This study aims to examine how employer branding (EB) orientation shapes employee and organizational performance through human resource management (HRM) practices and ideal employer branding deviation (iEBd). Specifically, EB orientation reflects the strategic emphasis an organization places on employer branding, whereas iEBd captures employees’ perceived discrepancy between their ideal and actual work experiences.
Drawing on signaling theory and expectation–disconfirmation theory (EDT), this study tests a multilevel model linking EB orientation to performance outcomes through HRM practices and iEBd. Survey data were collected from 524 employees and senior managers across 55 Greek SMEs and analyzed using multilevel structural equation modeling (MSEM).
HRM practices fully mediate the relationship between EB orientation and both organizational and employee performance. In addition, iEBd partially mediates the relationship between HRM practices and employee performance, highlighting the importance of alignment between employees’ ideal employment standards and their actual work experience in shaping performance.
This study introduces and empirically tests the iEBd construct, positioning it as a key employee-level evaluative mechanism linking HRM practices to employee performance. By integrating signaling theory with expectation–disconfirmation logic, the study offers a novel perspective on how employees interpret and evaluate employer branding within organizations.
1. Introduction
In response to the growing global talent shortage, organizations increasingly adopt strategic approaches to attract and retain talent (Oseghale et al., 2018), ultimately aiming to enhance performance (Tlaiss et al., 2017). Among these strategies, building a distinctive employer reputation has become critical for securing competitive advantage. Positioned at the intersection of HRM and marketing, employer branding is widely recognized as a strategic tool for differentiating organizations in the talent market (Lievens, 2007).
However, despite growing scholarly attention to employer branding, important theoretical gaps remain. A substantial body of research emphasizes external employer branding perceptions, particularly among job seekers (e.g. Lievens and Highhouse, 2003; Baum and Kabst, 2013) and focuses on large organizational contexts (e.g. Stor and Haromszeki, 2025). Far less attention has been devoted to how employer branding is interpreted internally by current employees, and how these internal perceptions relate to performance, especially within small and medium-sized enterprises (SMEs) (Tumasjan et al., 2020; Harney and Alkhalaf, 2020). This limits theoretical understanding of how employer branding operates as an internal organizational process and how employees' interpretations shape performance outcomes. Moreover, studies linking employer branding to performance have typically relied on single-level analyses (e.g. Tumasjan et al., 2020; Stor and Haromszeki, 2025), limiting understanding of the cross-level mechanisms through which strategic branding initiatives influence employee and organizational outcomes.
Employer branding centers on developing and communicating a distinctive employer value proposition (EVP), encompassing the functional, economic, and psychological benefits offered to employees (Backhaus and Tikoo, 2004). It concerns how organizations position and enact themselves as employers. Although closely related, employer branding differs from internal branding in discipline and focus (Saleem and Iglesias, 2016). Whereas employer branding focuses on developing and communicating the EVP to attract prospective and retain current employees (Backhaus and Tikoo, 2004), internal branding focuses on aligning current employees with the corporate brand and its values through internal processes such as brand leadership and communication (Saleem and Iglesias, 2016).
This study adopts this employment-centered perspective, focusing on how employer branding (EB) orientation is embedded within HRM practices and how employees evaluate their work experiences in light of that orientation. EB orientation is an organizational-level strategic orientation reflecting the extent to which employer branding is prioritized as an overarching HR guiding principle, evident in leadership emphasis and organizational practices (Tumasjan et al., 2020). Strategic prioritization alone, however, cannot influence performance unless translated into coherent, EB-aligned HRM practices. Accordingly, HRM practices are conceptualized as the mechanism through which the EVP is enacted and communicated (Alves et al., 2020), enhancing clarity and consistency of the employer offering (Collins and Martinez-Moreno, 2022).
While prior research has largely examined employer branding as a signaling process, less attention has been paid to how employees evaluate these signals relative to their own standards. Accordingly, this study focuses on two constructs, EB orientation and ideal employer branding deviation (iEBd), to explain how organizational intent is reflected in employee responses. Rather than treating employer branding as a broad conceptual domain, the study positions EB orientation as the strategic driver and iEBd as the key evaluative mechanism linking employee experience to performance.
Drawing on signaling theory (Spence, 1973), we argue that employer branding serves as a signal directed at potential and current employees, shaping how they interpret the organization’s employment offerings and influencing performance-related outcomes. HRM practices serve as the mechanism through which employer brand signals are conveyed and interpreted. Employees interpret these signals in light of their personally valued standards of an ideal employment experience. While signaling theory explains how these practices convey employer brand-related information, it does not specify how employees evaluate these signals. Expectation–disconfirmation theory (EDT) (Oliver, 1980) complements this perspective by explaining how employees assess their work experiences relative to those standards. In this study, this comparison is captured through iEBd, reflecting the alignment between employees’ ideal and actual work experiences.
EB orientation, as a strategic priority, is necessary for the development and effective implementation of HRM practices. This is particularly salient in SMEs, where limited economies of scale make HRM systems a costly, long-term investment (Harney and Alkhalaf, 2020), and where leadership commitment becomes essential for the development and sustainability of HRM practices. When strategically prioritized, HRM systems are more likely to convey stronger signals of an ideal employment offering. Consequently, employees’ work experiences are more likely to align with their ideal employment standards, reducing evaluative deviations and strengthening performance. These characteristics make SMEs a meaningful context for examining employer branding because strategic priorities are more directly reflected in employees' work experiences. Compared with larger organizations, SMEs typically exhibit lower formalization and closer leadership–employee proximity (Harney and Alkhalaf, 2020), particularly in micro and small firms, where HRM is often weakly institutionalized and owner-managed. In these contexts, strategic employer branding priorities are more readily reflected in enacted HRM practices than in larger enterprises, where HRM systems are more sophisticated and standardized. Consequently, they may convey weaker and more generic signals, making it more difficult for employees to assess how well the organization's employer brand aligns with their own ideals. Thus, SMEs are not merely an underexplored context, but one in which branding–HRM coherence may be particularly consequential for performance.
To fully capture this process, a multilevel perspective is theoretically necessary because the focal constructs operate across different levels of analysis, requiring explicit theorization of the cross-level mechanisms linking them (Renkema et al., 2017). EB orientation and HRM practices operate at the firm level, whereas iEBd reflects individual-level evaluations, with performance manifesting at both employee and organizational levels. Examining these relationships within a single-level framework would obscure the cross-level mechanisms linking strategic orientation to behavioral outcomes.
