Leader Communication and Follower Identity: How Leader Motivating Language Shapes Organizational Identification through Cultural Knowledge and Fit

Abstract

This study examines the links between leader communication (as conceptualized through motivating language) and follower organizational identification as mediated by follower cultural knowledge and fit. Results show that motivating language has a positive and strong effect on follower organizational identification with a one standard deviation increase in motivating language corresponding to over a half of a standard deviation increase in follower organizational identification. This influence comes partly through growth in a follower’s cultural knowledge and fit, but also through a direct influence. Model testing occurred with subjects from the USA and India with the model fitting equally well in both nations. These findings have important implications for research and practice which are explained in the paper’s discussion and conclusion section.

Keywords

motivating language organizational identification cultural knowledge cultural fit

Introduction

How can one person’s language modify another person’s identity? More concretely for this study, how does a leader’s communication help to shape an internal follower’s (as compared to an external stakeholder such as a supplier or a customer) feeling of being connected with an organization? Organizational identification—when someone’s self-concept intertwines with the characteristics of the organization to which he or she belongs—has long been observed by organizational, psychological, and communication researchers (Madlock, 2008; Mael & Tetrick, 1992; Myers & Kassing, 1998; Riketta, 2005; Schneider et al., 2017; Sias & Duncan, in press). In these studies, scholars have discovered that multiple factors influence how closely a follower identifies with an organization—such as the organization’s reputation, individual employee characteristics, social media strategies, and an organization’s general communication climate. Importantly, leader communications (although understudied in this context) have emerged as being prominent in fostering a worker’s organizational identification through such persuasive forces as leader communication competence, transformational leadership, and a leader’s contributions to the organization’s communication climate (Alqahtani, 2015; Barge & Schlueter, 1988; Henderson et al., 2015; Madlock, 2008; Men, 2014; Myers & Kassing, 1998).

This progress is commendable since internal stakeholders’ organizational identification impact such key outcomes as intrapreneurship, job satisfaction, commitment, performance, decision- making, and retention (Riketta, 2005; Sias & Duncan, in press). Yet major knowledge gaps challenge us. Case in point, we have scant awareness into how leadership communication, organizational identification, and organizational culture work together. Insights about how these relationships operate are critical since organizational identification is already conceptualized as embedded in culture (Barge & Schlueter, 1988; Fairhurst, 2001; Henderson et al., 2015; Myers & Kassing, 1998) and one study even shows that top leader vision may connect the two (Jo Hatch & Schultz, 1997). Thus, more discoveries about the interplay between leadership communication, organizational culture, and member identification would not only advance research, but could also help grow better worker organizational fit (felt inclusion), which is vital to performance, job satisfaction, and retention (Cascio, 2012; Hsieh, 2010). Scholars have called for such exploration, notably Henderson and colleagues (2015), Barge and Schlueter (1988), Myers and Kassing (1998) and Men (2014), in their advocacy for more studies which emphasize leader communication’s role.

This new knowledge can be gained through the application of motivating language theory (MLT) which is a comprehensive framework and positive template of leader oral communication (J. Mayfield & Mayfield, 2018d; Sharbrough et al., 2006; Sullivan, 1988). Plus face-to-face talk is the most pervasive and effectual communication channel that leaders use when interacting with followers (Mintzberg, 1973; Tengblad, 2006). Moreover, this research brings forth multiple benefits by making headway on MLT understanding as well as in the areas of organizational identification and culture. To date, the relationship between leader motivating language and organizational identification has not been addressed, despite recent studies that show significant links between MLT and related, but distinct factors such as national culture (Madlock & Hildebrand Clubbs, in press; Madlock & Sexton, 2015; J. Mayfield & Mayfield, 2012; Mayfield et al., 2001), school climate (Alqahtani, 2015; Sabir, 2018), and commitment (Madlock & Sexton, 2015; Sabir & Bhutta, 2018).

In sum, we assert that leader talk guides followers to identify with the unique culture of the organization where they are members through motivating language. Precisely, our study contends that leader motivating language (ML) (Holmes, 2012; J. Mayfield & Mayfield, 2018d; Mayfield et al., 1995; Sullivan, 1988) increases follower organizational identification by expanding her or his perceived understanding of an organization’s culture and how well they fit (feel included) into that environment. The resulting contributions from this research agenda should enhance our scholarly and practical awareness of how leaders communicatively enhance follower organizational identity (and the attendant rewards) through alignment with organizational culture. Going further, this investigation should make new inroads into comprehending how motivating language processes operate, a goal supported by multiple scholars (Holmes & Parker, 2017; Mayfield & Mayfield, 2019). Lastly, we seek to add to extant knowledge about the generalizability of these insights through a cross-national comparison between two disparate national settings—the USA and India.

We present this agenda with a literature background exploring organizational identification and motivating language, expectations on how motivating language should influence both cultural knowledge and cultural fit, and how, in turn, these two constructs influence an internal stakeholder’s organizational identification. Next, we will explain study methodology followed by a discussion and conclusion.

