Perceived Organizational Listening Effectiveness: A Comparison of Consumer Intelligence Provider and Consumer Intelligence User Assessments

Abstract

This paper describes research focusing on how an organizational member’s role in the process—specifically, whether s/he is a consumer intelligence provider or a consumer intelligence user—may impact his/her assessment of organizational listening practices and effectiveness. After reviewing literature in three relevant areas of research, the paper describes a study in which five hypotheses are tested, each of which predicts differences between provider and user assessments. Results indicate that provider and user assessments are comparable regarding (a) creating a climate for listening to consumers and (b) capturing consumer feedback. However, user assessments of effectiveness at analysis, dissemination, and utilization consumer intelligence are significantly less favorable than those from providers. Implications for theory-building and knowledge development, implications for practitioners, and directions for future research, are discussed.

Keywords

management communication organizational communication organizational listening voice of the customer customer knowledge management

What is the proper balance between message production and message receiving when it comes to how organizations communicate with key external publics and stakeholders?

Proponents of so-called “excellence theory” in public relations (Grunig & Grunig, 2013) assert that organizations should practice two-way and symmetrical communication to build and maintain positive relationships with external stakeholders. Critics of such an approach have argued that it is too idealistic and simplistic (Laskin, 2012), that a dialogic approach is not easily applied to communication between organizations and key publics/stakeholders (Pieczka, 2011), and that, by-and-large, attempts to put such an approach into practice have not been successful (Kenny, 2016).

Describing the so-called “Four Flows” model of organizational communication, McPhee and Zaug (2009) define “institutional positioning” as the exchange of information between an organization and “entities such as suppliers, customers, and competitors and collaborators” who are key stakeholders in the ecosystem in which the organization exists (p. 39). They emphasize message production that establishes an organization’s identity, and that “develops and maintains an organization’s place in the inter-organizational and larger social system” (p. 40). This emphasis on message production is echoed by other organizational communication scholars. For example, Lammers’ (2011) discussion of an organization’s communication with external audiences is almost entirely focused on how the institutional message “communicates the core meaning of the organization” to such audiences (p. 167).

Curiously, when it comes to communication between institutions and their external key stakeholders, much less attention has been devoted to organizational listening. Flynn et al. (2008) lamented the fact that research into listening as an organizational variable was almost non-existent in the scholarly business communication literature. Macnamara (2016a) argues that most discussions of such communication focus on “an ‘architecture of speaking” operationalized through advertising, media publicity, websites, publications, events, presentations and speeches, and even social media. He also asserts that this architecture of speaking needs to be counter-balanced by creating an “architecture of listening” (p. 10). In a subsequent paper, Macnamara (2018) claims the findings of a literature review that revealed “little attention to listening in organizational, corporate, government, and political communication and related fields of practice such as public relations” served as the impetus for his 3-year Organizational Listening Project (pp. 3, 4).¹ This project, along with the work of a handful of other scholars (e.g., Johnston & Reed, 2017; Kluger & Zaidel, 2013; Maben & Gearhart, 2018; Purdy & Manning, 2015) have produced a burgeoning, but still exiguous body of theory and research on organizational listening: At present, development of a robust knowledge and understanding of the listening process, potential challenges and obstacles, and best practices and solutions remains a work in progress.

Mindful of the above, this paper describes the most recent study from a program of research aimed at building increased insight into organizational listening effectiveness. Specifically, it focuses on listening effectiveness in commercial organizations pertinent to capturing and leveraging the Voice of the Consumer (VoC). The objectives and foundations of VoC initiatives are discussed, along with findings from studies that have attempted to gauge the success of these initiatives. Subsequently, a study is described that aims to build on previous research by comparing consumer intelligence provider assessments of listening effectiveness with those of consumer intelligence users. Key findings are presented, and implications for theory-building and knowledge development, implications for practitioners, and directions for future research also are discussed.

Listening to and Leveraging the “Voice of the Consumer”

More and more commercial organizations are conducting survey interviews, monitoring consumer calls and social media posts, and listening to consumers via other methods in an effort to capture and leverage the “Voice of the Consumer.” Intelligence and insights derived from the VoC typically are expected to facilitate product and service design (Urban & Hauser, 2004), improve consumer experiences and relationships (de Ruyter & Wetzels, 2000), help manage consumer problems and complaints (Bolkan & Daly, 2009; Bolkanet al., 2012; Davidow, 2003), make consumers more receptive to organizational media and messaging (Blackshaw, 2008; Godes & Mayzlin, 2009; Graham & Havlena, 2007; Hong & Yang, 2011), and help shape corporate social responsibility agendas (Edinger-Schonset al., 2020; Tapscott & Ticoll, 2012). In a majority of cases, efforts to capture and leverage the VoC are implemented as formalized programs. Schmidt-Subramanian (2014) reports that nearly 60% of North American companies have implemented VoC programs, and that the number is climbing.

Most firms invest in VoC programs with an eye toward achieving practical consumer relations and related commercial outcomes. Still, the principles on which VoC programs are founded can easily be traced to a number of organizational communication theories and models. These include the customer-centric philosophy embraced by scholars who support a Service-Dominant Logic perspective of marketing and marketing communication (Lusch & Vargo, 2006; Vargo et al., 2017; Vargo & Lusch, 2004; Zinzer & Brunswick, 2016). Such a perspective requires a firm to shift from a “make-and-sell” to a “sense-and-respond” mentality that facilitates development of knowledge and competencies that lead to optimal consumer experiences (Vargo et al., 2017, p. 128).

VoC programs also appear to have roots in the concept of a value chain, originated and developed by Porter (1985, 2001; Porter & Kramer, 2011). A value chain perspective requires an understanding what brings value to customers in order to align end-to-end organizational functions of inbound logistics, operations, outbound logistics, marketing and sales, and service to maximize that value.

Optimizing around customer value requires competence at organizational listening, defined by Macnamara (2016b) as the approach employed by an organization “to give recognition, acknowledgement, attention, interpretation, understanding, consideration, and response to an organization’s stakeholders and publics” (p. 52). Macnamara (2016b) describes a listening architecture that is based on the preceding definition, and argues that “listening for business sustainability” is one its key functions (p. 301). He claims that such an architecture requires: (a) an organizational culture that promotes listening; (b) policies that specify and require listening; (c) systems and technologies that are open, interactive, and that aid listening; (d) resources and staff to ‘do the work” of listening; (e) skills for listening; and (f) articulation of the voices of stakeholders and publics to organizational decision making and action.

Macnamara’s (2016b) organizational listening process and architecture have the potential to generate a wellspring of consumer data. However, in order to fulfill VoC program expectations these data must be transformed into consumer intelligence that is shared with managers and partners who can use it to improve and optimize consumer experiences (Paquette, 2011). Riege (2005) has identified multiple barriers to knowledge sharing in organizations, several of which directly reflect the influence of member roles (which is central to the present study and addressed in greater detail below).

Once consumer insights and intelligence have been shared, managers who have a hand in shaping consumer experiences must “do something with what they know” (Pfeffer & Sutton, 2000, p. 1). That is, consumer intelligence must be used to focus and direct action. In this regard, VoC-based intelligence typically is used to take action in the forms of (a) responding to individual consumer problems or requests, (b) mentoring managers and employees, and/or (c) driving large-scale systemic improvement efforts (Brandt, 2020, 2022).