The study makes three primary contributions. First, it tests a multilevel EB–HRM–performance model, showing that EB orientation relates to both employee and organizational performance through HRM practices. Second, it introduces and operationalizes the iEBd construct – the discrepancy between employees’ ideal and actual work experiences – and demonstrates its explanatory power for employee performance. Third, by integrating signaling and expectation–disconfirmation perspectives, the study clarifies how employer branding operates internally. EB orientation is enacted through HRM practices, which employees interpret against their own valued employment ideals, shaping employee performance, while its association with organizational performance likely reflects broader, firm-level alignment between HRM practices and strategic priorities. Examining these relationships within SMEs highlights contexts where alignment between strategic intent and actual work experiences is especially critical.
2. Theoretical background and hypotheses
2.1 Employer branding
Over the past two decades, a growing body of research at the intersection of HRM and marketing, has emphasized the strategic integration of branding principles into HRM, commonly referred to as employer brand management (Russell and Brannan, 2016). It highlights the influence of employer image, brands, and reputation on HRM processes and outcomes (Theurer et al., 2016). Ambler and Barrow (1996) defined employer branding as the application of brand marketing principles to HRM to improve talent attraction, recruitment efficiency, and employee engagement, ultimately improving performance. Employer branding has since been recognized as a long-term strategy for sustaining talent flows and enhancing organizational performance (e.g. Srivastava and Thomas, 2010; Moroko and Uncles, 2008).
Building on this foundation, employer branding has been widely conceptualized as a mechanism through which organizations signal their values and employment offerings to current and potential employees (Lievens and Highhouse, 2003). Both communicated employer attributes and organizational practices shape how individuals evaluate an organization as a place to work (e.g. Theurer et al., 2021). However, inconsistencies between communicated employer attributes and employees’ lived experiences may weaken employer attractiveness (Wang et al., 2022). Together, these findings position employer branding as a signaling mechanism, highlighting the importance of alignment between organizational intentions and their enactment.
Employer branding has been associated with organizational attractiveness and employee-related outcomes, including engagement, retention and loyalty (Love and Singh, 2011; Chopra et al., 2024). A key mechanism underlying these outcomes is the employer value proposition (EVP), which reflects the unique employment experience organizations seek to offer (Backhaus and Tikoo, 2004).Within this framework, EB orientation is conceptualized as a strategic driver that informs the design and enactment of HRM practices, shaping employees’ actual work experiences.
2.2 The role of HRM
The impact of HRM on organizational outcomes has long been central to management research (e.g. Delaney and Huselid, 1996). Strategic HRM literature consistently identifies staffing, training, performance appraisal, and compensation as core HRM practice domains associated with organizational performance across industries (Combs et al., 2006; Harney and Alkhalaf, 2020). The present study examines these HRM practices. Together, these practices form HRM systems that develop human capital and enhance organizational success (Paauwe and Boselie, 2005).
Building on this strategic HRM perspective, recent employer branding research conceptualizes HRM practices as the primary mechanism through which employer brand signals are enacted, ultimately influencing employee and organizational performance (Guest et al., 2021). Following prior work, we distinguish between employer brand, employer reputation, and employer brand signals. The employer brand refers to the attributes and values an organization promotes to position itself as an attractive employer (Lievens and Highhouse, 2003). Employer reputation reflects how stakeholders perceive the organization based on accumulated experiences and signals. Employer brand signals are cues, such as HRM practices, leadership style, or organizational messaging, through which these attributes are conveyed (Theurer et al., 2016; Guest et al., 2021). We focus on employer brand signals as interpreted by current employees and linked to performance outcomes. Scholars increasingly argue that HRM systems should align with employer branding strategy to amplify organizational impact. For example, Tumasjan et al. (2020) link employer branding to organizational performance through recruitment efficiency and a positive workplace climate, highlighting its role in signaling an employee-oriented culture.
The role of HRM practices in transmitting employer brand signals can be understood through the lens of signaling theory (Bergh et al., 2010). The theory has since been widely applied in management research (Connelly et al., 2011), with Spence (1973) originally introducing it in the context of labor markets. More recently, researchers have examined how HRM practices convey organizational values and the EVP. Guest et al. (2021) argue that organizations act as signal senders, employees as receivers, and HRM practices as the signals shaping employees’ attitudes and perceptions. For instance, training and competitive pay signal investments in employee development, strengthening the employer brand (Zografou and Galanaki, 2024).
Within this framework, HRM practices constitute the mechanism through which EB orientation becomes operational. When employer branding is strategically prioritized, it is reflected in how firms design and implement HRM, shaping employees’ experiences, and influencing performance. HRM thus acts as a bridge, translating abstract brand ideals into tangible practices that drive behavioral outcomes.
More specifically, HRM practices are conceptualized as brand-centered mechanisms rather than generic administrative tools. Recruitment, training, performance appraisal, and compensation signal and embed the EVP into daily work experiences. EB orientation sets the strategic direction for HRM implementation, strengthening employer brand signals. Consequently, employees are more likely to perceive that their experiences reflect their ideal employment conditions. As established in the management literature, HRM systems enhance employee performance in two ways: by building workforce capability (e.g. through recruitment and selection practices that secure employees with strong performance potential) and by enhancing employees’ motivation to perform effectively in their roles (e.g. through reward systems, perceptions of fairness, and other provisions that foster fulfilment in the lived employer brand).
A large body of literature has established a positive relationship between HRM practices and employee performance. Meta-analytic evidence shows that HRM systems are consistently associated with individual-level outcomes, including task performance and organizational citizenship behavior (Jiang et al., 2012; Combs et al., 2006). At the employee level, HRM practices influence performance through employees’ experiences and perceptions (Nishii et al., 2008; Kehoe and Wright, 2013).
Thus, we may hypothesize that:
HRM practices positively mediate the relationship between EB orientation and employee performance.
Firms with strong EB orientation enact this strategic emphasis through coherent HRM practices, thereby enhancing organizational performance. Although HRM practices primarily target internal audiences, their alignment with the EVP can strengthen broader organizational outcomes. Prior research suggests that a strong employer brand is not only associated with enhanced organizational attractiveness but also with positive market-related outcomes. For instance, employer branding has been associated with customer responses (e.g. Chiang et al., 2018), and investor evaluations (e.g. Mariappanadar and Kairouz, 2017), suggesting that its effects extend beyond employees to the wider market environment.
