Introduction

In today’s competitive business environment, it has become increasingly important for organizations to provide value to their various stakeholders. For instance, at the Colgate-Palmolive Company, caring about stakeholders is a fundamental core value (Colgate 2015a). As such, Colgate is committed to attending to and addressing the interests of its customers, employees, suppliers, shareholders, communities, and other stakeholders since they are crucial to Colgate’s success and future growth (Colgate 2015b). This focus on satisfying multiple stakeholders has also been a priority to Zappos. In a statement released shortly after Zappos was acquired by Amazon, Zappos CEO, Tony Hsieh, was reassuring: “Our mission remains the same: delivering happiness to all of our stakeholders” (Zappos.com 2015). These two examples illustrate the current business reality where organizations have to constantly interact with a wide set of stakeholders and manage these stakeholder relationships effectively to create value and achieve success (Freeman et al. 2007).

In light of this significant trend in the business environment, a major shortcoming of the marketing literature in general, and the strategic marketing stream in particular, has been that studies traditionally concentrate on examining single stakeholder relationships, while disregarding the implications of multiple stakeholders for the marketing function (e.g., Hult et al. 2011). In particular, scholarly work has heavily focused on customers as the most important stakeholder and the only target of marketing actions (e.g., Ferrell et al. 2010; Hult 2011a). In this regard, “stakeholder marketing” (e.g., Bhattacharya and Korschun 2008) represents a new frontier in marketing (Achrol and Kotler 2012), one that moves beyond a more narrow view on, for example, market orientation (Hult 2011a), and captures a broader set of stakeholders that can be managed by marketing managers (Hult et al. 2011). Specifically, stakeholder marketing refers to the activities and processes for creating value through the organization’s various stakeholder relationships (Hult et al. 2011). Due to the stakeholder approach’s recent conception (e.g., Chabowski et al. 2011), the limited work on it in the strategic marketing literature has been largely conceptual (e.g., Hillebrand et al. 2015) and exploratory (e.g., Mish and Scammon 2010). While progress has been made in understanding the elements of stakeholder marketing (e.g., Bhattacharya 2010; Hult et al. 2011), identifying the relevant stakeholders of marketing (e.g., Hult et al. 2011), and distinguishing the stakeholder marketing perspective from the traditional marketing perspective (Hillebrand et al. 2015), important gaps remain in the literature. For example, it has been proposed that stakeholder marketing requires organizations to be “dedicated to learning about and addressing stakeholder issues” (Ferrell et al. 2010, p. 95). However, not much is known about how organizations learn about the various stakeholders’ requests or how stakeholder-related knowledge affects the type of marketing actions the organization takes. Further, there is a paucity of empirical work that provides evidence of the effects of stakeholder marketing. As a result, organizations that are interested in employing stakeholder marketing have limited direction on how to implement it and on what its effects are.

Meanwhile, the field of management has solidly focused on stakeholders for about three decades (e.g., Freeman 1984). Accordingly, management research on stakeholders has made considerable progress examining such areas as stakeholder salience, stakeholder management strategies, and the performance outcomes of stakeholder management (e.g., Laplume et al. 2008). Notably, a major contributing factor to a better understanding of the stakeholder approach has been the development of stakeholder theory (e.g., Donaldson and Preston 1995; Jones 1995; Jones and Wicks 1999). Broadly, stakeholder theory encourages managers to incorporate a wider range of stakeholders in the organization’s value creation (e.g., Freeman et al. 2004). More specifically, a prevalent view within the theory, the instrumental perspective, predicts that those organizations which develop mutually trusting and cooperative relationships with their stakeholders will gain a competitive advantage (Jones 1995). Despite the significant advances that can be attributed to stakeholder theory, the theory lacks sufficient structure, and so its ability to guide managerial behavior is limited (e.g., Jensen 2002; Sundaram and Inkpen 2004). For instance, it does not provide much insight into the organizational factors that lead organizations to attend to the different stakeholders or to implement stakeholder practices (cf. Bundy et al. 2013). Moreover, empirical work in the strategic management literature has failed to address these shortcomings.

Against this backdrop, the purpose of this paper is to empirically examine the impact of stakeholder-focused organizational learning on stakeholder-focused behavioral action (responsiveness, innovation, and imitation) and, subsequently, on performance outcomes. To this end, stakeholder theory is integrated with insights from the organizational learning literature (e.g., Huber 1991). The literature on organizational learning is particularly useful because it can explain how organizations become informed about their stakeholders’ demands. In addition, this approach assists in understanding how this stakeholder-related knowledge can, in turn, direct managerial action toward responding to the various stakeholders and, subsequently, create value. Broadly, organizational learning is the development of new knowledge that facilitates behavior change (e.g., Slater and Narver 1995). Uniquely different from previous learning studies in marketing (e.g., Sinkula et al. 1997), our study deals with stakeholder-focused organizational learning. This type of learning centers on gaining new stakeholder-related knowledge that has the potential to influence an organization’s behaviors toward its stakeholders. We concentrate on the organization’s knowledge about and actions toward its primary stakeholders (i.e., customers, employees, suppliers, shareholders, regulators, and the community) since these are essential for the continued success of the organization (e.g., Clarkson 1995). Based on the extant literature on organizational learning (e.g., Huber 1991), four learning processes associated with stakeholder-focused organizational learning are considered in this research to affect stakeholder-focused responsiveness and ultimately organizational performance: knowledge acquisition, information distribution, information interpretation, and organizational memory (cf. Sinkula 1994). Taken further, stakeholder-focused responsiveness is defined as the organization’s propensity to take action in response to the stakeholder-related knowledge that is acquired, distributed, interpreted, and stored (cf. Kohli and Jaworski 1990). As a result, this perspective allows us to evaluate the processes of organizational learning in a stakeholder-centered context.

The basic value chain of organizational learning (learning to behavioral action to outcomes) involves organizations that learn in an environment that consists largely of other organizations that are also learning (Levitt and March 1988). As such, contrary to prior learning models, we present a more complete model of organizational learning that accounts both for the alternative mechanisms that organizations use to acquire information about stakeholders and for the way these mechanisms combine (e.g., Lieberman and Asaba 2006). Specifically, organizations can vary in their reliance on obtaining information from their own experience (experiential learning), from the observed behavior of their competitors (vicarious learning), and/or from direct communication with their peers (contact learning) (e.g., Lieberman and Asaba 2006; Ordanini et al. 2008). We contend that the way organizations acquire information leads them to respond differently toward their stakeholders, varying in the degree of innovation and imitation in their stakeholder practices. To complete the fundamental organizational learning value chain, we also examine the links between stakeholder-focused responsiveness, innovation, and imitation on one hand, and organizational performance on the other.

By developing a conceptual link between stakeholder theory and the organizational learning literature, this research makes a number of contributions. First, we identify stakeholder-focused organizational learning as a key antecedent of stakeholder-focused responsiveness. This is an important contribution to stakeholder research, as previous studies largely ignore the internal, organizational drivers of stakeholder-focused responsiveness, concentrating instead on the external factors (cf. Bundy et al. 2013). Second, relative to prior studies, we build a more realistic model of organizational learning by distinguishing among three mechanisms organizations employ to gather stakeholder-related knowledge and evaluating their effects on behavioral action (responsiveness, innovation, and imitation). Third, in assessing the performance implications of innovative versus imitative stakeholder practices, we extend stakeholder theory by showing that merely responding to stakeholders does not guarantee superior performance; how the organization responds matters. Lastly, at a broader level, we contribute to stakeholder marketing by providing a fine-grained explanation of the processes involved in learning about and responding to stakeholders’ interests by providing empirical evidence of the performance outcomes of the stakeholder approach.

As such, the paper is structured as follows. Next, we review stakeholder theory and the literature on organizational learning. We then introduce the proposed model, develop the research hypotheses, describe the methodology, and present the results. In the last section, we discuss the implications of this study and directions for future research.

Conceptual background

Stakeholder theory

Stakeholder theory views the organization as the center of a system of stakeholder relationships, with each stakeholder having different interests and expectations (e.g., Clarkson 1995). Broadly, it emphasizes the importance for organizations to actively deal with their stakeholders (Freeman 1984). Hence, stakeholder theory represents a conceptual shift from the shareholder paradigm, broadening management attention from a single-minded focus on shareholders to the inclusion of the organization’s various stakeholders in managerial decision making. Beyond the fundamental thesis that organizations need to pay simultaneous attention to a broad set of stakeholders, three mutually-supportive perspectives explain different aspects of the theory (Donaldson and Preston 1995). With its moral foundation, the normative perspective asserts that organizations ought to attend to the interests of all their stakeholders, as they all have intrinsic value. The descriptive perspective of stakeholder theory characterizes how organizations deal with their stakeholder relationships. Finally, the instrumental perspective makes a connection between the effective management of multiple stakeholder relationships and the achievement of organizational objectives. In particular, it proposes that organizations which develop stakeholder relationships founded on mutual trust and collaboration will gain a competitive advantage (Jones 1995). Since this study focuses on predicting the outcomes that result from certain stakeholder practices, we adopt the instrumental perspective of stakeholder theory.