Finally, all of the above must be part of a continuous, closed-loop process of organizational learning, planning and improvement that is routinized and refined over time. Thus, VoC programs also have roots in theories and models of quality management and continuous improvement (Brandt, 2020, 2022; Deming, 1993; Formento et al., 2013; Pande et al., 2000; Pyzdek & Keller, 2010).

In summary, most organizations invest in a VoC program as a practical matter. However, they do so having at least tacitly embraced principles of consumer-centricity, organizational listening and learning, knowledge sharing and utilization, and continuous improvement theories/models developed by marketing, management, and organizational communication scholars during the past several decades.

VoC Programs in Practice

Despite their pervasiveness (Schmidt-Subramanian, 2014; Temkin, 2016), surprisingly little is known about the effectiveness of VoC programs. A recent study by Brandt (2020) attempted to explore this issue. The qualitative exploratory phase of this research revealed ten essential components of a VoC program. Figure 1 displays these ten components, three of which play a key role in building and sustaining a climate for listening and learning, and seven of which involve activities that can be sequenced as critical steps in a closed-loop process of consumer listening and response.

Figure 1.

Ten VoC program components aligned with climate for and a closed-loop process of consumer listening and response.

In a subsequent quantitative study, Brandt (2020) developed and utilized measures of the ten essential VoC program components displayed in Figure 1 to survey managers from North American companies regarding perceived effectiveness of their respective VoC programs. Results showed that a majority of organizations have sufficient program support and engagement, but they have not yet achieved a desired level of overall effectiveness with regard to VoC processes and practices: Managers in most organizations believe they have been effective at capturing consumer feedback, but relatively few say they been comparably effective at analyzing, disseminating, or utilizing it to improve consumer experiences.

In a subsequent study, Brandt (2022) compared survey-based assessments obtained from senior executives (who typically are VoC program sponsors) with those obtained from middle managers (who are involved in actual VoC program operations). Findings were similar to those reported by Brandt (2020): Assessments of effectiveness at capturing the VoC were more favorable than those related to analyzing, disseminating, and utilizing the VoC. However, assessments obtained from middle managers were significantly less favorable than those obtained from senior executives, suggesting that organizational members who are “closer” to the day-to-day operation of VoC programs may sense problems and deficiencies that those at the “top of the house” do not.

The Present Study

In most VoC programs, middle managers typically play one of two basic roles: (1) Consumer intelligence (CI) provider and (2) consumer intelligence (CI) user (Brandt, 2020, 2022).

Day-to-day management/operation of the VoC program usually is delegated to CI providers. These are managers and the employees and/or partners they supervise. Providers operate as a centralized organizational team, and are responsible for capturing, analyzing, and disseminating insights drawn from consumer data/inputs.

CI users are product and service designers, consumer relations and support personnel, sales personnel, channel partners, and others who interact directly with consumers and/or have a direct hand in shaping the consumer experience. They are the recipients of insights drawn from consumer listening, and bear the burden of translating this intelligence into actions that lead to improved consumer experiences and relationships. In some cases, these actions are planned and implemented within a single organizational unit, and in other instances, they involve collaboration among several such units.

It is quite likely that CI providers and CI users view a VoC program through different lenses. While both parties are positioned to assess VoC program effectiveness from a continuous, hands-on perspective, providers control and are apt to focus on processes and practices connected to the first two phases of the close-loop process illustrated in Figure 1. In contrast, their accountability for applying such intelligence has the potential to heighten users’ attention to the quality of consumer intelligence and its dissemination, particularly as these VoC program elements impact users’ ability to leverage this intelligence in responding to individual consumers, training and mentoring managers and employees, and crafting systemic improvement plans.

Potential differences in provider and user perspectives of perceived organizational listening effectiveness have significant implications for the continued growth, impact, and sustainability of VoC programs: If users are unable to, or are ineffective in utilizing the intelligence they receive from providers to improve consumer experiences, anticipated benefits of consumer listening and learning may not materialize. This, in turn, may prompt disillusioned senior executives to question the value of and/or to pull support for further VoC program investments. Ultimately, multiple organizational members and stakeholders could be adversely impacted, including a firm’s consumers.

In short, differences between provider and user assessments of VoC program effectiveness may be present in many organizations. To date, such differences have not been investigated empirically. Because of their potential impact on VoC program success and sustainability, research on these potential differences is needed.

The purpose of this study is to explore potential role-based differences in the perception of organizational listening effectiveness by comparing CI provider VoC program assessments with those of CI users. A review of the literature in three areas of relevant research is used to develop five directional hypotheses regarding how provider and user assessments will differ, and a study designed to test these hypotheses subsequently is described.

Related Literature

An interdisciplinary literature review reveals three areas of research relevant to the present study: (a) customer knowledge management; (b) internal service quality; and (c) actor-observer asymmetry. A summary of key research findings in each of the preceding areas follows.

Customer Knowledge Management

A number of scholars argue that an organization’s adoption of a customer-centric philosophy alone is of limited value without continually updated customer intelligence that is integrated into day-to-day management and operations (Bose & Sugumaran, 2003; Rollins & Halinen, 2005). Unfortunately, while many organizations have access to enormous amounts of customer data, they struggle to extract actionable insights from these data, to share these insights in a timely manner, and to utilize them in ways that lead to improved customer experiences and relationships (Brandt, 2020; Campbell, 2003; Davenport et al., 2001; Khosravi & Hussin, 2018).

It is possible that the aforementioned difficulties stem from knowledge roles. Gupta and Govindarajan (1991) advanced four knowledge management roles, two of which are especially relevant to the present study. An integrated player has high information and knowledge inflows and outflows. Integrated players are receivers of large quantities of data and information, but also play a key role in providing information to other organizational units (Harzing & Noorderhaven, 2006; Qui & Lui, 2015). In contrast, an implementor has high information and knowledge inflows, but low outflows. Implementors generate little knowledge on their own, and rely principally on inflows from a centralized corporate unit and/or other organizational units/members. Implementors’ main tasks involve integrating inflowing information and knowledge into operations and managerial practices (Qui & Lui, 2015).

In the context of VoC programs, the knowledge role of CI providers appears to be that of an integrated player, while the knowledge role of CI users more closely resembles that of an implementor. That is, providers receive and process data from multiple VoC sources, attempting to transform these data into actionable intelligence. This intelligence becomes the outflow on which users depend for knowledge they need to plan and implement actions to improve consumer experiences and relationships. A critical question is whether feedback from users regarding the value and actionability of this intelligence is a key provider inflow: If not, the two-way communication that might otherwise improve the quality and actionability of insights being transferred from providers to users may not be occurring. Brandt (2020, 2022) findings that most organizations assess their effectiveness at capturing consumer data significantly more favorably than they assess their effectiveness at generating (via analysis), disseminating, and utilizing actionable consumer intelligence may very well reflect a knowledge role-driven communication quandary that ultimately inhibits the ability of users to “put the Voice of the Consumer to work.” Under such circumstances, one might expect CI users to assess VoC program effectiveness less favorably than CI providers, especially with regard to “downstream” processes and practices related to analysis, dissemination, and utilization: Like members of a relay team who bear the burden of running the “anchor leg” of a race, the success of CI users in performing their responsibilities depends largely on how well the CI providers who precede them perform theirs.