Moreover, research has long emphasized that leadership commitment to investing in HRM is critical for organizational performance, as the effectiveness of HRM systems depends on their strategic prioritization and implementation (Huselid, 1995; Wright et al., 2005). Organizations that strategically prioritize HRM are more likely to develop coherent and well-resourced HRM systems aligned with organizational goals. In turn, considerable research demonstrates that such HRM systems are positively associated with organizational performance. Previous meta-analyses show that high-performance work practices are consistently linked to firm-level outcomes, including productivity and financial performance (Combs et al., 2006; Jiang et al., 2012; Subramony, 2009). Taken together, this literature provides strong support for the role of HRM as a key mechanism linking strategic orientations to organizational performance.
Therefore, we propose that:
HRM practices positively mediate the relationship between EB orientation and organizational performance.
2.3 Ideal employer branding deviation
Organizations pursue employer branding by articulating a clear EVP for current and prospective employees (Srivastava and Bhatnagar, 2010). This EVP is enacted through HRM practices, which serve as employer brand signals. However, employees are not passive recipients; they actively interpret these signals (Spence, 1973). Highhouse et al. (2007) distinguish between explicit (instrumental) and implicit (symbolic) signals, with the latter shaping perceptions and inferences. Signal effectiveness also depends on the receiver’s attention, motivation, and relevance (Kooij et al., 2013; Guest et al., 2021). For instance, family-supportive HRM practices, such as parenting or caregiving support, may be perceived as less relevant to employees without such responsibilities.
While HRM provides the structural and symbolic context for employer branding, employees ultimately interpret and evaluate such signals. Their evaluations are guided not only by perceptual filters but also by personal ideals regarding desirable work experiences. Expectation–disconfirmation theory (EDT) (Oliver, 1980) complements signaling theory as it conceptualizes evaluation as a comparison between a reference standard and actual experience. Whereas signaling theory explains how employer brand signals are conveyed, EDT clarifies how employees evaluate them and how these assessments influence behavioral responses.
EDT posits that individuals evaluate experiences by comparing them against a reference standard, resulting in confirmation or disconfirmation (Zeithaml et al., 1993; Oliver, 1980), which shapes subsequent attitudes and behaviors. While prior applications of the theory typically conceptualize this standard in terms of expectations formed through external signals, the present study adopts a normative perspective, viewing the standard as employees’ internally held ideals of the employer. Employees therefore compare their actual employer experiences with these ideals, generating a discrepancy that reflects the degree of alignment between what they value and what they experience. This discrepancy, in turn, influences employee responses, including performance. This evaluative mechanism operates at the individual level, linking the interpretation of HRM-based signals to subsequent behavioral outcomes.
To capture this dynamic, we introduce ideal employer branding deviation (iEBd), defined as the gap between employees’ ideal and actual work experiences. Consistent with EDT, this discrepancy reflects the outcome of the disconfirmation process. Although conceptually related to several constructs in organizational behavior research, the iEBd construct captures a distinct evaluative mechanism. Unlike value internalization (Ryan and Deci, 2000), which concerns the process through which individuals adopt and integrate endorsed values into self-concepts, iEBd captures the divergence between employees’ personally valued employment ideals and their lived work experiences. Similarly, while person–organization fit (Cable and Judge, 1996) focuses on the compatibility between individual and organizational values, iEBd emphasizes the perceived gap between ideal and actual employment experiences, regardless of value congruence. Finally, unlike psychological contract breach (Lester et al., 2002) which concerns perceived violations of employer promises, iEBd reflects a broader evaluative comparison between personal employment ideals and experienced reality.
A high iEBd indicates substantial misalignment between ideal and actual work experiences. Employer branding consistently enacted through HRM practices reduces this discrepancy by aligning employees' experiences with their personal employment ideals. Conversely, when work experiences fall short of these standards, iEBd increases, reflecting a form of psychological misalignment that may reduce motivation and engagement, and ultimately performance.
Building on this EDT-grounded conceptualization, HRM practices serve a dual function: communicating employer brand signals and reducing iEBd. Accordingly, we propose that iEBd mediates the relationship between HRM practices and employee performance. Stronger HRM support for employer branding should lead to lower iEBd, which in turn enhances performance. Thus, we suggest that:
iEBd mediates the relationship between HRM and employee performance.
Figure 1 illustrates the conceptual framework.
A conceptual framework illustrating hypothesized relationships among EB orientation, HRM practices, ideal EB deviation, employee performance, and organizational performance across two levels of analysis. Ideal EB deviation and employee performance are positioned at the individual level, while EB orientation, HRM practices, and organizational performance are positioned at the firm level. For the individual-level outcome, HRM practices mediate the relationship between EB orientation and employee performance. Ideal EB deviation further mediates the relationship between HRM practices and employee performance, with both constituent paths expected to be negative. For the firm-level outcome, HRM practices mediate the relationship between EB orientation and organizational performance. Arrow direction indicates the hypothesized direction of the relationships, and arrow color distinguishes the three pathways: H1a in red for the mediation of the EB orientation–employee performance relationship by HRM practices, H1b in purple for the mediation of the EB orientation–organizational performance relationship by HRM practices, and H2 in green for the mediation of the HRM practices–employee performance relationship by ideal EB deviation.Conceptual framework. Source: Authors’ own work
A conceptual framework illustrating hypothesized relationships among EB orientation, HRM practices, ideal EB deviation, employee performance, and organizational performance across two levels of analysis. Ideal EB deviation and employee performance are positioned at the individual level, while EB orientation, HRM practices, and organizational performance are positioned at the firm level. For the individual-level outcome, HRM practices mediate the relationship between EB orientation and employee performance. Ideal EB deviation further mediates the relationship between HRM practices and employee performance, with both constituent paths expected to be negative. For the firm-level outcome, HRM practices mediate the relationship between EB orientation and organizational performance. Arrow direction indicates the hypothesized direction of the relationships, and arrow color distinguishes the three pathways: H1a in red for the mediation of the EB orientation–employee performance relationship by HRM practices, H1b in purple for the mediation of the EB orientation–organizational performance relationship by HRM practices, and H2 in green for the mediation of the HRM practices–employee performance relationship by ideal EB deviation.Conceptual framework. Source: Authors’ own work
3. Methodology
3.1 Participants and procedure
The study focuses on SMEs in the Greek private sector. To collect multilevel data, two distinct questionnaires were developed. The first targeted CEOs, founders, or top managers or, when unavailable, HR managers or senior executives (overall response rate: 76.3%). It assessed EB orientation, HRM practices, and organizational performance. The second, distributed to employees, assessed ideal and actual employer brand attributes and individual performance.