Background Literature

Organizational Identification

In developing our argument for how leader communication influences a follower’s organizational identification, we shall begin at the end—with the desired outcome a leader wants to achieve. The organizational identification construct taps into how much someone bonds with the in which they are a member (for internal stakeholders) (Henderson et al., 2015; Reed et al., 2016; Sias & Duncan, in press). The stronger the identification, the more someone feels personally invested in events and people associated with the organization. Such identification has a profound impact on someone’s psychological state and the behaviors he or she undertakes in the organization. For example, people with high identification can feel proud of their organization and enjoy telling external others about their involvement. Conversely, organizational members still may have high identification by remaining loyal to its values but express dissent toward certain actions such as a committed airline mechanic who disagrees with company treatment of passengers.

While researchers have studied organizational identification in many different contexts, the earliest and still most widespread research takes place in business contexts (Riketta, 2005). In these studies, researchers found organizational identification predicts many work-related attitudes (such as job satisfaction and job involvement) and workplace behaviors (such as performance and absenteeism). Findings also show factors influencing organizational identification range from those impossible to change (such as a person’s age) to those largely out of an organization’s control (such as an organization’s prestige) to those where organizations have the power to intervene (such as job challenge and communication climate) (Riketta, 2005).

Among the forces that can actually nurture organizational identification, leader communication is among the most cited. Why? Many scholars argue that organizational identification is inherently communicative through leaders’ persuasive words (Cheney, 1982; Henderson et al., 2015; Scott et al., 1998; Sias & Duncan, in press) For example, Jablin (2001) captured the dynamic ways that leader communication impacts organizational entry and assimilation (inboarding), including new member affiliation through sharing cultural knowledge. Similarly, leader communication that augments follower organizational identification should be a relatively achievable objective through such interventions as training and rewards which grow the requisite leader communication skills. To date, however, scant research has examined how this linkage emerges. We believe that the motivating language framework offers a vital path for bridging this influence on organizational identification.

Motivating Language Theory

Sullivan (1988) initially proposed motivating language theory (under the name of motivational language) as a means of expanding the boundaries of leader speech and forging its explicit connections with organizational and employee well-being. Put simply, Sullivan contended that leader language is highly related to the follower motivation that drives performance and job satisfaction. Traditionally leader oral communication has mainly focused on task-oriented messages which limits its motivational potential. As a remedy, motivating language theory offers a richer palette of speech that spurs such motivation and encourages a follower to bring their whole person to work instead of a compartmentalized self. These three linguistic dimensions, hands (direction-giving language), heart (empathetic language), and spirit (meaning-making language) will be explained in more detail later in this section (J. Mayfield & Mayfield, 2018c; Mayfield & Mayfield, in press).

Subsequent researchers have been inspired by and have refined the MLT model (J. Mayfield & Mayfield, 2018d). Studies reveal that leader motivating language (ML) significantly and positively influences many critical follower behaviors including performance, work attendance, perceived leader competence, job satisfaction, commitment, creativity, perceived supportive work climate, retention, and effective decision-making (Alqahtani, 2015; Holmes, 2012; Madlock & Sexton, 2015; J. Mayfield & Mayfield, 2018d; M. Mayfield, 2009; Mayfield & Mayfield, 2017a, 2018c; Sabir, 2018; Sabir & Bhutta, 2018; Sharbrough et al., 2006; Wang et al., 2009) The robustness of these connections compares quite favorably with relational strengths between most other management concepts. Relatedly, investigations have supported ML’s generalizability through numerous scholars’ congruent findings across multiple settings (J. Mayfield & Mayfield, 2018d). Of note, causal inferences between motivating language and outcomes have emerged from quasi-experimental design studies (Fan et al., 2014; Wang et al., 2009).

Before continuing further, we offer a brief definition of MLT with examples and discussion of its theoretical roots. MLT’s three dimensions are visually displayed in Figure 1. The first dimension, the hands or direction-giving language, refers to prevailing leader communication norms to focus on task and goal oriented messages (J. Mayfield & Mayfield, 2018a; M. Mayfield & Mayfield, 2017b). We do not denigrate the importance of such talk which conveys the nuts and bolts of work communication in the spirit of Barnard (1968). Leaders speak direction-giving language to transparently share needed information about requisite actions toward goal attainment, to dispel role ambiguity, and to articulate reward contingencies. Goal setting, prioritizing, and constructive performance feedback are examples of such talk. The foundations of the hands or direction-giving speech are nested in such theories as goal setting, the job characteristics model, expectancy, the Ohio State and University of Michigan task-oriented leadership, and path goal (directive leadership) (Hackman & Oldham, 1980; House, 1971; Locke & Latham, 2002; J. Mayfield & Mayfield, 2018d; Miner, 2005; Sullivan, 1988; Yukl, 2013).

Figure 1.

A graphical representation of motivating language’s three facets and their aspects. The figure shows the major aspects of each motivating language facet within each area. (This model was originally created by Milton & Jacqueline Mayfield and released under a CC BY-SA 4.0 license.).