Internal Service Quality

The concept of one group of employees being regarded as internal customers who depend on the output of other employees or organizational units in order to serve their own (internal or external) customers emerged during the introduction and popularization of total quality management during the late 1980’s and 1990’s (Finn et al., 1996), along with Porter’s (1985) value chain (discussed earlier in this paper). Viewed from this internal service quality perspective, an organization consists of a network of individuals and units that are linked together with the ultimate objective of satisfying the needs of external customers (Bouranta et al., 2009).

Clearly, organizations that implement formal VoC programs have the preceding kind of network and end goal in mind. With respect to the present study, the connection between CI providers and CI users is key: Users may be viewed as internal customers of providers. The aim of users is to utilize consumer intelligence to support decisions and actions that facilitate improved consumer experiences and relationships. To achieve this objective, users require intelligence that is granular, prioritized, and furnished in a timely manner by providers (Fish, 2021). To the extent that providers meet these requirements, they are delivering high levels of internal service quality to and satisfying the users who are their internal customers.

Unfortunately, high levels of internal service quality and internal customer satisfaction do not always materialize (Gorla, 2012; Gorla et al., 2010; Jun & Cai, 2010). A study of internal service quality provided by a purchasing department conducted by Jun and Cai (2010) indicates that the key determinants of internal customer satisfaction are customer intimacy (understanding internal customer needs), communication (especially internal suppliers seeking feedback about internal service quality from internal customers), and team-based continuous improvement (collaboration between internal suppliers and internal customers to improve service quality). The same study revealed that these aspects of the internal customer experience were the least favorably evaluated. Brandt (2020) findings that a majority of organizations have not achieved desired effectiveness with regard to analysis, dissemination, and utilization of consumer intelligence suggests that, more often than not, the requirements of CI users as internal customers of CI providers may not be fully satisfied—possibly due to poor performance on the internal service quality factors identified by Jun and Cai (2010) and/or additional reasons. Regardless, if user requirements are not being met by providers, then one might expect users to assess VoC program effectiveness less favorably than providers, especially with regard to “downstream” processes and practices related to analysis, dissemination, and utilization.

Actor-Observer Asymmetry

Actor-observer asymmetry centers on differences in perspectives of and explanations of behavior furnished by individuals who are actors in a situation versus individuals who observe those actors/situations. Much of the research on actor-observer asymmetry has focused on differences between actors and observers regarding their respective explanations of the causes of actions and outcomes (Locke, 2002; Malle, 2002, 2006). However, of particular relevance to the present study is a body of research on actor-observer asymmetry related to perception of risks or deficiencies in organizational systems and processes. These studies concentrate on how managers of organizational systems and processes (actors) assess the severity of system/process risks and deficiencies compared to how auditors of those systems/processes (observers) assess the same. Results consistently show that managers and auditors evaluate such risks differently, and that perceived risk levels and problem severity generally are lower among managers than auditors (Addison, 2003; Nuijten et al., 2018; Posey et al., 2014; Schmidt et al., 2001).

Studies of actor-observer asymmetry in assessing the severity of organizational system/process risks and problems appear to rely heavily on an “illusion of control” to explain why managers (actors) perceive less risk/problem severity than auditors (observers). For example, Nuijten et al. (2018) claim to have been “inspired by” a car driving experiment by Horswill and McKenna (1999) which revealed that people who were placed in the driver’s position (driving the car and taking action) were more comfortable with a higher level of risk than passengers (who were not driving and not in control). Nuijten et al. (2018) go on to state that “drawing on this distinction, we conceive of IT managers as being in a position that is similar to that of a car driver, and we conceive of IT auditors as being in a position that is similar to that of a passenger” (p. 82).

With respect to VoC program effectiveness, these findings regarding actor-observer asymmetry suggest that CI providers could be expected to differ from those of CI users based on the nature and proximity of control associated with their respective roles: As actors who are the primary stewards of VoC processes and practices, providers may be more inclined than users to rely on the illusion of control as a heuristic for assessing VoC program effectiveness. Under such circumstances, users once again could be expected to assess VoC program effectiveness less favorably than providers.

Summary and Hypothesis Development

A review of the literature reveals that an organizational member’s knowledge role may create conditions leading to differences among members in the assessment of knowledge management and sharing competence. Research also has shown that the quality of service delivered by internal suppliers to their internal customers has a major impact on achieving success in satisfying external customers. In addition, research on actor-observer asymmetry reveals that the illusion of control may lead those who manage organizational systems and processes to evaluate those systems/processes more favorably than those who do not.

As was discussed earlier, with respect to VoC programs, CI providers can be viewed as integrated players, internal suppliers, and actors, while CI users more closely resemble implementors, internal customers, and observers. Research reviewed above suggests that, because they are heavy on outflows and light on inflows, providers may not receive information from users (who are heavy on inflows and light on outflows) that might cause them to view VoC processes and practices less favorably than they otherwise do. Research reviewed above also suggests that internal customer requirements of users must be met by providers in order to achieve external customer end goals, and that when these requirements are not met, evaluations of internal service quality rendered by users likely will suffer. Finally, research reviewed above suggests that providers (who are the primary VoC program stewards) may rely on the illusion of control as a basis for assessing VoC program effectiveness, leading them to give more favorable evaluations than providers.

Previous studies of VoC program effectiveness (Brandt, 2020, 2022) focused on ten core elements, each of which is connected to one of five key areas (creating a climate for consumer listening and learning, or one of four steps in a closed-loop VoC process—see Figure 1). Accordingly, and because the present study was the most recent phase of the same research program, the following hypotheses were developed and tested:

H1: CI user assessments of key aspects of creating a climate for capturing and leveraging the VoC will be significantly less favorable than assessments of the same made by CI providers.

H2: CI user assessments of key aspects of capturing VoC data will be significantly less favorable than assessments of the same made by CI providers.

H3: CI user assessments of key aspects of analyzing and integrating VoC data will be significantly less favorable than assessments of the same made by CI providers.

H4: CI user assessments of key aspects of disseminating intelligence drawn from the VoC will be significantly less favorable than assessments of the same made by CI providers.

H5: CI user assessments of key aspects of utilizing intelligence drawn from the VoC will be significantly less favorable than assessments of the same made by CI providers.

Method

Sampling Frame

Governmental, educational, and non-profit organizations differ from commercial, for-profit organizations in important ways, including organizational mission and goals, management and governance, operations, and relevant external stakeholders. Even among commercial organizations, companies that sell to consumers differ from those that sell to other businesses. Accordingly, for the purpose of this study, a decision was made to try to minimize the impact of such organizational heterogeneity by narrowing the focus to business-to-consumer (B2C) firms representing multiple sectors and categories of goods and services.²

Participants in this study were drawn from a sampling frame of 5,000 individuals furnished by a company that specializes in building online panels of managers and employees who work in B2C companies.³

Participant Selection and Survey Procedure

Participant selection began with initial emails being sent to all 5,000 members of the sampling frame, inviting them to participate in a survey of consumer listening practices in their respective organizations. Replies expressing willingness to participate in the survey were received from 978 of these initial email recipients (≈20% response rate).