Sampling was based on the Greek Ministry of Development and Investments’ registry of EU-funded SMEs and the ICAP Data PRISMA [1] 2021 database (ICAP CRIF, 2021). Additional firms were recruited through LinkedIn. Of 785 invited firms, 290 agreed to participate, and 55 (19%) completed both questionnaires. The final dataset comprised 524 employee responses from 55 SMEs.
Eligible firms met specific criteria: private ownership across diverse sectors, at least three years of operation, and classification as SMEs under EU definitions (micro: <10 employees, small: 10–49, medium: 50–249). Micro and small enterprises together accounted for 83.7% of the sample (see Table 2), reflecting Greece’s SME–dominant economy, where over three-quarters of the workforce is employed in SMEs, well above the EU average (OECD, 2023). This context provides an appropriate setting for examining employer branding in resource-constrained environments, where alignment between intended practices and employees’ lived experiences is particularly important.
Data collection occurred between May 2021 and January 2022 using Qualtrics. CEOs/managers were invited to complete a personalized online survey via a link sent by email. Upon completing the first survey, consenting managers received a second survey link to distribute to employees. Data collection was completed once both manager and employee responses had been received for each participating firm. Responses from managers and employees were matched using a unique identifier, enabling multilevel analysis. Employee-level constructs (iEBd and employee performance) were modeled at the individual level (Level 1). Employees reported their ideal and perceived employer brand attributes, from which iEBd was computed, as well as their self-rated performance. Organizational-level constructs (EB orientation, HRM practices, and organizational performance) were reported by managers and modeled at the firm level (Level 2). This separation ensured conceptual alignment between constructs and levels of analysis within the multilevel structural equation modeling framework. To maintain confidentiality, managers could not access employee responses, and vice versa. Upon completion, participants received anonymized benchmark reports comparing their results with the sample average.
All statistical analyses were conducted using STATA 14. Sample sizes at both levels met recommended guidelines (Maas and Hox, 2005). A minimum employee response rate of 10% per firm was applied to ensure valid second-level representation [2].
Tables 1 and 2 present the sample’s demographic and organizational characteristics. Table 1 summarizes CEOs/manager and employee demographics, whereas Table 2 presents firm size, ownership, sector, and market reach.
CEOs/managers and employees’ demographics
| CEOs/managers | Employees | |||
|---|---|---|---|---|
| Mean | S.D. | Mean | S.D. | |
| Age | 44.33 | 10.32 | 37.99 | 8.73 |
| Job hierarchy | 0.86 | 0.17 | 0.52 | 0.19 |
| Frequency | Percentage % | Frequency | Percentage % | |
|---|---|---|---|---|
| Gender | ||||
| Male | 34 | 61.80% | 264 | 50.40% |
| Education | ||||
| High school | 5 | 9.10% | 93 | 17.70% |
| Certificate of vocational training | 4 | 7.30% | 68 | 13.00% |
| University degree | 20 | 36.40% | 201 | 38.40% |
| Postgraduate | 22 | 40.00% | 151 | 28.80% |
| Doctoral degree | 4 | 7.30% | 11 | 2.10% |
Firms’ characteristics
| 55 firms | ||
|---|---|---|
| Frequency | Percentage % | |
| Size (number of employees)a | ||
| Micro (<10) | 14 | 25.50% |
| Small (<50) | 32 | 58.20% |
| Medium (<250) | 9 | 16.40% |
| Family ownership | ||
| Family firms | 33 | 60.00% |
| Sector | ||
| Primary | 2 | 3.60% |
| Secondary | 11 | 20.00% |
| Trade and Services | 42 | 76.40% |
| Serving market | ||
| Local | 9 | 16.40% |
| Regional | 2 | 3.60% |
| National | 17 | 30.90% |
| European | 5 | 9.10% |
| World-wide | 22 | 40.00% |
aBased on European Commission (2021) classification
3.2 Measures
3.2.1 Employee performance
Employee performance was measured using the 4-item job-holder role subscale of the Role-Based Performance Scale (Welbourne et al., 1998). Sample items include “quantity of work output”, “quality of work output”, and “accuracy of work”. Participants evaluated performance levels on a 5-point Likert scale (1 = needs much improvement; 5 = excellent). We focused specifically on the job-holder dimension given the study’s focus on performance related to employees’ formal job responsibilities. This dimension captures core task performance, aligning directly with the theoretical model linking EB orientation and HRM practices to individual performance. The scale demonstrated strong reliability (Cronbach’s α = 0.81; mean = 3.84), with standardized loadings exceeding 0.60 (Hair et al., 2006). Although self-reported performance measures may raise concerns regarding inflation bias (Brutus et al., 2013; Desai, 2012), several features of the research design mitigate this risk. First, the study employed a multilevel design in which key predictors (EB orientation and HRM practices) and organizational performance were reported by managers, thereby reducing single-source bias. Second, the performance items referred to specific and concrete job-related behaviors. Finally, anonymity procedures reduced social desirability effects. Although incorporating supervisor-rated or objective individual performance indicators would provide additional robustness, employee performance was conceptualized as an individual-level role-based construct and measured using a validated self-report scale. This limitation is discussed further in the Limitations and Future Research section.
3.2.2 Organizational performance
Organizational performance was assessed using measures drawn from the CRANET European HRM Practices Survey (CRANET Research Network, 2023). These measures were included in the questionnaire administered to managers in the participating SMEs, following the framework of Gooderham et al. (2008). Managers rated gross revenue over the past three years on a 5-point scale (1 = large losses to 5 = well in excess of costs). To validate this subjective measure, a supplementary item asked respondents to select a financial turnover range. Due to data protection constraints (GDPR), objective turnover data was available for only 47 firms. Results remained consistent across both subjective and objective measures, except for the non-significance of ownership on financial performance (p = 0.82).
