# Measuring employee involvement in Lean Manufacturing efforts: proposal of a robust scale

Nayara Cardoso de Medeiros, Moacir Godinho Filho, Mario Henrique Callefi, Gilberto Miller Devós Ganga, Jonhatan Magno Norte da Silva, Matthias Thürer, Léony Luis Lopes Negrão, Fabiane Letícia Lizarelli

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## To cite this version:

Nayara Cardoso de Medeiros, Moacir Godinho Filho, Mario Henrique Callefi, Gilberto Miller Devós Ganga, Jonhatan Magno Norte da Silva, et al.. Measuring employee involvement in Lean Manufacturing efforts: proposal of a robust scale. International Journal of Production Research, 2025, 63, pp.4590 - 4615. 10.1080/00207543.2024.2448767. hal-05376906

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# Measuring employee involvement in Lean Manufacturing efforts: proposal of a robust scale

Nayara Cardoso de Medeiros, Moacir Godinho Filho, Mario Henrique Callefi, Gilberto Miller Devós Ganga, Jonhatan Magno Norte da Silva, Matthias Thürer, Léony Luis Lopes Negrão & Fabiane Letícia Lizarelli

**To cite this article:** Nayara Cardoso de Medeiros, Moacir Godinho Filho, Mario Henrique Callefi, Gilberto Miller Devós Ganga, Jonhatan Magno Norte da Silva, Matthias Thürer, Léony Luis Lopes Negrão & Fabiane Letícia Lizarelli (2025) Measuring employee involvement in Lean Manufacturing efforts: proposal of a robust scale, International Journal of Production Research, 63:12, 4590-4615, DOI: 10.1080/00207543.2024.2448767

**To link to this article:** https://doi.org/10.1080/00207543.2024.2448767

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# Measuring employee involvement in Lean Manufacturing efforts: proposal of a robust scale

Nayara Cardoso de Medeiros <sup>a, b</sup>, Moacir Godinho Filho <sup>c, b</sup>, Mario Henrique Callefi <sup>d</sup>, Gilberto Miller Devós Ganga <sup>b</sup>, Jonhatan Magno Norte da Silva <sup>e</sup>, Matthias Thürer <sup>d</sup>, Léony Luis Lopes Negrão <sup>b, f</sup> and Fabiane Letícia Lizarelli <sup>b</sup>

## ABSTRACT

This research addresses a significant gap in the academic literature: the lack of robust tools for quantifying employee involvement in Lean Manufacturing (LM). This shortfall is critical, as employee involvement is a cornerstone of LM's success. To address this deficiency, our study creates a comprehensive measurement scale designed to assess and enhance employee involvement in LM environments. The development of the scale followed a systematic and meticulously organised process. The validation process of constructs identified in the literature review involved conducting a series of expert interviews, adopting the Q-Sort method, utilising different statistical instruments, and administering a preliminary test. This pre-test, conducted with 94 operators, utilised exploratory factor analysis to refine the scale and used a non-probability sampling method. Subsequently, to improve the scale's validity and precision, a comprehensive survey was performed involving 176 employees and confirmatory factor analysis employed to verify its suitability for practical application. Theoretical contributions of this study include expanding the dimensions of literature on employee involvement in LM and providing a novel, robust scale for use in longitudinal and interdisciplinary studies. Managerially, the scale serves as a diagnostic tool for decision-makers, aiding in mapping, identifying, and strategizing employee involvement practices in LM implementations.

## ARTICLE HISTORY

Received 24 January 2024
Accepted 21 December 2024

## KEYWORDS

Employee involvement; lean manufacturing; scale development; continuous improvement; employee engagement

## SUSTAINABLE DEVELOPMENT GOAL

SDG 9: Industry, Innovation and infrastructure

## 1. Introduction

Lean Manufacturing (LM) originated from the Toyota Production System (Krafick 1988; Ohno 1998) and was conceptualised in the West in the early 1990s (Womack and Jones 1996). Today, LM is one of the dominant management paradigms in the industry (Frank et al. 2024). It aims to enhance productivity and quality by eliminating waste and improving processes. LM is essential in various industries, driving continuous improvement and operational efficiency (Kumar et al. 2023). In this context, the substantial impact of employee involvement on the successful adoption and enduring effectiveness of LM methodologies is well documented (Schumacher et al. 2023). The essence of LM is not merely about tools, methods, and techniques but crucially hinges on the employee's role. A common LM adage, 'develop people first, then products,' emphasises the importance of

nurturing a workforce that brings the system to life through problem-solving, collaboration, and growth.