The next step in the participant selection process consisted of emailing a URL to each of the 978 individuals who expressed a willingness to participate, giving them access and ability to respond to a phased online survey. The survey was comprised of three sections: (a) presence/absence of a VoC program screening question; (b) respondent role in the VoC program screening and classification question; and (c) an inventory of questions about perceived VoC program effectiveness. Completed survey responses were received from a total of 776 of these individuals (79% completion rate).

At the beginning of the survey participants were asked “Does your organization have a Voice of the Consumer program—that is, a formal process of capturing, analyzing, and sharing feedback from consumers regarding their experiences with your company, your products and services, and your people?” Respondents who answered “no” immediately were thanked and their survey participation terminated. The 207 respondents who answered “yes” were subsequently asked “Which of the following best describes your personal role and perspective regarding your organization’s Voice of the Customer (VoC) program?” and could select one of four response options:

“I lead a team of individuals who manages and administers our VoC program.”

“I am a member of a team of individuals who manages and administers our VoC program.”

“I am a manager or employee who receives and uses information provided by our VoC program team.”

“None of the above/I have a different role or perspective.”

Respondents who selected the fourth option received an on-screen message thanking them for participating, and indicating that they had completed the survey. The 195 respondents who selected one of the first three of the above options were subsequently given the opportunity to complete a detailed online assessment of VoC program effectiveness in their respective organizations. Once they replied to all assessment items, these respondents received an on-screen message thanking them for participating and indicating that they had completed the survey.

Measures of VoC Program Effectiveness

CI provider and CI users furnished assessments of VoC program effectiveness in their respective organizations using measures and scaling procedures developed by Brandt (2020) and shown in Table 1.

Table 1.

VoC Program Components and Associated Items.

1. Organizational engagement and buy-in

Managers and employees in all parts of the organization buy-in to the importance of listening to the VoC.

Managers and employees in all parts of the organization see great value in our VoC Program.

Managers and employees in all departments regularly and frequently review results and insights drawn from the VoC.

Cross-functional teams regularly and frequently review results and insights drawn from the VoC.

A centralized team oversees our VoC program, and provides ongoing support to managers and employees who see and use VoC results and insights.

2. Methods and mechanisms of consumer listening

We are able to collect enough data to get a clear understanding of the total consumer experience.

Our VoC program includes feedback we collect, as well as feedback consumers furnish without being asked to do so.

Our VoC program includes both structured (e.g., survey ratings) and unstructured (e.g., open-ended comments) data.

All data about the consumer experience are collected regularly and frequently.

We enable consumers to provide their feedback through many different channels and methods.

3. Metrics and targets

Core consumer metrics (e.g., overall satisfaction) are collected and included as part of a corporate scorecard or dashboard.

Measures are gathered regarding how consumers evaluate specific aspects of their experiences, and these measures are used to determine key strengths and opportunities for improvement.

There is a formal process for setting targets for consumer metrics that is easy for employees and managers to explain.

on consumer metrics are evaluated based on whether they are improving and/or remaining at high levels of performance.

Scores on consumer metrics are evaluated based on how they compare to key competitors and/or industry norms and benchmarks.

4. Data analysis and integration

Data from all VoC sources are organized and analyzed using a standard set of categories.

We use one or more formal methods to determine which aspects of the consumer experience have the greatest impact on NPS^® and/or other key outcomes.

We use a formal method to integrate data from all VoC sources to identify where we are performing well, and where there are opportunities for improvement.

We perform analyses that show how consumer metrics are related to key internal operational metrics.

We perform analyses that show how consumer metrics are related to key business outcomes, such as consumer retention and share-of-wallet.

We use text analytics to get maximum insight from all consumer statements and comments.

All of the above analyses are performed at an overall company level, as well as key divisions and departments.

5. Access to and dissemination of VoC data and insights

Senior leaders regularly present results and insights drawn from the VoC at all-company and team meetings and events.

Each department in our organization regularly reviews results and insights drawn from the VoC that are specific to that department or area.

A technology or platform makes data, results, and insights drawn from the VoC accessible to all departments and teams.

Results for all consumer experience measures and consumer comments are displayed using a consistent format or dashboard.

Managers and employees can access VoC data from various sources on-demand, and in real time.

Managers and employees can conduct their own analyses of VoC data using tools available via a technology/platform.

6. Use of the VoC to respond to and manage relationships with individual consumers

A contact center is in place to respond to individual consumers who call with questions, requests, or problems.

An individual, department, or unit is in place to respond to individual consumers who email with questions, requests, or problems.

An individual, department, or unit is in place to respond to individual consumers who post questions, requests, or complaints at the company website.

“Hot alerts” from consumer surveys are used to follow-up with dissatisfied consumers.

Our organization follows-up with and attempts to resolve the dissatisfaction of consumers who post negative messages about the company on social media.

Highly satisfied consumers are encouraged to talk about their experiences by posting messages about the company on social media.

7. Use of the VoC to train and develop managers, employees, and partners

Results and insights drawn from the VoC are used in the training of managers and employees who are expected to use these results and insights to improve business processes and operations.

Results and insights drawn from the VoC are used to coach and mentor managers and employees who have direct contact with consumers.

Consumer metrics and results are used to set performance goals for managers and employees who have direct contact with consumers.

Results and insights drawn from the VoC are used to identify top performers among managers and employees who have direct contact with consumers.

Results and insights drawn from the VoC are used to empower managers and employees who have direct contact with consumers.

Results and insights drawn from the VoC are used to identify opportunities to give managers and employees who have direct contact with consumers better tools and resources.

8. Use of the VoC to drive systemic improvement

We use a formal process to establish accountability for and ownership of priorities for improvement and action drawn from the VoC.

Owners of these priorities form teams that are tasked with developing and implementing action plans.

These teams use a formal process to ensure they thoroughly understand the consumer experience issue that has been given top priority before they begin to develop action plans.

These teams use a formal process to identify root causes of poor or dissatisfactory consumer experiences.

These teams use a formal process to generate and evaluate potential actions and solutions to be included in action plans.

These teams regularly monitor relevant consumer metrics and data, and provide progress updates once action plans have been implemented.

9. Use of the VoC to drive systemic improvement

We use a formal process to establish accountability for and ownership of priorities for improvement and action drawn from the VoC.

Owners of these priorities form teams that are tasked with developing and implementing action plans.

These teams use a formal process to ensure they thoroughly understand the consumer experience issue that has been given top priority before they begin to develop action plans.

These teams use a formal process to identify root causes of poor or dissatisfactory consumer experiences.

These teams use a formal process to generate and evaluate potential actions and solutions to be included in action plans.

These teams regularly monitor relevant consumer metrics and data, and provide progress updates once action plans have been implemented.

10. VoC program sustainability

The compensation of senior leaders who sponsor and own the VoC is tied to actual consumer experience results and improvements.

The compensation of managers and employees who have direct contact with consumers is tied to actual consumer experience results and improvements.

Our organization recognizes and rewards top performers for achieving or surpassing performance goals that are based on consumer experience metrics and results.

Our organization formally celebrates successful improvement efforts as reflected by consumer experience metrics and related results.

Senior leaders plan to continue or increase funding of our VoC program.