3.2.3 Ideal employer branding deviation
As iEBd reflects the discrepancy between employees’ ideal and actual work experiences, it was operationalized as the difference between corresponding ideal and actual scores for each parallel item. Consistent with discrepancy-based approaches that operationalize misalignment through difference scores (Kassinis et al., 2022), we subtracted actual perceptions from ideal ratings, with higher scores reflecting greater ideal-actual deviations. Employees’ ideal work attributes were captured using the 25-item employer attractiveness scale by Berthon et al. (2005), with responses ranging from 1 (not important) to 5 (very important). Actual experience was assessed using the same 25 items, reframed to evaluate employees' perceptions of their current employer (1 = does not characterize my organization; 5 = fully characterizes it). As expected, ideal scores (M = 4.34) exceeded actual ones (3.76). iEBd scores were calculated as the item-by-item difference between ideal and actual perceptions (e.g. “importance of a fun work environment” vs “experience of a fun work environment”).
Given that iEBd is a newly conceptualized construct, an exploratory factor analysis (EFA) was conducted using SPSS 25. Principal component analysis with varimax rotation and factor extraction using the MINEIGEN criterion were employed. Items with factor loadings above 0.40 were retained. Of the original 25 items of Berthon et al. (2005), six were removed due to cross-loadings, resulting in a final set of 19 items distributed across the four dimensions. iEBd was measured as a second-order factor with a total Cronbach’s alpha of 0.79. The analysis identified four dimensions: “professional development”, “deployment opportunity”, “relationships”, and “products and services” (see Table 3).
iEBd
| iEBd - 4 new factors | |||||
|---|---|---|---|---|---|
| Construct | Cronbach's alpha | Variable | Mean | S.D. | Standardized Loadings |
| Factor 1: Professional Development | 0.90 | Recognition/appreciation from management | 0.78 | 0.90 | 0.65 |
| A fun working environment | 0.64 | 1.03 | 0.60 | ||
| Α springboard for future employment | 0.57 | 1.01 | 0.59 | ||
| Feeling good about yourself as a result of working for a particular organization | 0.78 | 0.92 | 0.68 | ||
| Feeling more self-confident as a result of working for your organization | 0.71 | 0.96 | 0.69 | ||
| Gaining career-enhancing opportunities | 0.90 | 1.09 | 0.72 | ||
| Supportive and encouraging colleagues | 0.64 | 1.04 | 0.70 | ||
| Working in an exciting environment | 0.71 | 1.07 | 0.68 | ||
| The organization both values and makes use of people's creativity | 0.89 | 1.08 | 0.78 | ||
| Good promotion opportunities within the organization | 0.97 | 1.19 | 0.75 | ||
| An above-average basic salary | 0.71 | 0.96 | 0.51 | ||
| Professional Development | 0.76 | 0.73 | |||
| Factor 2: Deployment Opportunity | 0.73 | Opportunity to apply what was learned at a tertiary education | 0.46 | 0.93 | 0.66 |
| Opportinity to teach others what you have learned | 0.28 | 0.94 | 0.54 | ||
| Job security within the organization | 0.41 | 0.85 | 0.58 | ||
| Hands-on interdepartmental experience | 0.35 | 0.93 | 0.64 | ||
| Deployment opportunity | 0.37 | 0.68 | |||
| Factor 3: Relationships | 0.74 | Having a good relationship with your superiors | 0.38 | 0.84 | 0.84 |
| Having a good relationship with your colleagues | 0.35 | 0.85 | 0.71 | ||
| Relationships | 0.36 | 0.75 | |||
| Factor 4: Products and Services | 0.79 | The organization produces high-quality products and services | 0.34 | 0.92 | 0.78 |
| The organization produces innovative products and services | 0.44 | 1.02 | 0.84 | ||
| Products and Services | 0.39 | 0.89 | |||
| Second-Order Factor | 0.79 | iEBd | 0.60 | 0.62 | |
A confirmatory factor analysis (CFA), conducted via structural equation modeling (SEM) in STATA 14, validated the factor structure. Standardized loadings greater than 0.50 were retained (Hair et al., 2006). The four dimensions of iEBd map onto the distinction by Lievens and Highhouse (2003) between instrumental (factors 1, 2) and symbolic (factors 3, 4) employer brand attributes.
3.2.4 HRM practices
HRM practices were conceptualized as a formative organizational construct, consistent with the viability of formative measures (Chang et al., 2016). This aligns with the use of aggregated multi-item constructs (Chang et al., 2016; Spectrum, 1992), whereby selected items are summed to generate an overall construct score (Spectrum, 1992).
HRM practices were operationalized using measures drawn from the CRANET European HRM Practices Survey (CRANET Research Network, 2023). Consistent with the strategic HRM literature, the selected recruitment, training, appraisal, and compensation practices represent the core practice domains examined in HRM research (Combs et al., 2006; Harney and Alkhalaf, 2020) and serve as mechanisms through which the EVP is enacted and employer brand signals are communicated to employees (Guest et al., 2021; Tumasjan et al., 2020). In line with the paper’s employer branding focus, these practices are treated as EVP-enacting, communicating what the organization offers and values (e.g. career development, fairness, recognition), thereby embedding employer brand signals in employees’ daily experiences. To enhance statistical power and facilitate item-level analysis, all indicator scores were standardized (Chang et al., 2016; Aguirre-Urreta and Marakas, 2012). Table 4 outlines the four HRM practices and their operationalization. A composite HRM index was created by summing the standardized scores of all practices.