The second dimension, the heart or empathetic language, diverges from the leader communication norm of direction-giving talk (Mayfield & Mayfield, 2016; J. Mayfield & Mayfield, 2018e). Nonetheless, empathetic language extends a leader’s speech versatility and is a significant influence on MLT associated outcomes (J. Mayfield & Mayfield, 2018d; Sullivan, 1988). Empathetic language, initiates and maintains supportive emotional relations between a worker and a leader. Leaders bond with followers via empathetic language when they praise a worker’s successes or advocate for her or his efforts. For example, a supervisor’s commiseration with a direct report’s task challenges embodies empathetic talk as does her or his expressed compassion for the worker’s personal setbacks. The theoretical sources of empathetic language include emotional intelligence, compassion in the workplace, the Ohio State and University of Michigan’s person-centered leadership, and path goal theory (supportive leadership) (Dutton et al., 2014; Goleman, 1998; House, 1971; Miller, 2013; Miner, 2005; Yukl, 2013).

MLT’s third dimension, the spirit or meaning-making language, advances the boundaries of leader talk even further (J. Mayfield, 2009; J. Mayfield & Mayfield, 2018b). Although data suggest that meaning-making speech is the least often adopted (and most likely underutilized!) it is a powerful influence in the motivating language triad (J. Mayfield & Mayfield, 2018d; Sullivan, 1988). Meaning-making language intersects a follower’s personal goals with the organization’s vision, recognizes a follower’s unique work contributions, and promotes understanding of the culture (Gutierrez-Wirsching et al., 2015; Mayfield et al., in press; Sullivan, 1988). For example, meaning-making speech happens when a leader articulates how a follower’s individual values graft with those of an organization and what cultural rules to follow in order to “fit in.” (“Your creative skills are just what this company needs”, “Attending the president’s party is a “command performance”, and “Don’t be seen leaving meetings early without a good reason.”) Also, meaning-making language expresses cultural values informally through stories and narratives (J. Mayfield & Mayfield, 2018d; Sullivan, 1988). Equally important, meaning-making speech becomes very germane during times of organizational change, entry, and assimilation when a follower’s interpretation of organizational vision and values may be in sense-making mode. The theoretical sources for the spirit include the job characteristics model (task significance), sense-making theory, and organizational entry and assimilation (Hackman & Oldham, 1980; Jablin, 2001; Weick, 1995). Figure 1 provides a graphical overview of all three motivating language factors and the leader communication areas associated with each factor.

Four key assumptions must be met for ML to optimize its associated benefits. Above all, a leader has to speak words that are congruent with her or his actions (Sullivan, 1988). This assumption of behavioral integrity as an antecedent to ML has been supported in a recent study by Holmes and Parker (2017). Second, all three forms of motivating language must be used at appropriate times to deliver expected positive outcomes (J. Mayfield & Mayfield, 2018d; M. Mayfield & Mayfield, 2018d; Sullivan, 1988). Third, the scope of ML is leader-to-follower talk although the leader’s intended message must be accurately perceived by the follower. For this reason, the ML scale (Mayfield et al., 1995) relies on follower message decoding. Fourth, motivating language’s breadth is presumed to cover all forms of leader-to-follower oral communication (J. Mayfield & Mayfield, 2018d; Sullivan, 1988). We clarify this last assumption by adding that a singular direction (leader-to-follower) only exists because investigators have not yet had time to explore how ML occurs in discourse. Scholars believe that in fact motivating language often occurs in dialogs (J. Mayfield & Mayfield, 2018d; Sullivan, 1988) and not always in monologues. Finally, ML is best viewed as a state, not a trait, which means it is a skill developed through training, reflection, and experience (Mayfield & Mayfield, 2019; M. Mayfield & Mayfield, 2018a).

How Leader Motivating Language Shapes a Worker’s Organizational Identification

This section explains our premise that motivating language is closely connected to a given organizational identification level. In brief, we expect higher motivating language to create greater cultural knowledge of and experienced fit within an organization for members. Direction-giving language (hands) explicates cultural goals and priorities. Empathetic language (heart) offers the psychological safety (a sense of security and acceptance in one’s work context) (Edmondson & Lei, 2014; Mayfield, 1994) for members to experience cultural inclusion. And meaning-making talk (spirit) both explains a unique culture and grafts an individual follower’s values with the bigger organizational purpose. In these ways, the three ML dimensions construct the link between understanding and cultural fit (and can increase cultural fit as well). These two outcomes, understanding and fit, are predicted to grow a person’s organizational identification. While we delegate to a research question if these mediating variables fully explain motivating language’s relationship to organizational identification, we can present our main belief in the following hypothesis:

H₁: Leader motivating language positively predicts follower organizational identification.

Motivating language should elicit cultural knowledge—how accurately someone grasps the norms, rites, symbols, rituals, vision, and values of an organization (Schein, 2016; Smircich, 1983)—through all of its three dimensions. Meaning-making language or spirit most obviously should influence cultural knowledge since leaders favor this type of speech to transmit cultural enlightenment. However, direction-giving and empathetic language also play pivotal roles in augmenting cultural knowledge by reducing barriers which impede such understanding. Direction-giving language or hands guides a person understand her or his required tasks/goals and (ideally) how those tasks/goals fit into the organization’s strategic vision (J. Mayfield & Mayfield, 2014; M. Mayfield & Mayfield, 2018e; Mayfield et al., 2007). By conveying such information, a leader helps that person see which tasks and goals the organization prioritizes and rewards—a linking pin in any organization’s culture (Schein, 2016). Empathetic language or heart gives a follower emotional affiliation while he or she is learning about and gaining deeper insights into an organization’s culture (Ma et al., 2018; M. Mayfield & Mayfield, 2012b, 2016). Without such sustenance, a person may withdraw in their attempt to comprehend an organization to which he or she belongs. This reasoning introduces the next hypothesis:

H₂: Leader motivating language positively predicts follower cultural knowledge.