Efforts are made periodically to identify specific ways to improve our VoC program.

Respondents specifically were asked to indicate the extent to which the VoC program characteristics, processes, and practices shown in Table 1 have been fully implemented within their respective organizations. Ratings for each item were captured using a five-point scale where 1 = “This does not describe what goes on in my organization,” and 5 = “This very accurately describes what goes on in my organization.” Items associated with each of the ten VoC program components were grouped into survey modules (10 modules in total). The order in which each module appeared in the online survey was randomized across respondents, as was the order in which items within each module were presented.

Data Transformation and Preparation for Statistical Analysis

In preparation for statistical analysis, responses to the survey question about a participant’s role in his/her organization’s VoC program were transformed as follows:

Participants who indicated they were CI providers (i.e., who selected one of the first two survey response options listed above) were grouped and categorized as “Consumer Intelligence Providers” (n = 99).

Participants who indicated they were CI users (i.e., who selected the third survey response options listed above) were grouped and categorized as “Consumer Intelligence Users” (n = 96).

Since members of these two groups were not cohorts (i.e., no attempt was made to enlist pairs of providers and users who represented the same organization), the groups were compared with respect to key organizational characteristics to determine if organizational heterogeneity could be a concern in the statistical comparison of these groups on the dependent measures. Results indicated that the two groups were comparable with respect to company size and VoC program longevity. Based on these results, a decision was made to proceed with statistical a comparison of the two groups with respect to perceived VoC program effectiveness.

In addition to the preceding steps, ratings of effectiveness regarding specific VoC program characteristics and practices in each of the ten survey modules were re-scaled using a percent-of-maximum-possible or POMP method (Cohen et al., 1999). For each module, a respondent’s observed scores on all related items were summed. This sum was then divided by the module’s maximum possible score and multiplied by 100. The result of this transformation was a new summary (POMP) score for each module having a range of 0 to 100.

Results

Test of the Hypotheses

The objective of this study was to test hypotheses that CI user assessments of VoC program effectiveness in their respective organizations will be significantly less favorable than assessments provided by CI providers with respect to creating a climate for consumer listening and learning, and with respect to each of four steps in a closed-loop VoC process. As the key elements of each of these five areas are parts of an organizational listening system, a multivariate approach to data analysis was deemed appropriate. Accordingly, the analysis process proceeded as follows:

A one-way MANOVA was performed (George & Mallery, 2019; Hair et al., 2016) using participants’ classification as either provider or user as the independent variable, and participants’ POMP scores for each of the seven VoC program components as the dependent variables.⁴

Results of the two-group MANOVA applying Pillai’s Trace criterion revealed a significant difference between providers and users (V^(s) = 0.298, F = 7.187, df = 10,184, p < .001, η² = .244).⁵ Therefore, subsequent t-tests among individual pairs of group mean POMP scores were performed using the Bonferroni adjusted p of .005 as the critical value (Grice & Iwasaki, 2008).

Results, summarized in Table 2, reveal the following:

Table 2.

Differences in Mean POMP Scores Between CI Providers and CI Users.^a

VoC program component	CI providers		CI users		d ^b	α ^c
VoC program component	M	SD	M	SD	d ^b	α ^c
Senior sponsorship and support	77.50	0.186	73.15	0.230	0.210	.83
Organizational buy-in and engagement	75.78	0.191	72.83	0.228	0.211	.89
Methods and mechanisms of consumer listening	78.11	0.211	75.78	0.202	0.207	.92
Data analysis and integration	78.65^d	0.132	64.29^d	0.246	0.207	.92
Targets and metrics	79.03^e	0.146	67.31^e	0.253	0.201	.92
Access to and dissemination of VoC data and insights	77.78^f	0.133	62.27^f	0.278	0.212	.92
Use of the VoC to respond to and manage relationships with individual consumers	79.91^g	0.150	70.37^g	0.232	0.196	.93
Use of the VoC to train/develop managers and employees	78.80^h	0.127	71.30^h	0.247	0.197	.94
Use of VoC to drive systemic improvement	77.87ⁱ	0.169	64.31ⁱ	0.271	0.232	.94
Sustainability	78.19	0.143	75.81	0.289	0.240	.95

Means having a common letter in the subscript differ significantly at or beyond the .005 level of confidence (two-tailed). Means can range from a possible minimum of 0 and a possible maximum of 100.

Cohen’s d (effect size).

Cronbach’s alpha (reliability/internal consistency).

The differences between mean POMP scores for providers and users on “Executive Sponsorship,” “Organizational Buy-in,” and “Sustainability” are not statistically significant. H1 is not supported.

The difference between mean POMP scores for providers and users on “Methods and Mechanisms of Listening to Consumers” is not statistically significant. H2 is not supported.

Mean POMP scores for users are significantly lower than those for providers on “Targets and Metrics” and “Analysis and Integration of Consumer Data,” providing support for H3.

Mean POMP scores for users are significantly lower than those for providers on “Access to and Dissemination of Consumer Intelligence,” providing support for H4.

Mean POMP scores for users are significantly lower than those for providers on “Responding to Individual Consumers,” “Training Managers and Employees,” and “Systemic Improvement,” providing support for H5.

Discussion

The present study aimed at contributing to our knowledge and understanding of how member roles may impact perceptions of organizational listening effectiveness. Specifically, this study examined differences between consumer intelligence providers and consumer intelligence users with respect to the perceived effectiveness of VoC processes and practices in their respective organizations. Five hypotheses were developed and tested: Tests of the first two hypotheses revealed no differences between providers and users regarding assessments of effectiveness at creating a climate for listening and capturing VoC data. In contrast, tests of the last three hypotheses revealed that users assess VoC program effectiveness significantly less favorably than do providers when it comes to analyzing, disseminating, and utilizing intelligence drawn from VoC data. These findings reinforce Brandt (2020) conclusion that most organizations “are better at capturing consumer feedback than they are at analyzing, disseminating, or utilizing it to improve products, services, and consumer experiences” (p. 156).

The results of this study have implications for theory and knowledge development in the field of organizational communication. The findings also have important implications for practitioners. These are discussed below, along with limitations of this study, and directions for future research.

Implications for Organizational Communication Theory and Knowledge Development

This paper began with a discussion of the Communicative Constitution of Organizations or “Four Flows” framework described by McPhee and Zaug (2009), with special attention to the exchange of information between an organization and external key stakeholders known as “institutional positioning.” With respect to this flow, an emphasis on organizational message production that has not been counter-balanced by a comparable emphasis on organizational listening also was discussed.

Results of the present study, along with those reported by Brandt (2020, 2022), lend support to Macnamara’s (2016a) assertion that organizations indeed may not as far along with respect to receiving and learning from external stakeholders as they are at formulating and sending messages to them, and that their speaking architectures may be considerably more mature than their listening architectures, at least when it comes to capturing and leveraging the Voice of the Consumer. The implication is that heightened attention to research and knowledge development regarding organizational listening processes, practices, and competence is needed.

The core elements that provided the foundation for assessing VoC program effectiveness in the present study align closely with the components of Macnamara’s (2018) organizational listening architecture, as well as Lakos and Phipps (2004) “culture of assessment.” However, and at the risk of stating the obvious, results of the present study suggest that the mere presence of these elements in a VoC program architecture does not guarantee organizational listening competence or effectiveness. Future studies should focus on how organizations define and achieve competence and effectiveness with respect to building and operating a VoC program.