A stakeholder is “any group or individual who can affect or is affected by the achievement of the organization’s objectives” (Freeman 1984, p. 46). For a particular group or individual to be identified as a stakeholder, it must possess at least one of the following key attributes: power, legitimacy, or urgency (Mitchell et al. 1997). Power is the extent to which a stakeholder can impose its will on an organization as a result of its access to coercive, utilitarian, or normative means. Legitimacy refers to the degree to which the actions of a stakeholder are perceived to be desirable, proper, or appropriate within a social system (Suchman 1995). Lastly, urgency is defined as the extent to which stakeholder requests demand immediate attention (Mitchell et al. 1997). The level of managerial attention directed toward a particular stakeholder group is a function of the number of attributes that the stakeholder is perceived to have. As such, organizations will give priority to stakeholders who possess all three attributes vis-à-vis those who have only one or two attributes.

In line with this framework of stakeholder identification (Mitchell et al. 1997), managers tend to prioritize their primary stakeholders, in other words, those who are powerful and have urgent, legitimate demands (Godfrey et al. 2009) and who are therefore essential for the continued success of the organization (Clarkson 1995). Primary stakeholder groups consist of customers, employees, suppliers, shareholders, regulators, and the community (Hult et al. 2011). On the other hand, secondary stakeholders exhibit legitimacy but lack both power and urgency (Godfrey et al. 2009). As a result, managers have no pressure to respond to secondary stakeholders (e.g., Mitchell et al. 1997), although they often choose to do so because these groups can influence the primary stakeholders (Freeman et al. 2007). Secondary stakeholders include special interest groups, consumer advocate groups, and the media. Given that organizations may not be motivated to respond to the claims of secondary stakeholders, in our study, we limit our focus to organizations’ knowledge of their primary stakeholders and their subsequent organizational responses.

Research in marketing has implicitly applied stakeholder theory by examining single stakeholder relationships, but it has failed to adopt a holistic perspective (e.g., Hillebrand et al. 2015; Hult et al. 2011). In particular, scholarly work has traditionally concentrated on customers as the most important stakeholder (e.g., Hult 2011a). The development of research in the market orientation stream exemplifies this. While early advocates of the concept stressed that a market-oriented organization is “sensitive and responsive to any stakeholder or issue that may affect its long-run performance” (Narver and Slater 1990, p. 34), researchers have long embraced a more limited scope of market orientation, generally emphasizing the customer. This approach to marketing research, which heavily focuses on customers as the sole stakeholder, is unfortunate since it does not reflect the changed business environment where addressing a broader set of stakeholders is pivotal for success (e.g., Smith et al. 2010). For this reason, marketing scholars are increasingly advocating for more expansive research that adopts a stakeholder-oriented view (e.g., Ferrell et al. 2010; Maignan and Ferrell 2004).

The need for marketing research that considers multiple stakeholders has resulted in the conception of stakeholder marketing (e.g., Bhattacharya and Korschun 2008). Stakeholder marketing refers to the activities and processes for creating value through the organization’s various stakeholder relationships (Hult et al. 2011). As such, it “looks beyond customers as the target of marketing activities” (Bhattacharya and Korschun 2008, p. 113) and instead is concerned with the role of stakeholder relationships in the marketing function and the organization. Implicit in this definition is the notion that organizations employing stakeholder marketing can generate superior marketing and financial performance outcomes (e.g., Hult et al. 2011). However, for stakeholder marketing to work, organizations have to be “dedicated to learning about and addressing stakeholder issues” (Ferrell et al. 2010, p. 95). Therefore, an important implication of stakeholder marketing is that its implementation requires learning about the different stakeholders to effectively respond to them (cf. Maignan and Ferrell 2004; Mish and Scammon 2010). In this respect, the literature on organizational learning (e.g., Huber 1991) is particularly fruitful since it can explain how organizations learn about and understand the demands of their various stakeholders—an area which has been largely absent from stakeholder research. The stakeholder-related knowledge generated from organizational learning, in turn, can be used to deal with stakeholders by determining the most appropriate marketing actions.

Organizational learning

Organizational learning is “the development of new knowledge or insights that have the potential to influence behavior” (Slater and Narver 1995, p. 63). By focusing on the gathering and development of information, the organization can develop on a cognitive level and increase its learning potential in the future (Fiol and Lyles 1985). This, in turn, can create a competitive advantage for the organization. In fact, research indicates that organizational learning occurs at three levels: the individual, group, and organization (Cangelosi and Dill 1965; Crossan and Berdrow 2003; Crossan et al. 1999). As such, these distinct levels in organizational learning work together dynamically to formalize information gathered on customers, competitors, and others in the marketplace such that the organization may draw from it in the future.

Previous research has identified four processes that are associated with organizational learning (Huber 1991; Sinkula 1994): knowledge acquisition, information distribution, information interpretation, and organizational memory. Knowledge acquisition is the process by which organizations obtain knowledge (Huber 1991). It involves collecting information about the external environment and then bringing that information into the boundaries of the organization and converting it to organization-related knowledge (Moorman 1995). For example, organizations acquire information about customer needs, market segmentation, competitor practices, and the changing role of channel partners (Day 1994). Clearly, knowledge acquisition is essential because, without it, an organization would not be able to keep up with market changes (Day 1994; Sinkula et al. 1997).

This study identifies three different mechanisms of knowledge acquisition: experiential, vicarious, and contact. First, organizations can acquire information independently and from their own experiences by way of experiential knowledge acquisition (Day 1994; Huber 1991; Levitt and March 1988). As pointed out by Slater and Narver (1995), this knowledge acquisition mechanism can be internally focused (i.e., exploitation) or externally focused (i.e., exploration) and relates to any type of first-hand experience the organization may have to acquire knowledge (March 1991). For instance, internally focused experiences may consist of producing a new product and obtaining knowledge from experience to improve productivity (Huber 1991; Levinthal and March 1993). Meanwhile, externally focused experiences include routine analysis of customer databases and formal market research, such as focus groups, customer attitude surveys, and assessments of sales response in test markets (Kohli and Jaworski 1990). In contrast, vicarious knowledge acquisition takes place as a second-hand data gathering mechanism when organizations draw inferences from the observed behavior of competitors and others in the field (Huber 1991; Lieberman and Asaba 2006; Ordanini et al. 2008). To acquire information about the practices implemented by competitors, organizations can rely on, for example, benchmarking, media, conferences, statistics, and books (Ordanini et al. 2008). As a result of this knowledge acquisition approach, organizations can determine which actions tend to produce positive or negative outcomes. In addition, contact knowledge acquisition is another second-hand information gathering mechanism. However, with this type, organizations make inferences based on contact with others “by means of personal and formal relationships between organizations and their members” (Ordanini et al. 2008, p. 385). By communicating with peers in other organizations, knowledge can be acquired through, for example, discussions with suppliers and working with lead business-to-business customers (Kohli and Jaworski 1990; Kraatz 1998).

For an organization to understand and effectively respond to market needs, newly acquired market information must be communicated to relevant departments and individuals throughout the organization (Kohli and Jaworski 1990; Sinkula et al. 1997). Information distribution is the process by which organizations share information from different sources (Huber 1991). Depending on who acquires such information, it can flow from the marketing department to other departments and in the opposite direction (Kohli and Jaworski 1990) as well as throughout the organization (Hult 2011b). As organizations eliminate the functional barriers that obstruct the flow of information between departments, they enhance their ability to make and implement prompt decisions (Slater and Narver 1995). Further, effective information distribution serves to coordinate the actions of different departments, which in turn facilitates the achievement of organizational objectives (Kohli and Jaworski 1990).

Before an organization can act on new information, it must first interpret it (Day 1994; Sinkula et al. 1997). Information interpretation is the process by which an organization gives distributed information one or more commonly understood meanings (Huber 1991). This may entail formal techniques, such as devil’s advocacy, or more informal ones, such as team meetings in which individuals share their interpretations of market information (Moorman 1995). In this context, disagreement among participants leads to a closer examination of the validity of different alternatives (Slater and Narver 1995). In particular, a high level of equivocality in the market information (e.g., Sinkula 1994) generates multiple and conflicting views about the organizational situation and the course of action the organization should follow (Daft and Lengel 1986). In this case, the information may need to be cycled among members a number of times before the conflicts are resolved and a common interpretation is reached (Daft and Weick 1984).