Motivating language should leverage cultural fit (how much someone feels included and psychologically safe in a given culture) in two ways. First, motivating language should directly influence a person’s cultural fit. As with cultural knowledge, leaders can use meaning-making talk to connect a person’s personal goals and aspirations with the organization’s culture and vision. The more a leader persuades someone to see the congruence between her or his values and the organization’s mission, the more a follower will see a personal affiliation with the organization’s culture. Direction-giving language shows someone how the link can be implemented and empathetic language via messages of acceptance and support cultivates an emotional rapport with the organization’s culture. Hypothesis 3 formally distills these ideas.

H₃: Motivating language positively predicts a follower’s cultural fit.

Secondly, cultural knowledge should mediate motivating language’s persuasive influence. People must first comprehend an organization’s culture before they perceive alignment (fit) with the culture. Since our model proposes that higher motivating language invites deeper cultural knowledge, it logically follows that ML will similarly augment cultural fit through cultural knowledge. Hypothesis 4 presents the expected link between cultural knowledge and fit.

H₄: Cultural knowledge positively predicts cultural fit.

However, motivating language should have yet another vital function in connecting cultural knowledge and cultural fit. While someone may understand a given organizational culture, heightened awareness does not necessarily produce fit. As a person arrives at new cultural knowledge, the follower may observe more attributes with which he or she disagrees and which in turn actually decrease a sense of belonging. In such disconnecting situations, a leader’s motivating language can guide a follower to overcome such cognitive dissonance in by reinforcing how their personal values and talents link with the organizational vision (meaning-making language). congruently, leaders express empathetic language to reduce the disappointment and stress of clashes between personal beliefs and organizational goals, while direction-giving language informs a worker about how to cope and successfully perform unfamiliar or uncomfortable tasks. As such, motivating language should moderate the relationship between cultural knowledge and fit, and the next hypothesis formally states this expectation:

H₅: Motivating language positively moderates the relationship between cultural knowledge and cultural fit.

A Model of Motivating Language’s Influence on Organizational Identification

Motivating language influences organizational identification through the mechanisms of cultural knowledge and cultural fit. The model proposes that these two constructs directly influence organizational identification while motivating language has a mediated effect on organizational identification. Cultural knowledge invokes organizational identification by clarifying what the organization’s culture entails. An employee cannot identify with something he or she does not understand. Hypothesis 6 presents this idea.

H₆: Cultural knowledge positively predicts organizational identification.

Yet as with the relationship between cultural knowledge and fit, enhanced understanding may not always lead to increased identification. Enriched understanding may simply reveal cultural attributes with which a follower dissents. Taking this consideration into account, we expect identification to increase most when the person perceives similarity between their values and those of the culture. We present this idea in the following hypothesis:

H₇: Cultural fit moderates the relationship between organizational understanding and organizational identification.

We also expect cultural fit to directly influence organizational identification. Cultural fit captures how comfortable and accepted (included) someone feels in an organization—whether or not they feel connected with a given organization’s norms and shared values. The more someone perceives compatibility with an organization’s culture the more probable that identification with the organization will ensue. Stated another way, when a follower feels alienated from an organization’s culture that person will find it difficult to identify with the organization. Hypothesis 8 captures this assertion.

H₈: Cultural fit will positively predict organizational identification.

With these hypotheses in place, we now present our research question.

RQ₁: Do cultural knowledge and cultural fit fully or partially mediate the relationship between motivating language and organizational identification?

Figure 2 presents the model as a whole and summarizes the expected links between the constructs.

Figure 2.

A proposed model of the link between leader motivating language and follower organizational identification. The model shows that motivating language should positively influence a follower’s cultural knowledge and cultural fit and moderate this relationship as well. In addition, the follower’s cultural knowledge increases the cultural fit between the organization and the follower. Finally, a person’s increased cultural knowledge and fit will lead to improved organizational identification as moderated by a person’s cultural fit. Please note that the model shows a fully mediated relationship between leader motivating language and follower organizational identification (to reduce model clutter), but we leave open the possibility that motivating language has a partially mediated relationship.

Research Methodology

Data Collection

To test the proposed model, we collected data through the Mechanical Turk site (Buhrmester et al., 2011) from respondents in the USA and India. The site advertises jobs (including survey participation) to a diverse audience whose demographics closely represent the general population trends in the US and India (Difallah et al., 2018). This data collection method has quickly attained respectability in social science research. Through this site, we collected data from 439 adult working respondents in the USA and 412 from India. (We compensated respondents US$0.70 for survey completion.) We also included two attention checks to screen respondents, inserting items asking participants to select a different specified answer each time. After removing any respondent missing either attention check we had a respondent pool of 384 from the USA and 349 from India.