Earlier in this paper, several models were discussed that, taken together, promote the idea that data from consumers must be transformed into actionable intelligence (Paquette, 2011) that is shared (Argote & Minron-Spektor, 2011) and utilized (Pfeffer & Sutton, 2000) by individuals and organizational units that have a hand in shaping consumer experiences for the purpose of improving those experiences. Furthermore, all activities must be part of a closed-loop process (Deming, 1993; Formento et al., 2013; Pande et al., 2000; Pyzdek & Keller, 2010) that can be used for the purpose of evaluating the success or failure of improvement efforts, and that can be refined over time. Findings from the present study, along with those reported by Brandt (2020, 2022) suggest that most organizations have not achieved desired effectiveness with regard to creating, disseminating, and utilizing consumer intelligence. By way of implication, principles embodied in the models mentioned above do not appear to be making their way into effective VoC processes and practices that are leading to consumer experience improvements. Research focusing on the root causes and impediments to translating theory into practice is needed.

It appears that a member’s position in the organizational hierarchy or echelon, as well as his/her role in the process, may significantly influence how a member evaluates organizational listening effectiveness. Results reported by Brandt (2022) indicate that senior executive VoC program sponsors evaluate the effectiveness of their respective organizational listening program significantly more favorably than middle managers involved in program operations. The present study reveals that, among middle managers involved directly in the closed-loop listening, learning, and response process, CI users evaluate the effectiveness of the respective VoC programs significantly less favorably in three areas than their CI provider counterparts. Ineffective vertical and horizontal communication has been shown to impact organizations in a variety of ways (Lunenburg, 2010; Putnam & Mumby, 2013). It may be that variables such as upward distortion and upward silence also may be in play with respect to the information that senior executives have and use to form impressions about the effectiveness of VoC programs. Similarly, it also may be that poor horizontal and lateral communication between CI providers and CI users inhibits the transfer of knowledge that might improve CI users’ assessments of VoC data analysis, and consumer intelligence dissemination and utilization. These are questions that need to be addressed by communication scholars in future studies of organizational listening, in general, and efforts to capture and leverage the VoC, in particular.

Implications for Practitioners

To the extent that they are struggling with creating, disseminating, and utilizing consumer intelligence to improve consumer experiences, there are at least three steps organizations can take to develop and refine VoC processes and practices.

First, organizations should conduct VoC program assessments on a regular basis. Lakos and Phipps (2004) define a “culture of assessment” as “an organizational environment in which decisions are based on facts, research, and analysis, and where services are planned and delivered in ways that maximize positive outcomes and impacts for customers and stakeholders” (p. 352). This line of thinking aligns closely with the underlying principles of the value chain (Porter, 1985) discussed earlier in this paper. Fortunately, assessment of a VoC program can be accomplished by applying the same measures that were used in the present study. These measures will enable an organization to pinpoint elements of the program that are working well, as well as those in need of improvement. Such an assessment will provide focus and direction for efforts to increase VoC program effectiveness.

A second step organizations can take is engage in benchmarking efforts. Kumar et al. (2006) define benchmarking as “the process of identifying, understanding, and adapting outstanding practices from organizations anywhere in the world to help an organization improve its performance. It is an activity that looks outward to find best practice and high performance and then measures actual business operations against those exemplars” (p. 292). Organizations should seek out benchmarking partners having exemplary VoC programs to identify processes and practices that can be incorporated into their own VoC initiatives. Organizations such as the American Society for Quality (www.asq.org) and the Customer Experience Professionals Association (www.cxpa.org) regularly seek to identify and recognize VoC program exemplars, and to offer networking opportunities to executives and managers interested in benchmarking VoC best practices.

A third step practitioners can take is to seek out and attempt to remove common obstacles to knowledge (in this case, consumer intelligence) transfer and utilization. Riege (2005) has shown that such obstacles often can be traced to individual organizational members, organizational structure, and/or technology. Some of the recommendations Riege offers to address the aforementioned barriers may be in order. Adapted for a VoC program, these include: (a) managing organizational structures and boundaries in ways that facilitate information flow and knowledge transfer, (b) establishing formal cross-functional teams to monitor and evaluate all practices and processes related to CI transfer and utilization, and (c) experimenting (where feasible) with VoC role switching and rotation.

Riege (2005) also recommends investing in technologies that make organizational listening activities and outputs visible and accessible to individuals in all parts and at all levels of an organization. So-called “enterprise feedback management” (EFM) software systems can be used to capture, organize, analyze, and disseminate consumer feedback, and also can be used to document and monitor progress with respect to VoC-driven improvement initiatives (Fish, 2021).

Finally, and if they have not already done so, practitioners should adopt a formal process of translating insights drawn from the VoC into action planning and implementation. Formal methods for such action planning and implementation are described and illustrated by Brandt (2008) and Fish (2021).

Limitations of this Study

As with any investigation, the present study has limitations. Two key limitations of this study stem from sampling procedures. First, a decision was made to restrict participant selection in this study to managers from B2C firms only. Future studies should be conducted among individuals representing business-to-business (B2B) and public sector organizations to determine if (a) the VoC program components and measures used in the present study are equally relevant in those types of organizations and (b) comparable role-based influences on the assessment of organizational listening effectiveness are in play.

Another limitation stemming from sampling procedures relates to the relationship between CI providers and CI users. Providers and users who participated in the present study were not cohorts. Thus, while these managers provided assessments of VoC program effectiveness within their own respective organizations, the actual organizations being evaluated by each group were not the same. In the present study, it was determined that the organizations being evaluated by the two groups of participants were comparable with respect to firm size and VoC program longevity. Nevertheless, a potential confounding effect of organizational heterogeneity on the findings of the present study cannot be established or ruled out. Accordingly, research is needed in which providers and users are members of the same organization (i.e., nested cohorts) and multiple organizations are sampled. This will make it possible to evaluate the relevance of organizational heterogeneity in assessments of VoC program effectiveness, and whether the magnitude of role-based influences varies from one organization to the next.

Conclusion

The growth in interest and inquiry into organizational listening is an intriguing, albeit belated development. The present study aimed to increase our understanding role-based influencers of perceived organizational listening effectiveness pertaining to capturing and leveraging the Voice of the Consumer. Results indicate that such influencers do exist, and that they have the potential to impact success in achieving organizational listening, learning, and development goals. The findings also raise a variety of questions that need to be addressed via future research. Efforts to answer these questions promise to facilitate continued cultivation and refinement of organizational communication theory, inquiry, and practice.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

David Randall Brandt

Notes

Author Biographies

David Randall Brandt (Ph.D., Michigan State University) is an Assistant Professor of Practice in the School of Media and Communication, College of Informatics, at Northern Kentucky University.

William A. Donohue (Ph.D., Ohio State University) is a Professor in the Department of Communication, College of Communication Arts and Sciences, at Michigan State University.

References

Addison

(2003). E-commerce project development risks: Evidence from a Delphi survey. International Journal of Information Management, 23, 25–40. https://doi.org/10.1016/S0268-4012(02)00066-X

Argote

Miron-Spektor

(2011). Organizational learning: From experience to knowledge. Organization Science, 22(5), 1123–1137.

