself, determine how a strategic recommendation is evaluated. The communicator and the structure of the explanation surrounding that information can contribute to 44 the perceived value of the proposed decision. This aligns with organizational communication and sensemaking scholarship that treats communication not only as a channel through which information travels but also as part of the process through which organizational members construct meaning around strategic issues (Weick, 1995; Maitlis & Christianson, 2014; Ocasio et al., 2018). These findings have implications for both those presenting strategic recommendations and those responsible for evaluating them. Communicators advocating for an evidence-supported recommendation should recognize that perceived expertise and a clear, logically structured explanation can help stakeholders interpret complex information. At the same time, evaluators should recognize that strong communicative cues can shape their assessment of a recommendation, even when the underlying evidence raises questions about the proposed action. Research on source credibility provides one explanation for why communicator characteristics may contribute to this process. Source credibility concerns the characteristics of a communicator that influence how believable or persuasive that communicator appears, with perceived expertise representing one important dimension (Hovland & Weiss, 1951; Ohanian, 1990; Pornpitakpan, 2004). Research on narrative cognition similarly indicates that the structure and organization of information can influence how people interpret complex evidence (Pennington & Hastie, 1992). These bodies of research provide a basis for understanding why both communicator characteristics and the organization of a message may contribute to evaluations of strategic recommendations. For decision makers, the practical lesson is therefore not to eliminate expertise or narrative structure from organizational communication. Both are valuable features of effective professional communication. Rather, organizations may benefit from deliberately separating two 45 questions during strategic evaluation: Does this recommendation make sense as presented? Does the available evidence support it? The findings of this study suggest that these judgments may be more closely connected than organizational decision-makers assume. Limitations and future research Limitations should be considered when interpreting these findings. First, this study relied on a fictional vignette to provide control over the information participants received. Still, it cannot fully capture the stakes, political dynamics, interpersonal histories, time pressures, and consequences of real organizational decision-making. The findings therefore demonstrate how participants evaluated the scenario presented in this study rather than establishing that executives or managers would necessarily behave identically when making consequential decisions in their own organizations. Second, this study used convenience sampling and included participants with varying levels and types of organizational experience. This diversity provides perspectives from different organizational roles, but it also limits the extent to which the results can be generalized to a particular population of senior decision-makers. Future studies could replicate the experiment using samples comprised of executives, managers, analysts, or other organizational groups and compare whether communicative cues operate differently across roles. The study also focused on only two communicative cues. Expertise and narrative coherence were selected based on prior research involving source credibility, HSM, narrative cognition, and organizational communication. Other cues, including confidence, authority, message fluency, visual presentation, social consensus, and organizational status, may also affect how contradictory evidence is evaluated. Future research could examine whether the compensatory pattern identified here extends to other combinations of communicative cues. 46 Finally, future studies should more closely examine how contradictory analytic evidence is handled during the evaluation process. The present study measured participants’ evaluations of the decision. Still, it did not determine whether participants ignored the contradictory evidence, discounted its credibility, reinterpreted it, remembered it differently, or believed the recommendation remained valuable despite the conflict. Distinguishing among these mechanisms would help answer the broader question motivating this research: not only whether communication influences evaluations when evidence and recommended action disagree, but how communicative cues shape what evaluators do with evidence that challenges a proposed decision. Conclusion This study began with a practical problem common to organizational decision-making: analytic evidence does not always determine how a strategic recommendation is evaluated. The findings demonstrate that communicative perceptions contribute to this evaluation. Perceived presenter expertise and narrative coherence were both statistically significant positive predictors of PDV, and experimentally manipulating presenter expertise and narrative coherence produced a significant difference in how participants evaluated a recommendation. At the same time, the underlying contradictory analytic evidence remained constant. The interaction between two communicative cues provides a particularly important contribution. Presenter expertise and narrative coherence did not produce the additive pattern predicted in H3. Instead, either cue was associated with stronger evaluations when the other was weak, while combining high expertise with high narrative coherence produced no statistically significant additional increase. This pattern suggests a plausible compensatory relationship in which strength in one communicative cue may partially offset weakness in another when 47 evaluators assign value to a strategic recommendation. 