Sample Characteristics

As expected, this participant recruitment method yielded a diverse sample, both in terms of personal demographics and in terms of workplace background. Table 1 gives an overview of participant personal demographics and Table 2 provides information on workplace demographics.

Table 1.

Personal Demographic Information.

Demographic characteristic		USA	India
Gender	Female	45.5%	33.4%
Gender	Male	55.0%	66.6%
Ethnic Group	White (non-Hispanic)	72.9%	NA^a
	Black or African-American	13.8%
	Hispanic or Latino	4.5%
	Asian or Asian-American	7.4%
	Middle-Eastern	0.8%
	Native American	0.5%
Age	Mean	36.8	29.7
	Median	34.5	28.0
	Standard Deviation	10.7	6.0
Education	High School	0.0%	0.3%
	Associates Degree	8.3%	0.3%
	Some College	7.8%	2.9%
	Four Year College Degree	66.7%	65.2%
	Masters	15.3%	30.6%
	Terminal	1.9%	0.8%

No commonly agreed upon ethnic grouping exists for Indian participants.

Table 2.

Work Demographic Information.

Demographic characteristic		USA	India
Work experience	Full Time Work	14.33 years	6.48 years
	Part Time Work	4.18 years	2.08 years
	Time With Org.	5.94 years	4.87 years
	Time In Job	4.77 years	3.89 years
	Time With Boss	4.22 years	3.80 years
Work sector	Consumer good production—discretionary	6.5%	1.6%
	Consumer good production—staples	0.9%	0.5%
	Education	11.1%	11.4%
	Energy	0.6%	2.4%
	Food Service	5.0%	1.6%
	Financial	13.5%	14.4%
	Government (non-military)	5.3%	0.3%
	Health Care	13.5%	7.3%
	Industrials	8.8%	10.3%
	Information Technology	18.8%	42.3%
	Materials Extraction	0.9%	0.5%
	Military	0.0%	0.0%
	Real Estate	1.8%	0.3%
	Retail Sales	10.9%	3.5%
	Telecommunication	2.3%	3.3%
	Utilities	0.3%	0.3%
Company size	Small (less than 100 employees)	31.6%	30.9%
	Medium (from 100 to 1,000 employees)	43.6%	53.7%
	Large (more than 1,000 employees)	24.7%	15.4%
Work type	Unskilled Labor	6.5%	4.1%
	Skilled Labor	43.8%	42.7%
	Professional Work	49.7%	53.2%
Work category	Management	26.8%	31.8%
	Independent Contractor	2.4%	0.3%
	Owner-Operator	0.5%	1.9%
	Office and Adm. Support	16.8%	9.7%
	Healthcare Support	8.7%	5.1%
	Protective Services	1.9%	1.6%
	Food Preparation Services	4.6%	1.9%
	Personal Care	0.8%	3.5%
	Installation, Maint., and Rep.	0.8%	3.5%
	Ground Clean. & Maint.	0.8%	0.0%
	Other Service	5.4%	4.0%
	Trade Worker or Laborer	4.3%	1.3%
	Prof., Scientific, or Tech.	20.6%	30.4%
	Educator	5.4%	5.1%

Construct Measures

To capture study constructs, we used previously existing, validated measures where possible. We used the motivating language scale to measure leader motivating language use (Mayfield et al., 1998), we captured person-culture fit with Cable and DeRue’s (2002) measure, and organizational identification with Mael and Ashforth’s (1992, 1995) scale. However, no suitable scale existed for measuring someone’s understanding of her or his organization’s culture.

As such, we created a scale to capture how well someone knew the culture he or she worked in. We developed 10 items that asked about various aspects of an organization’s culture (such as organizational history and expected behaviors), and then tested these items with a confirmatory factor analysis. (See Appendix 1 for the complete motivating language and Appendix 2 for the cultural knowledge scale and full details on its development.) The use of all self-report measures creates the potential for common methods bias and social response bias (MacKenzie & Podsakoff, 2012; Podsakoff et al., 2003, 2012), so we used a control for marker variables strategy to deal with this issue. Appendix 3 presents full information on this strategy, but our results showed little influence from these potential validity issues.

Scale Generalizability and Descriptive Information

After controlling for potential common methods bias, we performed a set of confirmatory factor analyses on the cultural knowledge scale. We found no significant differences in the way the scale operated across the nations, and that the scale had strong measurement properties with all items loading as expected. For structural equation model testing, we dropped some items with weaker loadings to generate a final measurement scale (Osborne, 2007).

For our other measures, we controlled for bias in the same way as the cultural knowledge measure. We also tested for cross-national comparability in the same way, creating a measurement model with all scales. This test showed that the scales operated in the same way across both countries. All scales had adequate reliability and factor structure results. Table 3 provided relevant descriptive statistics for all items. Figure 4 offers the correlations between constructs including our methods bias control items, while Figure 5 gives the correlations between constructs after we controlled for this bias. These tables also show the item distributions and bivariate relationships. For the study variables, these graphics do not indicate strong non-normality or non-normal relationships. (However, the taste preference measure appears to have more of a uniform distribution than a normal one.)

Table 3.

Construct Descriptives.