Structure and measurement of HR
| HRM Practices | Number of constructs | Question | Number of items | Example of items | Measurement scale | Manipulation |
|---|---|---|---|---|---|---|
| I.Recruitment | 1 | “Please indicate the frequency of the use of the following recruitment methods in your organization” | 12 | “word of mouth”, “social media” | “yes”: 1/“no”: 0 | standardization of each item, sum of items and standardization |
| II.Job Descriptions | 1 | “Are there job descriptions in your organization?” | 1 | “yes, for all jobs”: 3, “yes, for the majority of jobs”: 2, “yes, for the minority of jobs”: 1, “no”: 0 | standardization | |
| III.Selection | 1 | “Please indicate the frequency of use of the following selection methods in your organization” | 6 | “interviews”, “social media profiles” | 1–5 Likert scale, 1: “never”, 5: “always” | standardization of each item, sum of items and standardization |
| Staffing total | 3 | sum of I, II and III | ||||
| IV.Training intensity | 1 | “When does employee training take place?” | 4 | “upon hire”, “upon promotion” | “yes”: 1/“no”: 0 | standardization of each item, sum of items and standardization |
| V.Training participation | 1 | “How many employees participated in training programs during the previous year?” | Continuous variable (division of the number of trained employees by the total number of employees in each firm) | standardization | ||
| VI.Training frequency | 1 | “How often do you estimate the need for training of employees in your organization” | 5 | 1–5 Likert scale, 1: “never”, 5: “always” | standardization of each item, sum of items and standardization | |
| Training total | 3 | 9 | sum of IV, V and VI | |||
| VII.Stable Compensation | 1 | “Salaries and wages are defined…” | 4 | “Exactly at the minimum wage”, “somewhat above the minimum, wage” | 1–4 Likert scale, 1: “exactly at the minimum wage”, 4: “more than (1.5 * the minimum wage)” | standardization of each item, sum of items and standardization |
| VIII.Performance- based Compensation | 1 | “Do you offer any of the following” | 3 | “Bonus based on individual goals/performance”, “bonus based on team goals/performance” | “yes”: 1/“no”: 0 | standardization of each item, sum of items and standardization |
| Compensation total | 2 | 7 | sum of VII and VIII | |||
| IX.Appraisal | 1 | “Is the appraisal data used to inform decisions in the following areas?” | 4 | “Training and development”, “career moves” | 0–3 Likert scale, 0: “not at all”, 3:” to a very great extent” | standardization of each item, sum of items and standardization |
| Appraisal total | 1 | 4 | sum of IX |
3.2.5 Employer branding orientation
EB orientation was assessed using the 4-item scale by Tumasjan et al. (2020). Consistent with its conceptualization, the scale captures the firm's employer branding orientation as an organizational-level strategic orientation, as reported by managers. Sample items included: “employer branding is essential in running this company” and “long-term employer brand planning is critical to our future success”. Responses were rated on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). The scale showed high internal consistency (Cronbach’s α = 0.80, mean = 4.48).
3.2.6 Control variables
At the individual level, we controlled for four variables commonly associated with performance outcomes. Gender was coded as a binary variable (1 = male, 2 = female), and age was measured as a continuous variable in years. Educational background was recorded on a 5-point ordinal scale, ranging from 1 (high school degree) to 5 (doctoral degree). Job hierarchy was operationalized as a relative position score, calculated by dividing the respondent’s level within the organizational hierarchy by the total number of levels in the firm. This produced a continuous variable ranging from 0 to 1, with higher scores indicating higher positions. For example, managers had a mean score of 0.86, while employees averaged 0.52, indicating mid-level positions. At the firm level, we controlled for three variables. Firm size was measured as a continuous variable using the natural logarithm of the number of employees. Family ownership was recorded as a binary variable (0 = non-family firm, 1 = family firm). Finally, sector was coded as a categorical variable distinguishing between primary/secondary sectors (0) and trade/services (1).
To ensure instrument accuracy and conceptual equivalence in the Greek context, all instruments were translated using the back-translation method (Brislin et al., 1973). One researcher translated the original English items into Greek, and a second independently translated them back into English. A third researcher reviewed both versions, resolving inconsistencies through discussion. Semantic equivalence exceeded 90% before final reconciliation. Descriptive statistics and the correlation matrix for all variables are presented in Table 5.
Descriptive statistics and correlations
| M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Individual level | ||||||||||||||
| 1. Employee performance | 3.84 | 0.68 | 1 | |||||||||||
| 2. iEBd | 0.60 | 0.61 | −0.24** | 1 | ||||||||||
| 3. Gender | 1.50 | 0.50 | −0.00 | 0.13** | 1 | |||||||||
| 4. Age | 37.99 | 8.73 | −0.05 | 0.08 | −0.09* | 1 | ||||||||
| 5. Education | 2.85 | 1.09 | 0.03 | 0.04 | 0.08 | −0.15** | 1 | |||||||
| 6. Job hierarchy | 0.51 | 0.19 | −0.05 | −0.07 | 0.07 | 0.23** | 0.12** | 1 | ||||||
| Firm level | ||||||||||||||
| 7. HRM | −0.14 | 4.10 | 0.12** | −0.13** | −0.03 | −0.10** | 0.19** | 0.01 | 1 | |||||
| 8. EB orientation | 4.48 | 0.44 | 0.03 | −0.11** | −0.05 | −0.07 | 0.07 | −0.07 | 0.39** | 1 | ||||
| 9. Organ. performance | 4.18 | 0.76 | 0.03 | 0.06 | 0.02 | 0.12** | 0.03 | 0.13** | 0.01 | −0.07 | 1 | |||
| 10. Firm’s size | 3.64 | 1.03 | −0.05 | 0.26** | 0.07 | 0.22** | −0.08 | −0.08 | −0.13** | −0.28** | 0.32** | 1 | ||
| 11. Family ownership | 0.63 | 0.48 | −0.12** | 0.09* | −0.00 | 0.11** | −0.26** | −0.02 | −0.11** | 0.11** | 0.11** | 0.15** | 1 | |
| 12. Sector | 0.73 | 0.44 | −0.09* | 0.03 | −0.03 | 0.11** | −0.16** | 0.02 | −0.08 | - −0.10** | −0.03 | −0.06 | 0.52** | 1 |
Note(s): For the variables of gender, education, organizational performance, family ownership, and sector, non-parametric properties were considered, as these variables may not fully meet the assumptions of Pearson correlation due to their categorical or ordinal nature
3.3 Multicollinearity and heteroskedasticity diagnostics
To test model robustness, we examined multicollinearity and heteroskedasticity. Variance inflation factors (VIF) and tolerance values (1/VIF) were used to assess multicollinearity (Hair et al., 2011). Results indicated no evidence of multicollinearity. Separate diagnostics at the individual and firm levels yielded acceptable tolerance and VIF values. At the individual level, tolerance values ranged from 0.85 to 0.96, whereas at the firm level, VIFs ranged from 1.60 to 1.86. Heteroskedasticity was tested using the Breusch-Pagan test (Breusch and Pagan, 1979). All p-values exceeded 0.05, indicating no evidence of heteroskedasticity.