It is essential for knowledge to be retained within the organization in spite of personnel turnover and the passage of time (Levitt and March 1988). Organizational memory is the process by which organizations store knowledge for future use (Huber 1991). It may be manifested as shared beliefs (e.g., frames of reference, models, values, norms, and organizational stories), formal and informal routines (e.g., operating procedures and scripts), and physical artifacts (e.g., organizational structure and features of products) (Moorman and Miner 1997). The degree to which these are utilized determines how long organizational memory is preserved (Slater and Narver 1995).

Hypotheses development

This study integrates the instrumental perspective of stakeholder theory and the literature on organizational learning to examine the impact of stakeholder-focused organizational learning on stakeholder-focused behavioral action (responsiveness, innovation, and imitation) and, subsequently, on performance outcomes. While stakeholder theory predicts that those organizations attending to multiple stakeholders achieve their performance objectives (e.g., Donaldson and Preston 1995), the theory lacks sufficient structure to guide managerial behavior (e.g., Jensen 2002; Sundaram and Inkpen 2004). For instance, it does not provide much insight into the organizational factors that drive organizations to pay attention to the different stakeholders or to execute stakeholder practices (cf. Bundy et al. 2013). In this regard, the organizational learning literature (e.g., Huber 1991) provides a reasoned explanation of how organizations become informed about their stakeholders’ claims and how this stakeholder-related knowledge can, in turn, direct managerial action toward responding to the various stakeholders and create value. At the same time, incorporating this literature stream into stakeholder theory is beneficial to further develop the stakeholder marketing concept, given that learning about and understanding stakeholders’ interests is necessary to successfully implement stakeholder marketing (cf. Ferrell et al. 2010; Maignan and Ferrell 2004; Mish and Scammon 2010).

Based on the extant literature on organizational learning (e.g., Huber 1991; Hurley and Hult 1998; Sinkula 1994; Slater and Narver 1995), stakeholder-focused organizational learning is defined as the development of new stakeholder-related knowledge that facilitates changes in behaviors toward stakeholders. In particular, it simultaneously concentrates on knowledge about the organization’s multiple, primary stakeholder groups—i.e., customers, employees, suppliers, shareholders, regulators, and the community (Hult et al. 2011). Stakeholder-focused organizational learning, therefore, is more expansive than market-based learning, which is mainly concerned with understanding the organization’s customers and competitors, without much consideration to the other stakeholder groups (e.g., Slater and Narver 1995). This broader, stakeholder-focused perspective is also a more accurate reflection of how, today, organizations have to balance their attention and resources among the various and often conflicting stakeholders. The central idea underlying this study is that the stakeholder-related knowledge generated from stakeholder-focused organizational learning drives organizations to respond to their stakeholders. As such, stakeholder-focused organizational learning is identified as a key antecedent of stakeholder-focused responsiveness.

Stakeholder-focused responsiveness is defined as the organization’s propensity to take action in response to the stakeholder-related knowledge that is acquired, distributed, interpreted, and stored (cf. Bundy et al. 2013; Eesley and Lenox 2006; Kohli and Jaworski 1990). Interestingly, research has found that the information an organization gathers through market research may affect its decision to pursue a strategy of either innovation or imitation (e.g., Ofek and Turut 2008). This suggests that the way the organization responds to the knowledge gained through the different knowledge acquisition mechanisms varies in the degree to which its stakeholder practices are characterized as innovative or imitative. Hence, both innovation and imitation are treated as forms of responsiveness along with more traditional responsiveness issues rooted in the market orientation and organizational learning literatures. Lastly, the links between stakeholder-focused responsiveness, innovation, and imitation on one hand, and organizational performance on the other, are examined. Figure 1 illustrates the relationships studied.

Fig. 1
figure 1

A model of stakeholder-focused organizational learning, responsiveness, innovation/imitation, and performance

Full size image

Organizational learning and responsiveness

The first set of hypotheses links the four processes of organizational learning—knowledge acquisition, information distribution, information interpretation, and organizational memory—with responsiveness. The strategic management literature suggests that organizational learning is a strategic resource that can benefit the organization (Hult et al. 2003). In the context of stakeholders, this notion encourages the organization to learn about the interests and expectations of its various stakeholders such that it can take behavioral action, perform better, and develop a sustainable competitive advantage (Bell et al. 2002; Crossan and Berdrow 2003). In turn, this achievement-focused approach to stakeholders’ needs is critical for the organization to maintain its relevance (e.g., Freeman et al. 2007). As such, the responsive actions the organization takes are fundamentally related to the learning process (Lichtenthaler 2009). Thus, we can expect the organization to be adaptive and responsive to stakeholders based on the knowledge and information it acquires (Crossan and Berdrow 2003).

Knowledge acquisition and responsiveness

It is well-established in the literature that data collection (i.e., knowledge acquisition) is a compelling antecedent to substantive action in the marketplace (e.g., Daft and Weick 1984). Those organizations that actively engage in stakeholder-focused knowledge acquisition activities recognize stakeholder needs before their rivals and are motivated to find solutions to those needs (e.g., Slater and Narver 1995). Additionally, organizations that regularly obtain information about procedures and practices that do and do not work respond to this information by modifying their procedures with the objective of improving productivity and performance (Day 1994; Yli-Renko et al. 2001). This suggests that by continuously monitoring the outcomes of their stakeholder practices, organizations take actions aimed at enhancing such practices and ultimately at improving stakeholder satisfaction. In fact, the first-hand, experiential knowledge that can reach the highest levels of the organization has been found to be critical in supporting responsiveness (Holmqvist 2004; Tuschke et al. 2014). Similarly, gaining second-hand, vicarious knowledge by observing competitors’ interactions with stakeholders also drives the organization to act (Baum et al. 2000; Kim and Miner 2007; Posen and Chen 2013). Further, the level of contact knowledge that an organization can obtain through its network can encourage action in response to stakeholder claims (Hagedoorn 2006; Ordanini et al. 2008). Hence, the expectation is that the more the organization emphasizes stakeholder-focused knowledge acquisition activities, the more responsive it will be to stakeholder needs.

  1. H1a:

    Experiential, vicarious, and contact knowledge acquisition related to an organization’s primary stakeholders positively affects stakeholder-focused responsiveness.

Information distribution and responsiveness

The organizational learning literature suggests that as stakeholder-related information is distributed throughout the organization, individuals and units gain new understanding about stakeholders and their needs (Huber 1991; Hult et al. 2004). As a result, the organization as a whole becomes more educated and aware of stakeholders’ demands (Hult et al. 2007), which improves the organization’s ability “to make rapid decisions and execute them effectively” (Slater and Narver 1995, p. 65). As such, the process of information distribution shapes the organizational direction of the organization (e.g., Kohli and Jaworski 1990). Consistent with this contention, Sinkula et al. (1997) empirically find that market information distribution directly affects the extent to which an organization changes its marketing strategies. Accordingly, the expectation is that an organization is more responsive to stakeholders’ demands if stakeholder-related information is widely distributed throughout the organization.

  1. H1b:

    Information distribution related to an organization’s primary stakeholders positively affects stakeholder-focused responsiveness.

Information interpretation and responsiveness

A central tenet of the information processing literature is that an organization’s interpretation of information about its external environment influences the actions it subsequently takes (e.g., Daft and Weick 1984). Information interpretation leads to a shared understanding of opportunities and problems that exist in the organization’s environment (Hult et al. 2007; Hult et al. 2004), which in turn provides a concerted direction for individuals throughout the organization (e.g., Daft and Lengel 1986). Empirical research also provides support for this premise (e.g., Thomas et al. 1993). Building on these notions, it is expected that an organization that actively interprets information concerning stakeholders will be more responsive to their needs than organizations that do otherwise.

  1. H1c:

    Information interpretation related to an organization’s primary stakeholders positively affects stakeholder-focused responsiveness.

Organizational memory and responsiveness

A powerful feature of organizational memory is its role in guiding actions (e.g., Moorman and Miner 1997). For instance, an organization’s memory may contain policies and procedures for dealing with particular stakeholders (e.g., Day 1994). This, in turn, dictates individual and group actions toward stakeholders (Moorman and Miner 1997). In addition, the stakeholder information that is housed in organizational memory can contribute to efficient and effective decision making (e.g., Walsh and Ungson 1991). By reviewing its past decisions, an organization can determine which actions are likely to satisfy stakeholder demands as well as which are likely to produce negative outcomes, such as the withdrawal of economic or social participation leading to boycotts. The information that resides in memory shapes the way the organization responds to a current decision situation. As such, the expectation is that an organization with a high level of organizational memory that is rich in knowledge about stakeholder claims and about standard practices for treating stakeholders will be more responsive to stakeholders than other organizations with lower levels of organizational memory (cf. Moorman and Miner 1998).