Construct	Median	Mean	Variance	Reliability
Direction-giving language	3.500 (3.600)	3.369 (3.603)	0.693 (0.380)	0.92 (0.88) [0.91]
Empathetic language	3.500 (3.667)	3.429 (3.672)	0.891 (0.473)	0.90 (0.83) [0.88]
Meaning-making language	2.875 (3.500)	2.774 (3.442)	0.955 (0.452)	0.91 (0.84) [0.90]
Cultural knowledge (full)	3.700 (3.600)	3.707 (3.664)	0.482 (0.372)	0.87 (0.74) [0.85]
Cultural knowledge (reduced)	—	—	—	0.82 (0.74) [0.80]
Cultural fit	4.000 (4.000)	3.696 (3.939)	0.894 (0.532)	0.92 (0.76) [0.87]
Organizational identification	3.500 (4.000)	3.323 (3.884)	0.906 (0.446)	0.89 (0.80) [0.88]
Taste preference	3.000 (3.500)	2.873 (3.508)	1.434 (0.578)	0.92 (0.66) [0.85]
Social bias	1.455 (1.364)	1.462 (1.344)	0.028 (0.038)	0.75 (0.54) [0.61]

Note. Numbers outside of the parentheses are from the USA sample, inside of the parentheses are for the Indian sample, and the combined sample reliabilities are inside square brackets.

Results

Model Testing

To test the model itself, we used structural equation modeling with the lavaan (Rosseel, 2012) package for the statistical programming language R (R Core Team, 2018). As with the measurement instruments, we tested to see if the model operated in the same way in the USA and India. We began our analysis by first fitting the model’s non-interaction terms (since a poor fit in this model would preclude further testing the interaction terms) and, as with first testing the measurement model, followed the guidance of SEM experts who consider incremental testing a best practice (Osborne, 2007). In addition, we tested for model differences between the USA and India.

This model showed no significant difference between the two sample groups (a BIC 63.248 point lower for the model with constrained loadings across nation) and a good fit between the model and sample data. The model had a CFI of 0.976, a TLI of 0.968, an RMSEA of 0.052 (with a confidence interval of 0.042 to 0.062), and an SRMR of 0.040.

However, when we added in the interaction terms, the model’s fit became significantly worse (a BIC over 11,000 points higher) and no longer provided a good fit with the data. As such, and contrary to expectations, it appears that motivating language and cultural fit work in a direct rather than a moderating fashion. From this result, we next turn to looking at how the constructs influence each other.

Leader motivating language significantly influenced follower cultural knowledge with a path loading of 0.406 (standardized path of 0.459) and significantly influenced cultural fit with a path loading of 0.499 (standardized path of 0.452). In turn, a person’s cultural knowledge had a significant influence on that person’s cultural fit with a path of 0.255 (standardized path of 0.204). Motivating language also had a significant indirect effect on cultural fit (through its influence on cultural knowledge) of 0.103 (standardized path of 0.094) creating a total (significant) effect of ML on cultural fit of 0.602 (standardized path of 0.545).

For the influences on a worker’s organizational identity, only cultural fit had a significant direct effect. Cultural fit had a path loading of 0.545 (standardized path of 0.619). However, cultural knowledge did have a significant indirect effect (mediated by a person’s cultural fit) with organizational identification. A person’s cultural knowledge had a total influence (direct and indirect) of 0.156 (standardized path of 0.142).

For our research question about motivating language’s direct influence on organizational identity, we found that ML had a significant direct effect of 0.160 (standardized path of 0.165) and an indirect effect of 0.335 (standardized path of 0.345). Therefore, out of ML’s total effect on OI (0.495, standardized 0.510), the mediating variables explain approximately two-thirds of the relationship with about one-third of its influence remaining unexplained.

As for variance explained, all variables in the model explained 53.2% of the variance in a person’s organizational identification, 33.0% of the variance in cultural fit, and 21.0% of the variance in cultural knowledge. Table 4 provides full model results and Figure 3 provides a graphical representation of those results.

Table 4.

Model Results.

Relationship	Path	Lower C.I.	Upper C.I.
Motivating language → Cultural knowledge	0.406 (0.459)	0.320	0.493
Motivating language → Cultural fit (Direct)	0.499 (0.204)	0.393	0.604
Motivating language → Cultural fit (Indirect)	0.103 (0.094)	0.049	0.158
Motivating language → Cultural fit (Total)	0.602 (0.545)	0.507	0.697
Motivating language → Organizational identification (Direct)	0.160 (0.165)	0.065	0.165
Motivating language → Organizational identification (Indirect)	0.335 (0.345)	0.251	0.419
Motivating language → Organizational identification (Total)	0.495 (0.510)	0.398	0.593
Cultural Knowledge → Organizational identification (Direct)	0.017 (0.016)	−.090	0.124
Cultural Knowledge → Organizational identification (Indirect)	0.139 (0.126)	0.065	0.213
Cultural Knowledge → Organizational identification (Total)	0.156 (0.142)	0.037	0.142
Cultural knowledge → Cultural fit	0.255 (.204)	0.127	0.382
Cultural fit → Organizational identification	0.545 (0.619)	0.437	0.652
Variance accounted For—R²
Cultural knowledge	21.0%
Cultural fit	33.0%
Organizational identification	53.2%

Note. The table presents the standardized paths in parentheses.