Beasley

T. M.

Sheehan

J. K.

(1994). Choosing a MANOVA test statistic when covariances are unequal [Conference session]. Annual Meeting of the Midwestern Educational Research Association, Chicago, October, 2017. https://eric.ed.gov/?id=ED379296

Blackshaw

(2008). Satisfied consumers tell three friends, angry consumers tell 3,000. Doubleday.

Bolkan

Daly

J. A.

(2009). Organizational responses to consumer complaints: An approach to understanding the effectiveness of remedial accounts. Journal of Applied Communication Research, 37, 21–39. https://doi:10.1080/00909880802592656

Bolkan

Goodboy

A. K.

Bachman

G. F.

(2012). Antecedents of consumer repatronage intentions and negative word-of-mouth behaviors following an organizational failure: A test of investment model predictions. Journal of Applied Communication Research, 40, 107–125. https://doi:10.1080/00909882.2011.573569

Bose

Sugumaran

(2003). Application of knowledge management technology in customer relationship management. Knowledge and Process Management, 10(1), 3–17. https://doi.org/10.1002/kpm.163

Bouranta

Chitiris

Paravantis

(2009). The relationship between internal and external service quality. International Journal of Contemporary Hospitality Management, 21, 275–293. https://doi.org/10.1108/09596110910948297

Brandt

D. R.

(2008). Getting more from the voice of the customer. Marketing Management, 17, 36–42.

10.

Brandt

D. R.

(2020). The current state of corporate voice of the consumer programs: A study of organizational listening practices and effectiveness. International Journal of Listening, 34(3), 156–177.

11.

Brandt

D. R.

(2022). Hierarchical and role-based differences in the perception of organizational listening effectiveness. International Journal of Business Communication, 59(2), 23294884211055839.

12.

Callegaro

Baker

R. P.

Bethlehem

Göritz

A. S.

Krosnick

J. A.

Lavrakas

P. J.

(Eds.). (2014). Online panel research: A data quality perspective. John Wiley & Sons.

13.

Campbell

A. J.

(2003). Creating customer knowledge competence: Managing customer relationship management programs strategically. Industrial Marketing Management, 32(5), 375–383. https://doi.org/10.1016/S0019-8501(03)00011-7

14.

Cohen

Aiken

L. S.

West

S. G.

(1999). The problem of units and the circumstance for POMP. Multivariate Behavioral Research, 34, 315–346. https://doi:10.1207/S15327906MBR3403_2

15.

Davenport

T. H.

Harris

J. G.

Kohli

A. K.

(2001). How do they know their customers so well? MIT Sloan Management Review, 42(2), 63.

16.

Davidow

(2003). Organizational responses to consumer complaints: What works and what doesn’t. Journal of Service Research, 5, 225–250. https://doi:10.1177/1094670502238917

17.

Deming

W. E.

(1993). The new economics. MIT Center for Advanced Engineering Study.

18.

de Ruyter

Wetzels

M. G. M

. (2000). The impact of perceived listening behaviors in voice-to-voice service encounters. Journal of Service Research, 2, 276–284. https://doi:10.1177/109467050023005

19.

Edinger-Schons

L. M.

Lengler-Graiff

Scheidler

Mende

Wieseke

(2020). Listen to the voice of the customer—First steps towards stakeholder democracy. Business Ethics: A European Review, 29, 510–527. https://doi:10.1111/beer.12252

20.

Finn

D. W.

Baker

Marshall

G. W.

Anderson

(1996). Total quality management and internal customers: Measuring internal service quality. Journal of Marketing Theory and Practice, 4, 36-51.

21.

Fish

(2021). The customer experience field guide: A practical manual for getting things done. BookLogix.

22.

Flynn

Valikoski

Grau

(2008). Listening in the business context: Reviewing the state of research. The International Journal of Listening, 22, 141–151. https://doi:10.1080/10904010802174800

23.

Formento

H. R.

Chiodi

F. J.

Cusolito

F. J.

Altube

L. A.

Gatti

S. P.

(2013). Key factors for a continuous improvement process. Independent Journal of Management and Production, 4, 391–415.

24.

George

Mallery

(2019). IBM SPSS statistics 26 step by step: A simple guide and reference. Routledge.

25.

Godes

Mayzlin

(2009). Firm-created word-of-mouth communication: Evidence from a field test. Marketing Science, 28, 721–739. https://doi:10.1287/mksc.1080.0444

26.

Gorla

(2012). Information systems service quality, zone of tolerance, and user satisfaction. Journal of Organizational and End User Computing, 24, 50–73. https://doi.org/10.34105/j.kmel.2016.08.015

27.

Gorla

Somers

T. M.

Wong

(2010). Organizational impact of system quality, information quality, and service quality. The Journal of Strategic Information Systems, 19, 207–228. https://doi:10.1016/j.jsis.2010.05.001

28.

Graham

Havlena

(2007). ‘Finding the missing link:’ Advertising’s impact on word-of-mouth, web searches, and site visits. Journal of Advertising Research, 47, 427–436. https://doi:10.2501/S0021849907070444

29.

Grice

J. W.

Iwasaki

(2008). A truly multivariate approach to MANOVA. Applied Multivariate Research, 12, 199–226. https://doi.org/10.22329/amr.v12i3.660

30.

Grunig

J. E.

Grunig

L. A.

(2013). Models of public relations and communication. In Grunig

J. E.

(Ed.), Excellence in public relations and communication management (pp. 285–325). Lawrence Erlbaum Associates.

31.

Gupta

A. K.

Govindarajan

(1991). Knowledge flows and the structure of control within multinational corporations. Academy of Management Review, 16(4), 768–792. https://doi.org/10.1002/(SICI)1097-0266(200004)21:4%3C473::AID-SMJ84%3E3.0.CO;2-I

32.

Hair

J. F.

Anderson

R. E.

Babin

B. J.

Black

W. C.

(2016). Multivariate data analysis (8th ed.). Pearson.

33.

Harzing

A. W.

Noorderhaven

(2006). Knowledge flows in MNCs: An empirical test and extension of Gupta and Govindarajan’s typology of subsidiary roles. International Business Review, 15(3), 195–214. https://doi.org/10.1016/j.ibusrev.2006.01.001

34.

Hong

S. Y.

Yang

S. U.

(2011). Public engagement in supportive behaviors toward an organization: Effects of relational satisfaction and organizational reputation in public relations management. Journal of Public Relations Research, 23, 191–217.

35.

Horswill

M. S.

McKenna

F. P.

(1999). The effect of perceived control on risk taking. Journal of Applied Social Psychology, 29, 377–391. https://doi.org/10.1111/j.1559-1816.1999.tb01392.x

36.

Johnston

M. K.

Reed

(2017). Listening environment and the bottom line: How a positive environment can improve financial outcomes. The International Journal of Listening, 31, 71–79. https://doi:10.1080/10904018.2014.965391

37.

Jun

Cai

(2010). Examining the relationships between internal service quality and its dimensions, and internal customer satisfaction. Total Quality Management, 21, 205–223.

38.