  1. H1d:

    Organizational memory related to an organization’s primary stakeholders positively affects stakeholder-focused responsiveness.

Knowledge acquisition mechanisms, innovation, and imitation

The manner in which the organization responds to the knowledge it acquires about stakeholders and their needs is a function of the mechanism it relies on to collect such knowledge. To the extent an organization acquires stakeholder-related information from direct experience (i.e., experiential knowledge acquisition), it will respond with more innovative stakeholder practices. Innovative stakeholder practices are defined as those practices that are targeted toward the organization’s stakeholders and that are new to the market (e.g., Garcia and Calantone 2002). Alternatively, an organization that relies more on vicarious knowledge acquisition will be more inclined to respond to stakeholders’ demands by employing imitative stakeholder practices. These are practices that are not new to the market, as they have already been executed by the organization’s rivals, suppliers, clients, or partners, or by other organizations. As for the middle ground, an organization that undertakes stakeholder-related knowledge acquisition through contact can respond to stakeholders either by engaging in innovative or imitative stakeholder practices.

The literature on organizational learning suggests that organizations acquiring information about stakeholders from externally focused experience or exploration (i.e., experiential knowledge acquisition) are likely to discover and implement innovative stakeholder practices (e.g., Levinthal and March 1993; McGrath 2001). Specifically, exploration is related to experimentation with new alternatives (e.g., March 1991). Potential consequences of this include the development of new technologies, processes, products, or modes of management (e.g., Levinthal and March 1993; McGrath 2001). Similarly, it has been argued that exploration facilitates generative learning (e.g., Slater and Narver 1995), which in turn leads to radical innovation (e.g., Baker and Sinkula 2002). In a stakeholder context, this would result in the introduction of novel practices that address the stakeholders’ demands, while disrupting established stakeholder practices (e.g., Verbeke and Tung 2013). Empirically, He and Wong (2004) provide support for the positive association between exploration and product innovation intensity. As such, the organization’s degree of innovative stakeholder practices is expected to be strongly related to experiential knowledge acquisition (cf. Harrison et al. 2010).

Institutional theory and the literature on organizational learning provide the basis for the expectation that an organization’s degree of imitative stakeholder practices is closely related to vicarious knowledge acquisition. Institutional theory holds that mimetic isomorphism pressures drive organizations to observe the actions of others in the field (i.e., vicarious knowledge acquisition)—especially of competitors who they perceive to be more legitimate or successful—and imitate those actions in an attempt to gain legitimacy (e.g., DiMaggio and Powell 1983). This particularly arises under conditions of environmental uncertainty, such as when organizations do not know how to respond to changing consumer demands, new regulations, or negative publicity (e.g., Bondy et al. 2012). In these instances, organizations seeking legitimacy find it easy to simply conform to what others are doing (e.g., Suchman 1995). Within the context of an organization’s interactions with stakeholders, attaining legitimacy is pivotal. This is evident by the attention given to the proliferation of rankings, such as Fortune’s “Most Admired Companies,” which have a long-standing tradition of periodically evaluating organizations’ performance with regard to how they treat stakeholders (e.g., Waddock et al. 2002). Hence, organizations that are unclear about how to deal with their stakeholders are likely to engage in vicarious knowledge acquisition and replicate the stakeholder practices of their rivals.

The organizational learning literature also allows for the connection between vicarious knowledge acquisition and imitative stakeholder practices. According to this research stream, organizations do not simply mimic common practices and routines due to social pressures as stipulated by institutional theory (e.g., Ordanini et al. 2008), but instead they engage in inferential learning (e.g., Miner and Haunschild 1995). As such, they observe the benefits and drawbacks that competitors obtain from their actions and imitate only those actions that seem to produce positive outcomes (e.g., Haunschild and Miner 1997; Ordanini et al. 2008). In this manner, organizations capture second-hand experience through imitation (e.g., Levitt and March 1988) in an effort to reduce the learning curve (e.g., Bondy et al. 2012; Ingram and Baum 1997) and cut the costs, time, and risks associated with exploration and experimentation (e.g., Baum et al. 2000; Lieberman and Asaba 2006). Several studies empirically demonstrate that organizations engage in vicarious knowledge acquisition and imitate others in a variety of settings including investment banking decisions (Haunschild and Miner 1997), market choices (Korn and Baum 1999), and new product introductions (Srinivasan et al. 2007). Based on these findings, and building on institutional theory and the organizational learning literature, it is likely that those organizations that acquire information by observing the stakeholder practices of their competitors will respond to stakeholders by employing similar practices.

Several perspectives imply that contact knowledge acquisition—a second-hand data gathering mechanism like vicarious knowledge acquisition—leads to imitative practices. First, an extension of institutional theory asserts that when organizations are faced with ambiguous situations, they are likely to imitate those organizations with whom they have network ties as a means to attain legitimacy (Galaskiewicz and Wasserman 1989). This occurs because network ties enable managers to observe how other organizations deal with environmental conditions comparable to their own and to learn about strategies that they themselves might subsequently adopt. Similarly, from an organizational learning lens, organizations go through an inferential learning process in which they imitate the actions of network contacts whose actions have been more successful (e.g., Kraatz 1998). This enables organizations to act quickly, especially when additional information is not available (e.g., Lieberman and Asaba 2006). Lastly, social network theory recognizes the role of personal relationships in generating trust, transmitting information, and influencing behavior across organizational boundaries (e.g., Granovetter 1985, 2005). In particular, when an organization has direct contact with others, it can obtain evaluative information by witnessing the actions of other organizations and their consequences (e.g., Burt 1987; Rogers 1983). Since the organization trusts its network contacts and has confidence that they are doing the right thing (e.g., Granovetter 1985), it may use this information to model itself after its contacts (e.g., Rogers 1983). As such, strong ties facilitate an organization to behave similarly to its contacts (e.g., Granovetter 1973). Taken together, these three perspectives indicate that organizations convert the stakeholder-related information acquired from contacts into imitation of stakeholder practices.

At the same time, information acquired from an organization’s contacts can also be a source of innovation development (e.g., Imai et al. 1985). Powell et al. (1996, p. 142) conclude that “the locus of innovation is found within the networks of interorganizational relationships.” Accordingly, an organization’s network ties facilitate innovation given that they both provide timely access to information that is otherwise unavailable and enable the organization to expand its own learning capabilities through external collaborations. This greater knowledge base, in turn, stimulates the organization to derive novel developments, such as new stakeholder practices. Therefore, since organizations that acquire stakeholder-related information through contact can innovate or imitate, it is argued that they respond to stakeholders with innovative practices to a lesser degree than those organizations that rely on experiential knowledge acquisition. In addition, organizations that acquire contact knowledge can also respond with imitative practices to a lesser degree than those that acquire vicarious knowledge. The preceding discussion leads to the following hypotheses:

  1. H2a:

    The organization’s degree of innovative stakeholder practices is positively associated with the degree of experiential knowledge acquisition, the degree of contact knowledge acquisition, and the degree of vicarious knowledge acquisition in descending order of importance.

  2. H2b:

    The organization’s degree of imitative stakeholder practices is positively associated with the degree of vicarious knowledge acquisition, the degree of contact knowledge acquisition, and the degree of experiential knowledge acquisition in descending order of importance.

Combinative effects between knowledge acquisition mechanisms

Typically, organizations do not restrict themselves to a sole method to acquire information but instead draw on some combination of methods (e.g., Lieberman and Asaba 2006). This occurs because “knowledge facilitates the use of other knowledge” (Powell et al. 1996, p. 120). For instance, an organization that collects stakeholder-related information from disparate sources is likely to be more cognizant of the needs of its stakeholders and of stakeholder practices executed by other organizations. This complementary information equips the organization to respond more effectively to its stakeholders. Similarly, the concept of “combinative capabilities” suggests that organizations synthesize and synergistically apply stakeholder-related information acquired from different sources to respond to market opportunities (Kogut and Zander 1992). As such, this approach has found that action and production increase as a result of these knowledge combinations (Smith et al. 2005; Taylor and Greve 2006). Furthermore, based on the information the organization has gained to date, this process creates an environment in which it is more capable of being responsive (Van den Bosch et al. 1999). Accordingly, the prediction is that organizations that rely on a combination of knowledge acquisition mechanisms use this complementary information to respond to stakeholders. Stated formally:

  1. H3:

    After accounting for the direct effects of experiential, vicarious, and contact knowledge acquisition, the combinative effects of each pair of knowledge acquisition mechanisms positively affects stakeholder-focused responsiveness.