Figure 3.

Model results of the link between leader motivating language and follower organizational identification. Analysis results showed that leader motivating language significantly influenced follower organizational identification as partially mediated by the follower’s cultural knowledge and cultural fit. All shown paths were significant at the 0.05 level with non-significant paths omitted. Contrary to initial expectations, motivating language had direct and indirect effects on organizational identity while cultural knowledge’s effect was fully mediated by cultural fit.

Figure 4.

Variable Relationships—No Control for Bias.

Figure 5.

Variable Relationships—Controlled for Bias.

Sensitivity Analysis

After testing the model, we performed sensitivity analyses to check the robustness of our causal findings. Sensitivity analyses allow a researcher to check the robustness of a set of findings if certain key model assumptions are violated, and we found little evidence that external validity threats could credibly alter our analysis findings. (See Appendix 4 for full information on our testing and results.)

Table 5 presents our full sensitivity analysis for the model. As the table shows, most relationships have less than a 1% chance of their causal relationship being negated by an omitted variable. The two exceptions are CK → CF (with only a 6.25% chance) and CK → OI (with a 30.25% chance of negation). As such, omitted variable bias only seems a likely threat in the CK → OI relationship. We also found our results to be generally resistant to sampling bias with most relationships requiring over 78% of the general population (having no relationship between constructs) to have been omitted from our sample in order to invalidate our conclusions of causal influence. The two exceptions were CK → CF relationship (which would require omitting nearly half of the population) and CK → OI (which would still require omitting nearly a quarter of the population). Considering the diverse demographic sample characteristics, and our omitted variable test, these results indicate that the findings are generally resistant to sampling bias.

Table 5.

Sensitivity Analysis for Variable and Subsample Omission.

Construct relationship	Required omitted variable strength to each construct	Chance of omitted variable (%)	Population omission to disprove causality (%)
ML → OI	0.660	0.25	80.4
ML → CK	0.616	0.25	78.8
ML → CF	0.863	<0.01	84.2
CK → CF	0.326	6.25	49.8
CK → OI	0.190	30.25	23.7
CF → OI	0.729	0.25	80.2

Based on these tests, we can conclude that our results provide a good understanding of the relationship between our constructs. Table 6 provide a summary of our hypotheses and research question results.

Table 6.

Summary of Hypotheses and Research Question Results.

Hypothesis/Research Question	Results
H₁: Leader motivating language positively predicts follower organizational identification.	Supported
H₂: Leader motivating language positively predicts follower cultural knowledge.	Supported
H₃: Motivating language positively predicts a follower’s cultural fit.	Supported
H₄: Cultural knowledge positively predicts cultural fit.	Supported
H₅: Motivating language positively moderates the relationship between cultural knowledge and cultural fit.	Not Supported
H₆: Cultural knowledge positively predicts organizational identification.	Not Supported
H₇: Cultural fit moderates the relationship between organizational understanding and organizational identification.	Not Supported
H₈: Cultural fit will positively predict organizational identification.	Supported
RQ₁: Do cultural understanding and cultural fit fully or partially mediate the relationship between motivating language and organizational identification?	Partially Mediate

Discussion and Conclusion

This study contributes new evidence that leader communication (as conceptualized through motivating language) has a positive and significant association with follower organizational identification. In achieving this goal our investigation responds to research recommendations from scholars in the fields of management and communication (Barge & Schlueter, 1988; Cheney, 1982; Henderson et al., 2015; Madlock, 2008; Men, 2014; Scott et al., 1998). Moreover, we can explicitly state our findings as follows: for every one standard deviation increase in a leader’s motivating language use, we expect to see nearly one-half of a standard deviation rise in the follower’s organizational identification.

This study also contributes to current research by identifying a major portion of the mechanisms through which this process occurs. Leader motivating language builds follower cultural knowledge which, in turn, cultivates that person’s perceived cultural fit. The resultant sense of fit enhances organizational identification. Keeping the attendant benefits of organizational identification in mind—such as higher performance, job satisfaction, attendance and others, these discoveries have pivotal implications for organizational and internal stakeholder well-being. In fact, this model explains over 53% of the variance in a follower’s organizational identification.

This study adds more value to the field of leadership communication since it extends our grasp of motivating language theory. MLT scholars have called for exploring new outcomes, and especially for fresh insights into how motivating language operates (Holmes & Parker, 2017; Mayfield & Mayfield, 2019; Sabir & Bhutta, 2018; Sullivan, 1988). The model’s tests also show generalizability between the USA and India, and seem largely resistant to omitted variable and sample misspecification problems. These findings similarly address Madlock’s and Hildebrand Clubbs’ (in press) suggestions for more detailed investigations of ML in cross-cultural settings.

However, the full-proposed model did not find support from the analysis since the predicted moderating relationship did not receive validation. Likewise, the mediating relationships only partially explained the connection between motivating language and organizational identification. Considering these results—and despite this study’s substantive progress—future investigations should explore these links more. These new initiatives would benefit from probing into uncharted influences on the ties between leader communication and organizational identification, namely virtual and contingent workers and stage of organizational membership. Regrettably, our study was confined by its scope and unable to capture these potential moderators.