3.4 Common method variance (CMV)
Harman’s one-factor test (Podsakoff and Organ, 1986) was used to assess common method variance. Multiple factors with eigenvalues exceeding 1 emerged, and the first factor accounted for 18.42% of the total variance, suggesting that CMV was not a major concern. Additionally, the hypothesized multi-factor measurement model demonstrated substantially better fit than a single-factor alternative, further reducing concerns that a common latent factor accounts for the observed relationships.
3.5 Confirmatory factor analysis
Confirmatory factor analyses (CFAs) were conducted using STATA 14 to assess the discriminant validity of all constructs. Firm- and employee-level measures were estimated simultaneously within a unified measurement model. Following Hu and Bentler (1999), we tested three competing models to ensure construct distinctiveness. The hypothesized three-factor model, consisting of one second-order construct of iEBd and two constructs of EB orientation and employee performance, demonstrated superior fit and acceptable indices across all criteria, supporting discriminant validity (see Table 6).
Robustness – discriminant validity
| χ2 | df | χ2/df | Δχ2 | Δdf | CFI | TLI | RMSEA | SRMR | CD | |
|---|---|---|---|---|---|---|---|---|---|---|
| Hypothesized model | 654.24 | 311 | 2.10 | 0.94 | 0.94 | 0.05 | 0.05 | 1 | ||
| Alternative model 1 | 918.46 | 308 | 2.98 | 264.22* | 3 | 0.90 | 0.88 | 0.06 | 0.11 | 1 |
| Alternative model 2 | 655.88 | 313 | 2.10 | 1.64* | 2 | 0.94 | 0.93 | 0.04 | 0.04 | 0.93 |
Note(s): Hypothesized model (three-factor model) is compared with alternative model 1 (a six-factor model), 2 (a one-factor model)
*p < 0.01
4. Hypotheses testing
4.1 Main analyses
Multilevel structural equation modeling (MSEM) was conducted in STATA 14 using the generalized structural equation modeling (GSEM) command to test the hypothesized relationships. Results are reported in Table 7.
MSEM analysis results
| Relationships | B | S.E. | P |
|---|---|---|---|
| Dependent variable: HRM | |||
| EB orientation | 3.58** | 0.10 | 0.00 |
| Dependent variable: iEBd | |||
| HRM | −0.02** | 0.01 | 0.00 |
| Dependent variable: employee performance | |||
| HRM | 0.02** | 0.01 | 0.03 |
| iEBd | −0.26** | 0.48 | 0.00 |
| Gender | 0.03 | 0.06 | 0.60 |
| Age | 0.00 | 0.00 | 0.96 |
| Education | −0.12 | 0.03 | 0.68 |
| Job hierarchy | −0.21 | 0.16 | 0.20 |
| Dependent variable: organizational performance | |||
| HRM | 1.25** | 0.31 | 0.00 |
| Employee performance | −0.03 | 1.81 | 0.99 |
| Firm’s size | 1.07 | 1.21 | 0.88 |
| Family ownership | 8.47* | 3.03 | 0.00 |
| Sector | −9.60** | 3.29 | 0.01 |
Note(s): **p < 0.01, *p < 0.05
The multilevel SEM results support all hypothesized relationships. Among the control variables, both family ownership and sector had significant effects (p < 0.05). We next examined the mediating role of HRM between EB orientation and both 1) employee performance and 2) organizational performance. In both cases, the indirect effects were significant, supporting the mediating role of HRM. Specifically, HRM fully mediated the relationship between EB orientation and employee performance (B = 0.07, p < 0.05), as well as between EB orientation and organizational performance (B = 1.25, p < 0.05).
Finally, we examined whether iEBd mediated the relationship between HRM and employee performance. The results revealed a significant indirect effect (B = 0.01, p < 0.05), supporting H2.
5. Discussion
Drawing on signaling theory and informed by expectation–disconfirmation theory (EDT), this study develops and tests a multilevel model linking EB orientation to employee and organizational performance through HRM practices and iEBd. The findings support all hypothesized relationships.
When EB orientation is embedded within strategically aligned HRM systems, performance improves at both individual and organizational levels, reinforcing the strategic role of employer branding and the importance of alignment with HRM practices (Zografou and Galanaki, 2024). The mediating role of HRM indicates that such practices operate not as generic administrative systems but as brand-centered mechanisms that embed the EVP in employees’ everyday experiences. This interpretation aligns with arguments that HRM practices function as organizational signals through which employees infer priorities and intentions (Guest et al., 2021). Employer branding therefore operates as a strategic orientation whose internal effects materialize through HRM practices, shaping employees’ interpretations of their work experiences. This interpretation also resonates with internal branding research highlighting how brand-centered HRM translates organizational intentions into employees’ experiences and behaviors (Saleem and Iglesias, 2016; Du Preez et al., 2017). However, unlike internal branding, which emphasizes brand-centred HRM to align employees with the corporate brand, the present study focuses on how HRM enacts employer brand signals that employees evaluate against their ideal employment experiences. In this sense, the study conceptualizes employer branding as an internal signaling process through which employer brand signals shape employee experiences and performance outcomes.
Regarding iEBd, the negative associations between HRM and iEBd, and between iEBd and employee performance, underscore HRM’s role in narrowing the discrepancy between employees’ ideal and actual employment experiences. These findings position HRM not only as a signaling mechanism but also as a means of minimizing discrepancies between the two. Unlike constructs such as value internalization (Ryan and Deci, 2000), person–organization fit (Cable and Judge, 1996), and psychological contract breach (Lester et al., 2002), iEBd captures a dynamic evaluative gap between employees’ personally valued employment ideals and their lived work experiences. From a signaling perspective and consistent with expectation–disconfirmation reasoning, iEBd reflects an ongoing comparison process through which employees assess the effectiveness of brand-centered HRM practices against their internally held employment ideals. It therefore represents a continuous evaluative process rather than a stable attitudinal alignment. Coherent HRM practices are linked to lower iEBd and improved employee performance, whereas misaligned or underdeveloped practices increase the risk of dissonance, undermining employee outcomes. The findings echo prior work by Makhecha et al. (2018), who emphasize the importance of coherence between intended, implemented, and perceived HRM practices. They extend this framework by showing that HRM signals consistent with employees’ ideals and experiences enhance employee performance through more effective internal signaling.