Kenny

(2016). Excellence theory and its critics: A literature review critiquing Grunig’s strategic management of public relations paradigm. Asia Pacific Public Relations Journal, 17(2), 78–91.

39.

Khosravi

Hussin

A. R. C.

(2018). Customer knowledge management antecedent factors: A systematic literature review. Knowledge and Process Management, 25(1), 12–30. https://doi.org/10.1002/kpm.1557

40.

Kluger

A. N.

Zaidel

(2013). Are listeners perceived as leaders? The International Journal of Listening, 27, 73–84. https://doi:10.1080/10904018.2013.754283

41.

Kumar

Antony

Dhakar

T. S.

(2006). Integrating quality function deployment and benchmarking to achieve greater profitability. Benchmarking: An International Journal, 13, 290–310. https://doi.org/10.1108/14635770610668794

42.

Lakos

Phipps

S. E.

(2004). Creating a culture of assessment: A catalyst for organizational change. portal: Libraries and the Academy, 4(3), 345–361.

43.

Lammers

J. C.

(2011). How institutions communicate: Institutional messages, institutional logics, and organizational communication. Management Communication Quarterly, 25, 154–182.

44.

Laskin

A. V.

(2012). Public relations scales: advancing the excellence theory. Journal of Communication Management, 16(4), 355–370.

45.

Locke

K. D.

(2002). Are descriptions of the self more complex than descriptions of others? Personality and Social Psychology Bulletin, 28, 1094–1105. https://doi.org/10.1177%2F01461672022811008

46.

Lunenburg

F. C.

(2010). Formal communication channels: Upward, downward, horizontal, and external. Focus on Colleges, Universities, and Schools, 4, 1–7. http://www.nationalforum.com/Journals/FOCUS/FOCUS.html

47.

Lusch

R. F.

Vargo

S. L.

(2006). Service-dominant logic: Reactions, reflections and refinements. Marketing Theory. 6, 281–288. https://doi:10.1177/1470593106066781

48.

Maben

S. K.

Gearhart

C. C.

(2018). Organizational social media accounts: Moving toward listening competency. The International Journal of Listening, 32, 101–114. https://doi:10.1080/10904018.2017.1330658

49.

Macnamara

(2016a). Organizational listening: Addressing a major gap in public relations theory and practice. Journal of Public Relations Research, 28(3–4), 146–169.

50.

Macnamara

(2016b). Organizational listening: The missing element in public communication. Peter Lang.

51.

Macnamara

(2018). Toward a theory and practice of organizational listening. International Journal of Listening, 32, 1–23. https://doi:10.1080/10904018.2017.1375076

52.

Malle

B. F.

(2002). The social self and the social other: Actor-observer asymmetries in making sense of behavior. In Forgas

J. P.

Williams

K. D.

(Eds.), The social self: Cognitive, interpersonal, and intergroup perspectives (pp. 189–204). Psychology Press.

53.

Malle

B. F.

(2006). The actor-observer asymmetry in attribution: A (surprising) meta-analysis. Psychological Bulletin, 132, 895. https://doi/10.1037/0033-2909.132.6.895

54.

McPhee

R. D.

Zaug

(2009). The communicative constitution of organizations. Building Theories of Organization: The Constitutive Role of Communication, 10(1–2), 21.

55.

Nuijten

Keil

Van der Pijl

Commandeur

(2018). IT managers’ vs. IT auditors’ perceptions of risks: An actor-observer asymmetry perspective. Information & Management, 55, 80–93. https://doi.org/10.1057/ejis.2016.6

56.

Olson

C. L.

(1974). Comparative robustness of six tests in multivariate analysis of variance. Journal of the American Statistical Association, 69, 894–908. https://doi.org/10.1080/01621459.1974.10480224

57.

Pande

P. S.

Neuman

R. P.

Cavanagh

R. R.

(2000). The six sigma way: How GE, Motorola, and other top companies are honing their performance. McGraw-Hill.

58.

Paquette

(2011). Customer knowledge management. In Schwartz

Te’eni

(Eds.), Encyclopedia of knowledge management, (2nd ed., pp. 175–184). IGI Global.

59.

Pfeffer

Sutton

R. I.

(2000). The knowing-doing gap. Harvard Business School Press.

60.

Pieczka

(2011). Public relations as dialogic expertise. Journal of Communication Management, 15(2), 108–124. https://doi.org/10.1108/13632541111126346

61.

Porter

M. E.

(1985). Competitive advantage: Creating and sustaining superior performance. Simon and Schuster.

62.

Porter

M. E.

(2001). The value chain and competitive advantage. Understanding Business Processes, 2, 50–66.

63.

Porter

M. E.

Kramer

M. R.

(2011). Creating shared value. Harvard Business Review, January-February, 2011.

64.

Posey

Roberts

T. L.

Lowry

P. B.

Hightower

R. T.

(2014). Bridging the divide: A qualitative comparison of information security thought patterns between information security professionals and ordinary organizational insiders. Information & Management, 51, 551–567. https://doi.org/10.1016/j.im.2014.03.009

65.

Purdy

M. W.

Manning

L. M.

(2015). Listening in the multicultural workplace: A dialogue of theory and practice. The International Journal of Listening, 29, 1–11. https://doi:10.1080/10904018.2014.942492

66.

Putnam

L. L.

Mumby

D. K.

(2013). The handbook of organizational communication. SAGE.

67.

Pyzdek

Keller

(2010). The six sigma handbook. McGraw-Hill.

68.

Qui

Lui

(2015). Knowledge role and subunit characteristics in multiunit firms. Journal of General Management, 4, 3–25. https://doi.org/10.1177%2F030630701404000202

69.

Riege

(2005). Three-dozen knowledge-sharing barriers managers must consider. Journal of Knowledge Management, 9, 18–35. https://doi/10.1108/13673270510602746

70.

Rollins

Halinen

(2005, January 6). Customer knowledge management competence: Towards a theoretical framework [Conference session]. Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Big Island, HI, United States (pp. 240a–240a). IEEE.

71.

Schmidt

Lyytinen

Keil

Cule

(2001) Identifying software project risks: An international Delphi study. Journal of Management Information Systems, 17, 5–36. https://doi:10.1080/07421222.2001.11045662

72.

Schmidt-Subramanian

(2014). The state of voice of the consumer programs 2014: It’s time to act. Forrester Research Report.

73.

Tapscott

Ticoll

(2012). The naked corporation: How the age of transparency will revolutionize business. Free Press.

74.

Temkin

B. D.

(2016). The state of voice of the consumer programs, 2016. Temkin Group Insight Report.

75.

Urban

G. L.

Hauser

J. R.

(2004). “Listening in” to find and explore new combinations of consumer needs. Journal of Marketing, 68, 72–87. https://doi:10.1509/jmkg.68.2.72.27793

76.

Vargo

S. L.

Lusch

R. F.

(2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68, 1–17. https://doi.org/10.1509/jmkg.68.1.1.24036

77.

Vargo

S. L.

Lusch

R. F.

Akaka

M. A.

(2017). Service-dominant logic: A review and assessment. Review of Marketing Research, 6, 125–167.

78.

Zinzer

B. A.

Brunswick

G. J.

(2016). The evolution of service-dominant logic and its impact on marketing theory and practice: A review. Academy of Marketing Studies Journal, 20, 101–117.