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Pearson. 56 Appendix A Experimental Stimuli High Expertise / High Narrative Coherence Condition A mid-sized distribution company is considering a proposed investment in a new logistics software platform. The recommendation is being presented by the company's Vice President of Operations, who has 18 years of supply chain experience and has successfully led multiple system implementation projects across the organization. The presenter explains that the company's current logistics system was built for a smaller operation and has become increasingly difficult to manage as shipping volume has grown. Over the past three years, shipment delays have become more frequent, customer complaints about visibility have increased, and internal teams have spent more time manually correcting routing problems. The proposed platform is intended to streamline routing, automate carrier selection, and improve delivery visibility. However, the company's analytics team has reported that the projected three-year return on investment is only 2.1%, which falls well below the organization's usual benchmark for technology investments. The report also indicates that the implementation process is likely to create temporary workflow disruptions, and current customer survey data suggests that delivery visibility is not the company's top client concern. Despite these findings, the presenter recommends moving forward with the investment, arguing that the software addresses a growing operational problem, positions the company for future scale, and will create long-term strategic value that is not fully captured by the current analytics model. 57 High Expertise / Low Narrative Coherence Condition A mid-sized distribution company is considering a proposed investment in a new logistics software platform. The recommendation is being presented by the company's Vice President of Operations, who has 18 years of supply chain experience and has successfully led multiple system implementation projects across the organization. The presenter states that the company has experienced some shipment delays and that the current system has become harder to manage. The proposed platform includes routing tools, carrier automation, and delivery visibility functions. The presenter also notes that future scale is important and that operational efficiency should remain a priority for the company. However, the company's analytics team has reported that the projected three-year return on investment is only 2.1%, which falls well below the organization's usual benchmark for technology investments. The report also indicates that the implementation process is likely to create temporary workflow disruptions, and current customer survey data suggests that delivery visibility is not the company's top client concern. Despite these findings, the presenter recommends moving forward with the investment and argues that the software could still be useful to the organization. The presenter states that there are several reasons to proceed, that the platform may help in the future, and that it remains a reasonable strategic option even if the current analytics do not strongly support it. Low Expertise / High Narrative Coherence Condition A mid-sized distribution company is considering a proposed investment in a new logistics software platform. The recommendation is being presented by an operations project coordinator who has been with the company for one year and has limited direct experience overseeing 58 enterprise-level system implementation projects. The presenter explains that the company's current logistics system was built for smaller operation and has become increasingly difficult to manage as shipping volume has grown. Over the past three years, shipment delays have become more frequent, customer complaints about visibility have increased, and internal teams have spent more time manually correcting routing problems. The proposed platform is intended to streamline routing, automate carrier selection, and improve delivery visibility. However, the company's analytics team has reported that the projected three-year return on investment is only 2.1%, which falls well below the organization's usual benchmark for technology investments. The report also indicates that the implementation process is likely to create temporary workflow disruptions, and current customer survey data suggests that delivery visibility is not the company's top client concern. Despite these findings, the presenter recommends moving forward with the