Responsiveness, innovation, imitation, and performance

Responsiveness and performance

According to stakeholder theory, organizations that respond to multiple stakeholders by engaging in stakeholder practices achieve superior performance over organizations that do otherwise (e.g., Donaldson and Preston 1995). By attending to their stakeholders, organizations are able to develop and nurture stakeholder relationships on the basis of mutual trust and cooperation (Jones 1995). This can constitute intangible, idiosyncratic resources, such as reputation and brand equity (e.g., Harrison et al. 2010; Hillman and Keim 2001). The combination of such resources increases organizational heterogeneity and achieves differentiation which can, in turn, translate into successful value creation and a sustained competitive advantage (e.g., Verbeke and Tung 2013).

In addition, the primary stakeholders themselves control important resources that are necessary for the survival and continued success of organizations (e.g., Clarkson 1995; Frooman 1999; Pfeffer and Salancik 1978). In this regard, organizations that are committed to their stakeholder relationships and are responsive to stakeholders’ concerns have access to a stronger resource base because of their good reputation (e.g., Verbeke and Tung 2013). This greater ability to acquire resources that are essential to the value creation process can provide a competitive advantage. On the other hand, failure to respond to stakeholders may prompt resource-rich stakeholders to take costly actions against organizations by withdrawing access to their valuable resources (e.g., Eesley and Lenox 2006). Consequently, from a stakeholder view, stakeholder-focused responsiveness brings benefits to organizations through the resources derived from enhanced stakeholder relationships. Furthermore, the literature contains various examples that find that stakeholder management yields superior outcomes (e.g., Choi and Wang 2009; Greenley and Foxall 1997; Hillman and Keim 2001; Waddock and Graves 1997). Therefore:

  1. H4a:

    The organization’s degree of stakeholder-focused responsiveness positively affects business unit performance.

Innovation and performance

Organizations that respond to their stakeholders by engaging in innovative stakeholder practices benefit from definitive advantages. For instance, innovators can have a significant influence on how customers assess attributes in the product category and on the ideal combination of attributes (Carpenter and Nakamoto 1989). As a result, they can ultimately become the standard against which competitors are evaluated. This notion can be expanded to the stakeholder context in which innovators’ practices become the standard against which competitors’ practices are assessed, as well. In addition, by lowering costs for innovators, the learning curve generates a sustainable cost advantage, and it represents a major entry barrier for other organizations (Lieberman and Montgomery 1988). Additional innovator advantages include the preemption of scarce assets and the development of customer switching costs (e.g., Lieberman and Montgomery 1988). These advantages that innovators gain from executing innovative practices lead to superior performance outcomes (e.g., Rubera and Kirca 2012; Srinivasan et al. 2009).

This relationship between innovation and performance has been forwarded as a critical aspect of a market-oriented organization for quite some time (e.g., Han et al. 1998; Hurley and Hult 1998). In particular, innovators are able to develop strategies and programs that make them more competitive in the marketplace (Li and Atuahene-Gima 2001; Stock et al. 2013). More precisely, these types of organizations have the capacity to innovate on their own to such an extent that enhanced performance is the inherent byproduct of their approach (Li and Atuahene-Gima 2002; Sampson 2007). Within the stakeholder context, innovative organizations are able to align and configure their stakeholder practices better than others such that economic value can be created (Verbeke and Tung 2013). According to previous research, the compelling consequence of emphasizing stakeholders with an innovation-focused strategy has been financial success (Fang et al. 2011; Stock and Zacharias 2011). Therefore, we predict that:

  1. H4b:

    The organization’s degree of stakeholder-focused innovation positively affects business unit performance.

Imitation and performance

Institutional theory predicts that when organizations do not know how to deal with the demands of the different stakeholders, they will imitate the actions of others in the field (e.g., DiMaggio and Powell 1983). While adopting the practices executed by others provides legitimacy and secures survival, it does not improve performance (Meyer and Rowan 1977). Specifically, as organizations try to achieve legitimacy by conforming to societal expectations, they tend to make normatively rational choices motivated by social justification, and not economically rational ones based on efficiency or profitability (Oliver 1997). This translates to inadequate resource allocations and the suboptimal use of accumulated resources. As a result, previous work has empirically demonstrated that imitation has a negative effect on profitability (e.g., Barreto and Baden-Fuller 2006). Applied to the stakeholder context, organizations would not find benefit to imitating others’ strategies to address customers, employees, shareholders, suppliers, regulators, and local communities. More precisely, due to the imitative nature of the organization’s realized actions, a lack of differentiation is noticeable by stakeholders and, therefore, positive performance is difficult to achieve (e.g., Verbeke and Tung 2013). Thus, drawing on institutional theory and empirical evidence, we predict that:

  1. H4c:

    The organization’s degree of stakeholder-focused imitation negatively affects business unit performance.

Empirical study

Data collection

To empirically assess the hypotheses, we constructed an online survey to assess stakeholder practices in organizations (e.g., experiential knowledge acquisition, vicarious knowledge acquisition, contact knowledge acquisition, information distribution, information interpretation, organizational memory, responsiveness, innovativeness, and imitativeness). The mailing list was acquired from Dun and Bradstreet Information Services. Given the paper’s focus on stakeholder issues related to the marketplace of an organization, both marketing and supply chain executives were targeted as potential respondents to cover an organization’s entire value chain in the context of primary stakeholders.

Through a qualifying survey sent via email to the marketing and supply chain executives at 1,072 organizations, a total of 598 executives were identified as knowledgeable about stakeholder practices and willing to respond to the survey on stakeholder practices. The marketing and supply chain executives in the sampling frame had managerial positions with titles such as vice president, director, and manager. The organizations represented a broad cross-section of industries (e.g., computers, express delivery, food, retail, automotive, and defense).

Prior to collecting the data, we pre-tested the scale items with 10 experts in stakeholder, marketing, supply chain, and social science research practices. Huber and Power’s (1985) guidelines on how to get quality data from key informants were followed. The survey was developed using the method established by Dillman et al. (2009). A cover letter was sent via email, which included a web link to the survey that was constructed. The surveys were made available to the 598 identified managers and were anchored in 2009. Three waves of survey mailings were performed, with a total of 349 executives responding. This resulted in an effective response rate of 58.36% (349 responses out of 598). These 349 individuals represented 349 different strategic business units in 285 different firms (or 26.58% of the 1,072 firms). Each wave of surveys (first, second, third) was sent out on a different weekday (with 3 to 7 days in between each mailing) and/or at a different time to maximize the likelihood of obtaining responses.

The 349 SBU respondents from 285 firms included 53.9% marketing executives and 46.1% supply chain executives. At the firm level, these firms average $13.29 billion in annual sales (standard deviation = $3.49 billion), 43.08 years in operation (standard deviation = 32.18 years), $360.13 million in research and development investments (standard deviation = $1.10 billion), $311.22 million in advertising expenditures (standard deviation = $713.31 million), and have $2.98 billion in intangible assets (standard deviation = $9.31 billion). At the business-unit level, the SBUs averaged 4% in return-on-sales for 2010 (standard deviation = .11), which was our one-year lag for performance assessments.

In terms of the respondents acting as key informants, they had an average score of 5.04 (standard deviation = 1.33) on the question “I have great knowledge of stakeholders pertaining to my industry” and an average of score of 5.09 (standard deviation = 1.24) on the question “I have great knowledge of stakeholder practices in my organization.” Both questions used a seven-point Likert-type scale ranging from “strongly disagree” to “strongly agree.” In addition, on a scale from 1 = crucial to 7 = negligible, the respondents rated the overall importance of each of the six stakeholders as: 1.31 for customers (standard deviation = .70), 1.62 for employees (standard deviation = .90), 2.25 for shareholders (standard deviation = 1.07), 2.40 for suppliers (standard deviation = 1.03), 2.62 for regulators (standard deviation = 1.09), and 2.77 for local communities (standard deviation = 1.22).

Utilizing the procedures recommended by Armstrong and Overton (1977), no evidence of non-response bias was found when comparing the first and last quartiles of the respondents on the study measures. Additionally, no statistical difference was found between the firms in the sample and those not in the sample in annual sales for 2009 ($13.29 billion for the firms in the sample and $13.13 billion for the firms in the sampling frame that did not respond). The sampling method and unit of analysis (i.e., business unit) also follows stakeholder investigations of similar phenomena, and the response rate compares favorably with other strategically oriented marketing studies (e.g., Krush et al. 2015; Ramani and Kumar 2008).

Measures

The Appendix lists the measurement items used in the study. Where possible, established scales were adapted based on the context of this study. Specifically, the focus of the survey questions is on stakeholder-focused organizational learning (three types of knowledge acquisition, information distribution, information interpretation, and organizational memory) and the three types of behavioral action (responsiveness, innovation, and imitation). The survey was constructed to reflect operations in 2009 while the performance data reflected outcomes in 2010.