Virtual (including telecommuters) and contingent (contractual/short term) workers now comprise over thirty percent of the workforce in the USA (Dokko et al., 2015), yet these relationships remain relatively unstudied in terms of organizational identification. Madlock (2013), Wang and colleagues (2009), and Fan and co-investigators (2014) all advanced this quest by producing findings that attest to motivating language’s effectiveness in virtual settings. Along similar lines, Cardon and co-authors (2019) advocated for more exploration of leader communications situated in virtual platforms. Future investigations could build on this progress/appeal and evaluate appropriate models that measure ML’s intersection with organizational identification and culture in such contexts.

A worker’s organizational membership stage presents another possible critical moderator that merits investigation. New organizational members (notably during their first 6 months of belonging) and internal stakeholders involved with a major change may engage in more intensified sense-making during their entry, assimilation, and transition stages in an organization (Jablin, 2001; Jablin & Krone, 1994; Weick, 1995). Again, innovative models could find out how ML interacts with organizational identity under these conditions. Perhaps the potential impact of leader communication becomes stronger as identification levels form in an unfamiliar organizational culture or when followers adjust to shifts in the status quo.

This study has limitations which can encourage future researchers toward new discoveries. Due to this project’s cross-sectional nature, the direction of the links (causality) remain untested (Pearl, 2009) and new investigations should address this issue. Also, inherent limitations exist in the knowledge we can gain through survey-based research. Future explorations can respond to this potential constraint by adopting multiple, mixed method research tools including different response sources and complementary qualitative techniques. The latter form of research methodology also should further enrich our understanding of how leadership communication, organizational identification and culture interface. Lastly, a cross-national comparison does not establish generalizability in other national settings. Echoing Madlock and Hildebrand Clubbs (in press), we recommend more tests of motivating language and organizational identification in cross-national frameworks (M. Mayfield & Mayfield, 2014, 2018b).

This study makes significant contributions to practice. Our research emphasizes the power of leader communication for creating strong organizational alliances with followers. Leaders need awareness of how their communication drives a follower’s identification and target their speech in a strategic fashion (J. Mayfield & Mayfield, 2017; Mayfield & Mayfield, 2012a). This self-recognition combined with reflection becomes crucial for leaders at the highest levels since their role model influence to elicit identification creates a snowball effect throughout the organization (Mayfield & Mayfield, 2019). Top leaders can also set training agendas and incentives which develop and reward leader motivating language skills to enhance this diffusion effect (Mayfield & Mayfield, 1995; Mayfield & Mayfield, in press). We conceptualize ML as a journey, not as a destination, one in which abilities can be learned and encouraged.

In conclusion, identification is a critical stage in the path to optimize organizational and internal stakeholder well-being. Moreover, organizational identification can be nurtured through leader communication (via motivating language) that enhances a follower’s perceived knowledge of and inclusion with their culture.

Footnotes

Appendix 1

Appendix 2

Appendix 3

Appendix 4 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

This research was partially supported by a grant from the A.R. Sanchez Jr. School of Business, Texas A&M International University.

Author Biographies

Milton Mayfield is a Professor of Management in the A. R. Sanchez Jr. School of Business at Texas A&M International University. Milton has published over 100 journal articles, conference publications, and book and encyclopedia chapters. These manuscripts have been published in such outlets as the Creativity Research Journal, Human Resource Management, International Journal of Business Communication, The Encyclopedia of Creativity (2^nd ed.), and Development and Learning in Organizations. He also recently co-authored the books Motivating Language: Effective Leader Talk in the Workplace and Fundamental Theories of Business Communication. He specializes in the areas of leadership communication, international management research, and creativity and innovation, and has done consulting and training work with such organizations as the US State Department, Sony Mexico, Texas Department of Transportation, Virginia Tech University, and the Callahan Ranch. He is Co-Editor of the International Journal of Business Communication and MacMillan’s New Perspectives in Organizational Communication book series.

Jacqueline Mayfield is a Professor of Management in the A. R. Sanchez Jr. School of Business at Texas A&M International University and is the Radcliffe Killam Distinguished Professor of Management. Jacqueline has published over 100 journal articles, conference publications, and book chapters. These manuscripts have been published in such outlets as the International Journal of Business Communication, Creativity Research Journal, Human Resource Management, and Development and Learning in Organizations. She also recently co-authored the books Motivating Language: Effective Leader Talk in the Workplace and Fundamental Theories of Business Communication. She specializes in the areas of leadership communication, international management research, and human resource management, and has done consulting and training work with such organizations as the US State Department, Sony Mexico, Texas Department of Transportation, Virginia Tech University, and the Callahan Ranch. She is Co-Editor of the International Journal of Business Communication and MacMillan’s New Perspectives in Organizational Communication book series.

Robyn Walker is an Associate Professor of Clinical Management Communication in the Marshall School of Business at the University of Southern California. She does research in the areas of communication and team decision making, and her publications have appeared in American Communication Journal, Business Communication Quarterly, International Journal of Knowledge, International Journal of Business Communication, International Journal of Diversity in Organizations, Communities and Nations, and Culture and Change Management. She has also authored or edited a dozen books. She currently serves as Associate Editor for the International Journal of Business Communication.

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