investment, arguing that the software addresses a growing operational problem, positions the company for future scale, and will create long-term strategic value that is not fully captured by the current analytics model. Low Expertise / Low Narrative Coherence Condition A mid-sized distribution company is considering a proposed investment in a new logistics software platform. The recommendation is being presented by an operations project coordinator who has been with the company for one year and has limited direct experience overseeing enterprise-level system implementation projects. The presenter states that the company has experienced some shipment delays and that the current system has become harder to manage. The proposed platform includes routing tools, carrier 59 automation, and delivery visibility functions. The presenter also notes that future scale is important and that operational efficiency should remain a priority for the company. However, the company's analytics team has reported that the projected three-year return on investment is only 2.1%, which falls well below the organization's usual benchmark for technology investments. The report also indicates that the implementation process is likely to create temporary workflow disruptions, and current customer survey data suggests that delivery visibility is not the company's top client concern. Despite these findings, the presenter recommends moving forward with the investment and argues that the software could still be useful to the organization. The presenter states that there are several reasons to proceed, that the platform may help in the future, and that it remains a reasonable strategic option even if the current analytics do not strongly support it. 60 Appendix B Survey Instrument The following reproduces the survey instrument administered to participants in Qualtrics. Experimental vignettes are presented separately in Appendix A. Participant Consent and Eligibility Consent Form IRB#: IRB-AY26-27-1 You are invited to participate in a research study examining how individuals evaluate strategic business recommendations and organizational decision-making situations. The purpose of this study is to better understand how communication features, such as perceived expertise and message structure, influence how individuals evaluate strategic recommendations in organizational contexts. If you agree to participate, you will be asked to read a brief organizational decision-making scenario and answer questions about the scenario. The survey is expected to take an average of 10 minutes to complete. There are no known risks associated with participation beyond those encountered in normal daily activities or routine decision-making situations. Some participants may experience minor mental fatigue from reading and evaluating written scenarios. While there may be no direct benefit to you, this research may contribute to a better understanding of communication and decision-making processes within organizations. Your responses will remain anonymous. No personally identifying information will be collected as part of this study. Data collected will be used solely for academic research purposes and may be included in scholarly presentations, publications, or a graduate thesis. Participation in this study is voluntary. You may refuse to participate or stop participation at any time without penalty or loss of benefits to which you are otherwise entitled. If you have questions about this 61 research study, you may contact: Paul Castleberry Jr., paulcastleberry1@mail.weber.edu. You may also contact the Weber State University Institutional Review Board (IRB) for questions regarding your rights as a research participant. By selecting “I agree” and proceeding to the survey, you confirm that: You are at least 18 years of age, you have read the information above, and you voluntarily agree to participate in this study. If you have questions about this research, please contact: Weber State University Faculty Advisor: Dr. Aaron Atkins, datkins@weber.edu Paul Castleberry Jr., Master of Professional Communication, paulcastleberry1@mail.weber.edu For questions regarding your rights as a research participant, contact: Weber State University Institutional Review Board (Use the contact information provided by Weber's IRB office.) Consent: Do you agree to participate in this study? Response options: Yes, I agree to participate; No, I do not agree to participate. Survey logic: Participants selecting “No, I do not agree to participate” were directed to the end of the survey. Perceived Decision Value Please indicate your level of agreement with the following statements. Response scale: Strongly disagree; Disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Agree; Strongly agree. 1. I would support implementing this recommendation. 2. This decision would likely benefit the organization. 3. The recommendation appears well justified. 4. The recommendation would be useful for organizational decision-making. 5. Overall, this recommendation has strong decision value. 62 Perceived Presenter Expertise Please indicate your level of agreement with the following statements. Response scale: Strongly disagree; Disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Agree; Strongly agree. 1. The presenter appears highly knowledgeable. 