Three types of knowledge acquisition were used in this study (i.e., experiential, vicarious, and contact), based on work in market orientation (e.g., Kohli et al. 1993), organizational learning (e.g., Miner and Haunschild 1995), and institutional theory (e.g., Galaskiewicz and Wasserman 1989). Each of the scales is made up of four items. The experiential knowledge acquisition scale is adapted from Kohli et al.’s (1993) intelligence generation scale, and captures an organization’s gathering of stakeholder-related information through searching first-hand by experience (March 1991). New scales were developed for the other two types of knowledge acquisition based on the general structure of the intelligence generation items in the MARKOR scale (Kohli et al. 1993). The vicarious knowledge acquisition scale reflects an organization’s acquisition of stakeholder-related information by observing the behavior toward stakeholders of other organizations with whom it has no direct links (i.e., competitors). The contact knowledge acquisition scale captures the acquisition of stakeholder-related information by observing the stakeholder practices of others with whom the organization has a relationship.

To measure information distribution, items from Kohli et al.’s (1993) intelligence dissemination were adapted. The scale consists of five items that capture the distribution of stakeholder information across the organization. The information interpretation measure was motivated by Hult et al.’s (2004) two-item scale of shared meaning, which was subsequently expanded to a four-item scale by Hult et al. (2007). This scale is based on Huber (1991), and it captures an organization’s interpretation of stakeholder information. Moorman and Miner’s (1997) organizational memory scale was adapted for this study. As such, the scale consists of four items that capture the amount of knowledge, experience, and familiarity an organization has about its stakeholders relative to its major competitors.

As for intermediate outcomes, first, stakeholder-focused responsiveness was measured based on a scale adapted from Kohli et al. (1993). Taking this approach, the scale consists of five items and captures the extent to which an organization takes action to stakeholder-related information (e.g., Kohli and Jaworski 1990). The innovativeness measure was adapted from the scale developed by Hurley and Hult (1998) and, as used in this study, consists of four items. The imitativeness scale was motivated by Hurley and Hult’s (1998) innovativeness scale. The four-item scale was modified to reflect the imitative nature of this measure as opposed to the innovative nature of Hurley and Hult’s (1998) scale.

For performance, we used two different business unit–level measures (i.e., return-on-sales, market performance with referents). First, return-on-sales (ROS) was tied to the business unit and was measured 1 year after (2010) the antecedents data (2009). ROS was used specifically to provide performance insights into how much profit was being produced in a business unit per dollar of sales (i.e., net income divided by total sales). ROS has been included in a number of marketing studies (e.g., Dekimpe and Hanssens 1995; Homburg and Pflesser 2000; Slater and Narver 1994). The ROS ratio has been shown to be a good marketing performance variable to benchmark against competitors because it provides insights into the firms’ pricing and cost structure (Day and Wensley 1988; Srivastava et al. 1998). A low ROS ratio indicates that low earnings are generated from revenues to pay for fixed expenses and achieve profits. A low ROS is also a potential signal that the firm is unable to control its production expenses.

Second, market performance was measured as expected performance in the next year (2010) based on the logic by Vorhies and Morgan (2005). In addition, the market performance variable was anchored with referents (e.g., Shoham and Fiegenbaum 1999) to the organization’s objectives (i.e., “relative to our organization’s objectives, our market performance will greatly exceed our objectives in the next year”). Rooted in the theory of strategic reference points (SRP), “a referent is a standard of comparison that is used to interpret organizational outcomes” (Short and Palmer 2003, p. 209). Specifically, “by signaling organizational priorities and overall direction, [marketing and supply chain] managers focus the attention of organizational members on particular goals and objectives; in doing so, they define the strategic reference point for the firm” (Fiegenbaum et al. 1996, p. 220). Performance with referents is viewed as the appropriate way to measure outcomes related to organizational learning processes. In fact, “referents influence strategic actions and performance through their role in the organizational learning process” (Palmer and Short 2001, p. 212).

Confirmatory factor analysis and common method variance

Table 1 reports the means, standard deviations, correlation matrix, and shared variances for the nine study constructs. Table 2 summarizes the measurement analysis (i.e., composite reliabilities, average variances extracted, factor loadings, and fit statistics). All measures were subjected to reliability and validity assessments (e.g., Bagozzi and Yi 2012). As noted in Table 2, the data were found to be appropriate for analysis in this study.

Table 1 Means, standard deviations, correlations, and shared variances (n = 349)
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Table 2 Measurement results (n = 349)
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Fit statistics

The CFA model fit was evaluated using a series of fit indices recommended by Gerbing and Anderson (1992) and Hu and Bentler (1999)—the normed fit index (NFI), DELTA2, comparative fit index (CFI), and root mean square residual (RMSR)—along with the reporting of chi-square (χ 2) and degrees of freedom (d.f.). After removing eight items with loadings below .70 (cf. Fornell and Larcker 1981), an excellent fit to the data was achieved in the confirmatory factor analysis. The NFI = .95, DELTA2 = .95, CFI = .95, and RMSR = .06 (χ 2 = 2187.22, d.f. = 428).

Composite reliability

The nine latent factors’ reliabilities were assessed by calculating their composite reliability (Fornell and Larcker 1981). The factor loadings and their t-values were also examined (Anderson and Gerbing 1982). The scales’ reliabilities ranged from .82 to .95, all of which are above the recommended threshold for CFA-based composite reliabilities (Peter 1979). The factor loadings ranged from .70 to .98 (p < .01). Table 2 reports the CFA results.

Discriminant validity

The scales were also found to have good discriminant validity via two analyses. First, by comparing the average variances extracted (AVE) for each scale with the pairwise shared variances of all possible combinations among the nine scales, the AVEs ranged from .61 to .86 while the shared variances ranged from .13 to .59. Second, the technique suggested by Bagozzi and Phillips (1982) was used to again examine discriminant validity via a different type of analysis (cf. Bagozzi and Yi 2012). This entails examining all possible pairs of the nine constructs in a series of two-factor CFA models using LISREL 8.80 (Jöreskog et al. 2000). Specifically, each pairwise CFA model was run twice. In the first analysis, the φ coefficient was constrained to unity. In the second analysis, the φ coefficient was allowed to vary freely. Based on the results of a χ 2 difference test between pairs of constructs, the unconstrained models were better than the associated constrained models (i.e., χ 2 (1) > 3.84 was exceeded in all cases, with the ∆χ 2 (1) ranging from 179.92 to 923.82 for all possible pairwise combinations).

Common method variance

To examine if common method variance was inherent in the dataset, the one-factor test suggested by Podsakoff et al. (2003) was employed. The logic behind this test is that if common method variance poses a serious threat, a single latent factor would account for all manifest variables. The one-factor model resulted in a χ 2 = 6066.38 with d.f. = 464 versus χ 2 = 2187.22 with d.f. = 428 for the theoretically defined measurement model. Thus, the ∆χ 2 (36) = 3879.16, and no empirical evidence exists that common method variance is an inhibiting element in testing the hypotheses.

Hypothesis testing

Eighteen hierarchical regression models were conducted to assess the hypotheses. Cases with missing data were excluded listwise (i.e., any case with missing data was excluded), and the enter method was used to include variables in the equations at each step of the hierarchical regression analyses (cf. Cohen et al. 2003). All predictor variables were standardized by mean centering, and the natural log was used for the control variables of age (years) and size (sales). Industry was included as a dummy variable using the six-digit NAICS code. The Variance Inflation Factors ranged from 1.00 to 4.88 for the predictor variables in the eighteen equations, indicating that multicollinearity did not significantly affect the analysis (Cohen et al. 2003). The effect size for each equation—the probability of finding the R2’s achieved—was at least β > .90 at α = .05 using the method suggested by Cohen (1988).

For each equation tested to assess a certain set of hypotheses, we examined three different hierarchical regression models: equally weighted analysis (EWA), sample weighted analysis (SWA), and case weighted analysis (CWA). EWA represents a “standard” regression model where each variable in the model was equally weighted. SWA involves weighing each variable based on the average importance placed on each stakeholder group by the overall sample. CWA involves weighing each variable based on the importance placed on each stakeholder group by an individual respondent (i.e., each individual SBU). A seven-point Likert-type scale was used ranging from 1 = crucial to 7 = negligible for the respondent’s assessment of “overall importance” of a particular stakeholder group. The customer group was viewed as the most important (mean = 1.31), followed by employees (1.64), shareholders (2.24), suppliers (2.42), regulators (2.60), and local communities (2.78). Tables 3–7 report the complete results for the hypotheses including the effects of the control variables and effects at each step of the hierarchical regression analyses for all variables. Our focus in reporting the results in the next sections is on the significant relationships as they relate to the hypotheses.