EDT further explains these findings by suggesting that employees evaluate experiences against personally held ideals. In this study, this evaluative discrepancy is captured by iEBd and is associated with employee performance. This perspective complements signaling theory by clarifying how employees translate organizational signals into behavioral outcomes, although these findings should be interpreted in light of the use of a self-reported measure of employee performance.
At the organizational level, EB orientation functions as a strategic framework guiding the coherent implementation of HRM practices. When employer branding is embedded within organizational systems, HRM practices become more aligned and internally consistent, which is associated with improved firm-level performance. Signaling therefore operates not only through employee interpretation but also through the coordination and strategic alignment of HRM practices at the firm level.
Unexpectedly, no direct relationship emerged between individual and organizational performance, even though HRM was significantly related to both. This lack of a direct link suggests that organizational outcomes may depend on broader contextual or strategic factors beyond individual performance. One plausible interpretation is that employer branding, as perceived by employees, may foster internal alignment and engagement, thereby improving collective efficiency and firm-level outcomes through indirect mechanisms not captured in this model. Claims concerning external stakeholder perceptions, legitimacy, or broader reputation outcomes fall outside the empirical scope of the present study (Backhaus and Tikoo, 2004; Cable and Turban, 2003).
6. Theoretical and practical implications
This study advances understanding of the synergy between employer branding and HRM in shaping organizational performance (Zografou and Galanaki, 2024), extending the HRM-performance link. Building on signaling and expectation–disconfirmation perspectives, the findings show that employer branding, when strategically enacted through brand-centered HRM practices, is associated with meaningful employee experiences that are linked to individual and organizational performance. By introducing iEBd, the study adds a new dimension to employer branding research, identifying the evaluative mechanism through which employees make sense of employer brand signals enacted through HRM practices. Grounded in EDT, this discrepancy-based perspective clarifies how alignment between employees' ideal and actual work experiences shapes their performance.
Taking a multilevel perspective, the study contributes to ongoing debates on ideal versus actual HRM, as well as the distinctions among intended, perceived, and actual HRM (Wright and Nishii, 2013). It offers a new angle on the HRM “black box” discussion (Boxall et al., 2011) by conceptualizing EB orientation as a proxy for intended HRM and iEBd as a measure of the gap between ideal and actual HRM. This positions employer branding as a signaling mechanism operating across HR practices, leadership discourse, and organizational identity.
From a practical perspective, the findings underscore the importance of coupling coherent HRM practices with clear and consistent communication. The iEBd construct serves as a diagnostic tool for evaluating the alignment between employees' ideal and actual work experiences. When such discrepancies emerge, employee performance may be adversely affected. Accordingly, iEBd reflects the extent of this internal misalignment.
To address this risk, management should regularly reassess the consistency of its employment-related signals (Collins and Martinez-Moreno, 2022) and ensure alignment between internal employer branding (targeted at current employees) and external employer branding messaging (targeted at potential hires). Fostering open, two-way communication (Prouska et al., 2022) enables organizations to better understand how employees interpret the employment experience and to identify potential misalignments between employees’ ideal and lived employment experiences. Such dialogue does not necessarily imply that HRM practices should be adapted to individual preferences. Rather, it fosters clarity and coherence in how employment signals are enacted and understood. Accordingly, employer branding requires continuous evaluation, and tracking iEBd provides a practical mechanism for monitoring alignment between employees’ ideals and organizational realities, supporting sustainable engagement and performance (Moroko and Uncles, 2008).
iEBd also supports cross-functional collaboration. Aggregated iEBd insights can inform organizational decision-making. HR can use these insights to inform recruitment and retention decisions, marketing can ensure greater consistency in employer branding messaging, and public relations (PR) can maintain consistency between internal and external employer brand communication. Such coordination across HR, marketing, and PR (Wang et al., 2022) is critical for building a coherent employer offering, particularly in SMEs, where cross-functional agility is often greater.
Finally, the study reinforces the strategic value of employer branding in the SME context. Unlike larger firms with established reputational capital, SMEs often lack visibility in the talent market (Abid and Loufrani, 2024). For these firms, employer branding helps build a distinctive employer identity. When HRM practices and employer branding messaging are aligned, SMEs are better positioned to enhance their attractiveness and compete more effectively for talent. Employer branding therefore emerges not only as a talent management tool but also as a mechanism for strategic positioning grounded in consistent organizational signaling.
7. Limitations and suggestions for future research
This study has several limitations that provide avenues for future research. First, the data were drawn exclusively from Greek SMEs, which constrains generalizability. Although SMEs represent a significant segment of the economy, it remains unclear whether these relationships hold in larger organizations. Future studies should therefore compare SMEs and large firms to examine contextual differences in the effectiveness of employer branding strategies.
Second, the cross-sectional design restricts causal inference. Despite the model’s robustness, causal relationships cannot be firmly established. Longitudinal research could provide further insights into how employer branding strategies evolve and influence outcomes over time, while replication in post-pandemic work contexts (e.g. hybrid and remote arrangements) could enhance temporal relevance.
Third, employee performance was limited to job-related outcomes, excluding broader dimensions such as innovation, teamwork, organizational citizenship, and career development (Welbourne et al., 1998). Future research should incorporate multi-role performance indicators that capture these additional dimensions.
Fourth, employee performance relied on self-reported measures, which may introduce perceptual bias. Although the performance items referred to specific job-related behaviors and the multilevel design reduced single-source bias by separating predictors and outcomes across respondents, responses may still reflect social desirability or self-enhancement effects. Future studies could incorporate supervisor-rated or objective performance indicators to strengthen robustness.
8. Conclusion
This study demonstrates that embedding EB orientation within coherent HRM systems enhances both employee and organizational performance, shaping how the employment offering is reflected in employees’ actual work experiences. By foregrounding employees’ perceptions of these experiences, the findings suggest that employer branding relates to performance through interpretive processes captured by iEBd. This construct offers both a conceptual lens and a practical tool for examining how organizational signaling operates through HRM systems.
Notes
The ICAP Data Prisma includes basic business data, balance sheets and income statements for more than 30,000 Greek businesses as well as sector financial data. Using specific search parameters, researchers can search for an individual business or several ones.
Employee response rates averaged 56% in micro firms (range: 30–80%), 41% in small firms (13–100%), and 26% in medium-sized firms (13–57%).