2. The presenter seems experienced in this area. 3. The presenter appears competent. 4. The presenter appears qualified to make this recommendation. Narrative Coherence Please indicate your level of agreement with the following statements. Response scale: Strongly disagree; Disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Agree; Strongly agree. 1. The explanation is clear and logically structured. 2. The reasoning is easy to follow. 3. The explanation connects the evidence to the recommendation clearly. 4. The explanation feels complete. 5. The recommendation follows logically from the explanation. Systematic Processing Please indicate your level of agreement with the following statements. Response scale: Strongly disagree; Disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Agree; Strongly agree. 1. I thought carefully about the information in the proposal. 2. I made connections between the proposal and other things I know about business decisions. 63 3. I thought about how the evidence related to the recommendation. 4. I tried to understand the importance of the information for the organization. 5. I carefully considered whether the recommendation aligned with the available data. Heuristic Processing Please indicate your level of agreement with the following statements. Response scale: Strongly disagree; Disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Agree; Strongly agree. 1. I skimmed through the proposal. 2. I did not spend much time thinking about the information. 3. I did not think carefully about the arguments presented in the proposal. 4. I relied mostly on my overall impression of the recommendation. 5. The recommendation seemed reasonable regardless of the supporting evidence. Exploratory Evaluation Measures When evaluating the recommendation, please indicate the extent to which each factor influenced your judgment: Response scale: Not at all; Very little; A little; A moderate amount; A fair amount; A great deal; A very great deal. 1. The presenter’s expertise influenced my evaluation of the recommendation. 2. The clarity and organization of the explanation influenced my evaluation. 3. The analytic evidence influenced my evaluation of the recommendation. 4. The overall quality of the presentation influenced my judgment. Demographic and Background Questions What is your age? (numbers only) 64 How many years of full-time work experience do you have? (numbers only) Have you ever held a position with managerial or supervisory responsibilities? Response options: No; Yes, currently; Yes, previously. How would you describe your political ideology? Response options: Very conservative; Conservative; Slightly conservative; Moderate; Slightly liberal; Liberal; Very liberal; Prefer not to answer. Please indicate your level of agreement with the following statements. Response scale: Strongly disagree; Disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Agree; Strongly agree. 1. Senior leaders are generally in the best position to make strategic decisions. 2. Employees should be willing to challenge management decisions when evidence suggests a better course of action. 3. Experience and organizational position are important indicators of decision quality. Which of the following best describes your current role? Response options: Student; Individual contributor; Manager; Executive; Other. What industry best describes your current or most recent role? Response options: Business/Consulting; Manufacturing/Operations; Technology; Healthcare; Education; Other. What is your highest level of education? Response options: Undergraduate; Bachelor's Degree; Master's Degree; Doctoral Degree; Other. What is your gender? Response options: Male; Female; Non-binary / third gender; Prefer not to say. 65 Appendix C Tables and Figure Table 1 Descriptive Statistics, Internal Consistency, and Correlations Among Study Variables Variable M SD α 1 2 3 4 1. Perceived 4.43 1.43 .935 — Decision Value 2. Presenter 4.90 1.41 .943 .708*** — Expertise 3. Narrative 4.60 1.37 .922 .675*** .675*** — Coherence 4. Systematic 5.45 .94 .881 −.044 .047 .054 — Processing 5. Heuristic 3.75 1.33 .855 .302*** .231*** .303*** −.504* Processing Note. M = mean; SD = standard deviation; α = Cronbach's alpha. p < .001. 5 — Table 2 Multiple Linear Regression Predicting Perceived Decision Value Predictor B SE β t p 95% CI Presenter .464 .059 .452 7.92 < .001 [.349, .580] Expertise Narrative .389 .060 .371 6.50 < .001 [.271, .507] Coherence Note. Dependent variable = Perceived Decision Value. R² = .569; adjusted R² = .566. Overall model: F(2, 242) = 159.85, p < .001. Table 3 Factorial ANOVA of Presenter Expertise and Narrative Coherence on Perceived Decision Value Effect F df p ηp² Presenter Expertise 4.95 1, 253 .027 .019 Narrative Coherence 14.38 1, 253 < .001 .054 Expertise × Narrative 6.71 1, 253 .010 .026 Coherence Note. Dependent variable = Perceived Decision Value. ηₚ² = partial eta squared. 66 Figure 1 Interaction Between Presenter Expertise and Narrative Coherence on Perceived Decision Value 7 Perceived Decision Value 6 5 4 3 2 1 Low Expertise High Expertise Presenter Expertise Low Narrative Coherence High Narrative Coherence Note. Values represent mean Perceived Decision Value scores across the four experimental conditions.