Table 3 H1: Results on stakeholder-focused responsiveness
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Table 4 H2a: Results on stakeholder-focused innovativeness
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Table 5 H2b: Results on stakeholder-focused imitativeness
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Table 6 H3: Results of combinative effects on stakeholder-focused responsiveness
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Table 7 H4: Results on performance
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Organizational learning and responsiveness (H1)

To examine H1, we used the three forms of knowledge acquisition as predictor variables (i.e., experiential knowledge acquisition, vicarious knowledge acquisition, and contact knowledge acquisition) along with information distribution, information interpretation, and organizational memory. Experiential knowledge acquisition has a direct effect on responsiveness in the EWA (β = .22, p < .01), SWA (β = .28, p < .01), and CWA (β = .20, p < .01). Contact knowledge acquisition also has a positive effect in the EWA (β = .11, p < .10), SWA (β = .23, p < .05), and CWA (β = .12, p < .10). Likewise, information distribution has an effect in the EWA (β = .27, p < .01), SWA (β = .22, p < .05), and CWA (β = .24, p < .01), as does information interpretation in the EWA (β = .27, p < .01), SWA (β = .21, p < .01), and CWA (β = .29, p < .01). Organizational memory has a direct positive effect in the EWA (β = .13, p < .10) and CWA (β = .15, p < .05) but not in the SWA. Vicarious knowledge acquisition has no effect in the EWA, SWA, or CWA. The ΔR2 between Step 1 (controls) and Step 2 (direct effects) ranges between .64 and .75 (p < .01), and the equations had R2 ranging from .67 to .77. These results generally indicate that the aspects of H1 were supported except the link between vicarious knowledge acquisition and responsiveness (and organizational memory in the SWA). Table 3 provides detailed results of H1.

Knowledge acquisition and innovativeness (H2a)

The model testing supported the basic premise of H2a that experiential knowledge acquisition is the primary driver of innovativeness. Experiential knowledge acquisition was found to affect innovativeness in the EWA (β = .45, p < .01), SWA (β = .41, p < .01), and CWA (β = .60, p < .01). Contact knowledge acquisition was also significant in the SWA (β = .25, p < .05) but not in the EWA or CWA. The ΔR2 between Step 1 (controls) and Step 2 (direct effects) ranges between .24 and .46 (p < .01), and the equations had R2 ranging from .28 to .48. These results indicate that H2a was partially supported, with the most critical aspect of the hypothesis being supported (i.e., that experiential knowledge acquisition is the key knowledge acquisition driver of innovativeness). Table 4 provides detailed results of H2a.

Knowledge acquisition and imitativeness (H2b)

The model testing supported the basic premise of H2b that vicarious knowledge acquisition is the primary driver of imitativeness in the EWA and SWA, while the results of the CWA contradicted the EWA and SWA. Vicarious knowledge acquisition was found to affect imitativeness in the EWA (β = .20, p < .10) and SWA (β = .21, p < .10) but not in the CWA. In the CWA, experiential knowledge acquisition (β = .26, p < .01) and contact knowledge acquisition (β = .28, p < .01) were significant while contact knowledge acquisition was also significant in the SWA (β = .18, p < .10). Given that the key variable—vicarious knowledge acquisition—was only significant at the p < .10 level in the EWA and SWA, two additional regression analyses were conducted for each of EWA and SWA to examine the relationships in a more fine-grained manner. Specifically, the models were run with both pairwise exclusion of data and mean substitution of data with missing values, in addition to the listwise deletion of data used for all regression models, and both additional models rendered vicarious knowledge acquisition significant at the p < .05 level in the EWA and SWA. The ΔR2 between Step 1 (controls) and Step 2 (direct effects) ranges between .09 and .34 (p < .05), and the equations had R2 ranging from .11 to .36. These results indicate that H2b was partially supported, with the most critical aspect of the hypothesis being supported (i.e., that vicarious knowledge acquisition is the key knowledge acquisition driver of imitativeness) in the EWA and SWA but not in the CWA. Table 5 provides detailed results of H2b.

Combinative knowledge acquisition and responsiveness (H3)

A number of significant relationships were detected in relation to H3, after controlling for industry, size, age, and the direct effects of each knowledge acquisition mechanism (i.e., experiential, vicarious, and contact). The direct effects of experiential knowledge acquisition had a positive association with stakeholder-focused responsiveness in the EWA (β = .62, p < .01), SWA (β = .59, p < .01), and CWA (β = .77, p < .01), as did contact knowledge acquisition in the EWA (β = .20, p < .05), SWA (β = .39, p < .01), and CWA (β = .20, p < .05). Vicarious knowledge acquisition was insignificant in the EWA and CWA but had a negative association with stakeholder-focused responsiveness in the SWA (β = −.15, p < .10). Related to H3, the combinative effects of experiential knowledge acquisition and contact knowledge acquisition had a negative association with stakeholder-focused responsiveness in the EWA (β = −.34, p < .01), SWA (β = −.38, p < .01), and CWA (β = −.32, p < .01). The combinative effect of experiential knowledge acquisition and vicarious knowledge acquisition had a positive effect in the EWA (β = .16, p < .10) and SWA (β = .37, p < .01) but no effect in the CWA. The combinative effect of vicarious knowledge acquisition and contact knowledge acquisition had a positive effect in the EWA (β = .21, p < .05) and CWA (β = .19, p < .05) but no effect in the SWA. The ΔR2 between Step 1 (controls) and Step 2 (direct effects) ranges between .47 and .62 (p < .01) and between Step 2 and Step 3 (combinative effects) ranges between .03 and .04 (p < .05). The equations had R2 ranging from .54 to .67. These results indicate mixed support for H3 but, importantly, with each combinative effect of knowledge acquisition having an over-and-above variance explained effect vis-à-vis the direct effects in multiple EWA, SWA, and CWA models. Table 6 provides detailed results of H3.

Responsiveness, innovation, imitation, and performance (H4)

We used two performance variables in this study: ROS (time period 1) and market performance (projected to time period 1 and with a referent to the organization’s objectives). For ROS, stakeholder-focused innovation was significant in the EWA (β = .17, p < .01), SWA (β = .22, p < .01), and CWA (β = .12, p < .05). Responsiveness was significant in the EWA (β = .09, p < .10) and CWA (β = .13, p < .05) but not SWA with ROS as the criterion variable. For market performance, very consistent results were found across the EWA, SWA, and CWA models. Specifically, with market performance as the criterion, stakeholder-focused responsiveness was significant in the EWA (β = .26, p < .01), SWA (β = .26, p < .01), and CWA (β = .23, p < .01). Likewise, innovation was significant in the EWA (β = .12, p < .05), SWA (β = .14, p < .05), and CWA (β = .10, p < .10). Stakeholder-focused imitation was also significant in all three market performance models but with a negative influence in each of the EWA (β = −.11, p < .05), SWA (β = −.13, p < .01), and CWA (β = −.16, p < .10). The ΔR2 between Step 1 (controls) and Step 2 (direct effects) ranges between .05 and .12 (p < .05), and the equations had R2 ranging from .06 to .13. Thus, in various configurations and performance outcomes, support exists for H4a, H4b, and H4c. Table 7 provides detailed results of H4.

Discussion and implications

Broadly, this study addresses recent calls for stakeholder marketing research that expands the scope of traditional marketing concepts to include additional stakeholders beyond the widely-studied customers (e.g., Bhattacharya 2010; Ferrell et al. 2010; Hillebrand et al. 2015; Hult et al. 2011). It achieves this by developing the concept of stakeholder-focused organizational learning, which is more encompassing than market-based organizational learning (e.g., Sinkula et al. 1997). This approach involves learning about the organization’s primary stakeholders (i.e., customers, employees, suppliers, shareholders, regulators, and the community) in a more complete fashion given that these groups are essential for the continued success of the organization (e.g., Clarkson 1995). Specifically, by integrating stakeholder theory with insights from the organizational learning literature, this study makes multiple contributions to the literature. First, by identifying stakeholder-focused organizational learning as an antecedent to stakeholder-focused responsiveness, we contribute to the stakeholder literature. Namely, research in this area has mostly focused on the external drivers of responsiveness, without much consideration to the internal factors that prompt organizations to respond to stakeholders (e.g., Bundy et al. 2013). This is the first empirical study to provide a fine-grained explanation of the processes involved in learning about and responding to the stakeholders’ interests. The findings reveal that, for organizations to be responsive to their stakeholders, they need to effectively acquire, distribute, interpret, and store stakeholder-related knowledge.

Second, the disaggregation of the knowledge acquisition process to examine the effects of experiential, vicarious, and contact knowledge acquisition on behavioral action (stakeholder-focused responsiveness, innovativeness, and imitativeness) extends the organizational learning domain (e.g., Huber 1991). The findings show that both experiential and contact knowledge acquisition positively influence stakeholder-focused responsiveness. This clearly indicates that the information gathered by the organization either directly or indirectly through partners in the organization’s network lead to stakeholder-focused action. In effect, this suggests that this type of information is reliable enough to respond to the needs of stakeholders in a constructive fashion and lead to superior performance.

The finding that an organization’s degree of experiential knowledge acquisition is positively related to innovative stakeholder practices is consistent with the organizational learning literature. This domain suggests that investing significant resources into gaining first-hand information about stakeholders through searching results in the experimentation of new stakeholder practices (e.g., March 1991) and, ultimately, in the development of new technologies, processes, products, or modes of management (e.g., Levinthal and March 1993). On the other hand, the results show that contact knowledge acquisition and vicarious knowledge acquisition do not lead to innovative stakeholder practices. Since these mechanisms consist of observing the stakeholder practices implemented by others, they do not drive the organization to seek new solutions to fulfill stakeholder demands. Consistent with the innovation literature, the results also highlight the importance of stakeholder-focused innovativeness for superior performance. In effect, organizations which are first to implement a particular stakeholder practice shape how stakeholders evaluate such a practice, and ultimately, it becomes the standard in the market (e.g., Carpenter and Nakamoto 1989). This, in turn, enhances performance. Overall, these findings suggest that the world’s most innovative companies, such as Apple and Google, which consistently offer inventive solutions that satisfy their stakeholders’ demands and outperform their rivals in their business models and processes (Fast Company 2015), rely on experiential knowledge acquisition as their primary mechanism to learn about their stakeholders’ needs and wants.

Moreover, the results indicate that vicarious knowledge acquisition has a positive influence on imitative stakeholder practices. This is consistent with institutional theory, particularly with the notion of mimetic isomorphism, and with organizational learning. Building on these streams, it is evident that because organizations are unclear about how they should interact with their stakeholders, they (1) gather information about the stakeholder-focused actions taken by their competitors, (2) evaluate the outcomes these actions produce for their rivals, and (3) mimic those actions that produced positive outcomes. Hence, the link between vicarious knowledge acquisition and imitation helps explain why organizations engage in similar activities. One example can be seen in the automotive industry. The hybrid-electric automobile—which strongly caters to customers’, regulators’, and the community’s demands—was originally released by Toyota, but now has similar competitor products made by a number of automobile manufacturers. Organizations, such as Honda and Ford, operating in the uncertain automotive industry observed the stakeholder practices of other successful automakers (e.g., Toyota and its production of the Prius hybrid vehicle) and imitated their competitors’ practices (e.g., developing their own versions of the hybrid vehicle) in an attempt to gain legitimacy. However, the results indicate that imitation has a detrimental effect on an organization’s performance outcomes.

Taken together, the assessment of the performance implications of innovative versus imitative stakeholder practices extends stakeholder theory by showing that simply responding to the stakeholders does not guarantee superior performance, but how the organization responds, matters just as much. In this regard, organizations are better off investing resources into acquiring experiential knowledge, which results in innovative practices and subsequently in superior performance, rather than investing in vicarious knowledge, which negatively affects performance through imitation.

Another contribution to the literature is the examination of the combinative effects of experiential, vicarious, and contact knowledge acquisition on stakeholder-focused responsiveness. In fact, this study is among the first to empirically examine the interactions between the different forms of knowledge acquisition. By doing so, it adopts a realistic model of learning (e.g., Lieberman and Asaba 2006) given that organizations often acquire information from different sources and in different formats. The findings indicate that the combinative effects of vicarious knowledge acquisition and contact knowledge acquisition are positively related to stakeholder-focused responsiveness. These two knowledge acquisition mechanisms are similar in that they both consist of acquiring second-hand information. As such, an organization that relies on them, in essence, requires the same set of skills—drawing inferences from the observed behavior of other organizations (e.g., Lieberman and Asaba 2006; Ordanini et al. 2008). By continuously engaging in both knowledge acquisition mechanisms to obtain stakeholder-related information, an organization can master this skill over time. This, in turn, allows the organization to efficiently synthesize the complementary information acquired, prompting it to respond more effectively to its stakeholders.

On the other hand, contrary to expectations, the combination of experiential knowledge acquisition and contact knowledge acquisition has a negative effect on stakeholder-focused responsiveness. One possible explanation for this result is that these mechanisms require different sets of skills. As previously discussed, contact knowledge acquisition consists of observing the behavior of other organizations, specifically those with which the organization has ties (e.g., Ordanini et al. 2008). Alternatively, experiential knowledge acquisition requires the organization to be more proactive in scanning its external environment for first-hand information about stakeholders and their interests (e.g., Huber 1991). Hence, experiential knowledge acquisition by itself prompts an organization to develop innovative solutions that respond to stakeholder demands, but, when combined with contact knowledge acquisition, the effect is negative. This could be due, in part, to the potentially conflicting stakeholder-related information the organization gathers from its network ties and the challenge the organization faces in synthesizing information acquired from such different sources.

Future research

As we focus on the emerging research trend of stakeholder marketing, there are a variety of possibilities open for future research opportunities which relate to this domain. First, by examining whether stakeholder-focused organizational learning influences the organization’s propensity to take action in response to its stakeholders’ needs, this study addresses an important void in the stakeholder literature—i.e., the little consideration to the internal drivers of stakeholder-focused responsiveness. While identifying stakeholder-focused organizational learning as an antecedent of responsiveness is a step in the right direction, further research is needed to explore other potential internal factors, such as organizational culture, that prompt the organization to address stakeholders’ demands (cf. Mish and Scammon 2010).

Second, our analysis incorporates the main groups to consider in a stakeholder-focused study: customers, employees, shareholders, suppliers, regulators, and the community. While this is consistent with the stakeholder marketing literature (e.g., Hult et al. 2011), there are stakeholder groups that were not included in this study. Specifically, secondary stakeholders such as special interest groups, consumer advocate groups, and the media were not identified as part of our analysis. Unlike primary stakeholders, who are powerful and have urgent, legitimate demands, secondary stakeholders lack both power and urgency (Godfrey et al. 2009). At the same time, even though these stakeholders are not critical for the organization’s survival, organizations often respond to them, because they can affect the primary stakeholders (e.g., Clarkson 1995). Given the underlying differences between primary and secondary stakeholders, organizations may interact differently with secondary stakeholders. Future research should examine how organizations learn about and respond to secondary stakeholders, while accounting for these stakeholders’ degree of influence on the primary stakeholders, as this would provide a more complete understanding of the stakeholder network (cf. Hillebrand et al. 2015).

Next, while this study expands Huber’s (1991) framework of organizational learning by examining the influence of experiential, vicarious, and contact knowledge acquisition on stakeholder-focused responsiveness, there are other aspects of knowledge acquisition which have yet to be examined in the stakeholder context. For instance, the notion of congenital knowledge acquisition, which focuses on the origins and initial stages of an organization’s development, has not been examined to a large degree. Addressing the influence of an organization’s early formation as a critical facet of stakeholder-focused knowledge acquisition could receive more attention and further develop our understanding of organizational learning. In specific, such an emphasis has the potential to explain the early days of an organization as relevant and pertinent to its current actions and performance.

In addition, this study examines the combinative effects of the three mechanisms of knowledge acquisition: experiential, vicarious, and contact. While this provides an extension to the organizational learning literature, there are still other combinative effects which are possible to examine in the future. More exactly, an examination of the combined influence of the specific processes of organizational learning (knowledge acquisition, information distribution, information interpretation, and organizational memory) could extend our understanding of these constructs and their interaction in the future.

In explaining the results of the combinative effects between the knowledge acquisition mechanisms, we note that the different skillsets required for experiential and contact knowledge acquisition could explain the negative combinative effect on stakeholder-focused responsiveness. However, given that vicarious and contact knowledge acquisition both require the gathering of second-hand information, the reason for the positive combinative effect of experiential and vicarious knowledge acquisition is unclear. It could be that, consistent with the concept of mimetic isomorphism (e.g., DiMaggio and Powell 1983), the organization and its competitors respond similarly to the wants and needs of stakeholders based on similar processes that both have in place. As such, the information gleaned from the organization’s vicarious knowledge acquisition may support the findings from acquiring information experientially. Thus, the interaction of these two types of knowledge acquisition is positive. Still, the reason for this interaction to be positive in light of the negative combinative effect of experiential and contact knowledge acquisition is unclear. Therefore, we suggest future research to examine this further.

Finally, the purpose of this study was to explore the relationships between stakeholder-focused organizational learning, stakeholder-focused behavioral action, and organizational performance. In fact, a CFA was conducted to establish discriminant and convergent validity of the model which included testing three types of knowledge acquisition: experiential, vicarious, and contact. After that, hierarchical regression was used to test the hypotheses proposed. Given the exploratory nature of this study, future research may aim to establish causality to evaluate stakeholder-focused organizational learning by using structural equation modeling to a greater extent.