The advantages of having well-trained employees responsible for quality and continuous improvement are immense. Employees' active participation in data gathering to detect process differences and their authority to make decisions regarding their work are crucial factors for the effective implementation and long-term viability of LM practices. Establishing participatory objectives and deep-seated respect for individuals within the organisation are beneficial and essential components of LM success (van Assen 2021). An integrated approach, valuing both technical understanding and human involvement, is critical to realising the full potential of LM methodologies. However, there is a noticeable gap in the literature, with a tendency to prioritise technical LM practices like rapid tool changes, process levelling, and Kanban systems

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<footnote><sup>a</sup>Center of Technology, Graduation of Industrial Engineering, Federal University of Piauí, Teresina, Brazil; <sup>b</sup>Department of Industrial Engineering, Federal University of São Carlos, São Carlos, SP, Brazil; <sup>c</sup>Department of Supply Chain Management and Decision Sciences, EM Normandie Business School, Metis Lab, Le Havre, France; <sup>d</sup>Chair of Factory Planning and Intralogistics, Chemnitz University of Technology, Chemnitz, Germany; <sup>e</sup>Industrial Engineering Course, Backland Campus, Federal University of Alagoas, Delmiro Gouveia, AL, Brazil; <sup>f</sup>Department of Industrial Engineering, State University of Pará, Castanhal, Brazil</footnote>
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over crucial human resource strategies such as employee involvement. This oversight, highlighted in various studies, points to the necessity for adopting a more equitable strategy that accords equal importance to both technical and human aspects in LM implementation (Buer, Strandhagen, and Chan 2018; Negrão, Godinho Filho, and Marodin 2017).

Improving employee involvement in LM is a process that requires a combination of strategies focusing on empowerment, communication, training, and leadership. Empowering employees by providing them with the necessary training and autonomy to make decisions can significantly improve their involvement in LM initiatives (Marin-Garcia and Bonavia 2015). Clear and consistent communication about the goals, processes, and benefits of LM practices can help gain employee buy-in and participation (Dombrowski, Mielke, and Schulze 2012). LM training is referred to frequently in the literature as playing an important role in the success of LM implementation and in continuous improvement programmes (Dinis-Carvalho 2021). Senior management commitment and LM leadership are crucial in sustaining employee involvement (Bianco et al. 2023). Leaders must demonstrate commitment to LM principles and support employees' participation in LM activities.

Measuring their impact accurately is essential for all these efforts to understand their effectiveness and refine strategies accordingly. There are established scales for evaluating specific aspects in other operations management domains, including operational flexibility in hospitals (Chahal, Gupta, and Lonial 2018), customer perceived value of electric vehicles (Miao et al. 2014), and postponement strategies (Saghiri 2011). This type of scale is essential because it provides a fitting metric for assessing various factors, mainly since their development entails thoroughly examining reliability and validity (Forza 2002). Our research aims to address this gap by developing a novel measurement scale tailored for assessing employee involvement in LM implementation. More specifically, our study aims to address the following research question:

Research Question: How can employee involvement in LM implementation be effectively measured?

The existing body of literature does not yet provide a comprehensive instrument that managers can utilise to effectively measure employee involvement in the context of LM (van Assen 2021). Although existing research has developed various scales to measure LM and its principles, most of these scales are generally suited for LM and do not focus specifically on employee involvement. For example, Shah and Ward (2007) pioneered the

creation of comprehensive scales to measure LM constructs by identifying key measurement items through extensive literature review and empirical methods. Susilawati et al. (2015) proposed a fuzzy logic-based method that addresses the multi-dimensionality of LM practices and incorporates subjective human judgment, which is essential for evaluating employee involvement. Pakdil and Leonard (2014) created the Lean Assessment Tool (LAT) to measure various aspects of LM implementation, while Huang, Harris, and Loyd (2020) improved LM assessment instruments based on employee perception, further emphasising the role of employee insights in LM implementation. Meanwhile, Amin et al. (2021) also introduced a fuzzy-based leanness evaluation model that includes employee involvement as a key metric, and Costa et al. (2021) developed a Lean Six Sigma scale for the food industry.

Despite these valuable contributions, our research aims to develop a novel measurement scale specifically tailored to assess employee involvement in LM implementation, which is missing in the literature. This validated instrument will enhance academic research and practical decision-making in involving employees in LM efforts. For researchers, the scale can be used in future studies focused on employee involvement within the context of LM, providing a reliable tool for measurement and analysis. For companies, using our assessment scale would not only facilitate the identification of employee participation levels but also assist managers in discerning which practices necessitate additional investment and attention, hence augmenting the possibility for increased involvement (Wright et al. 2017).

The remainder of this paper is structured as follows. First, the literature is reviewed to provide the background to our study in Section 2. The research method is then outlined in Section 3, before the proposed measuring scale is introduced in Section 4. Section 5 discusses the scale, presenting useful perspectives on its application and effectiveness. Finally, Section 6 summarises important conclusions, limitations, and directions for future research.

## 2. Conceptual background

According to Marodin et al. (2023), LM aims at increasing the emphasis on creating value for customers, which leads to an improvement in performance. Academic literature on LM is widely available (Antony et al. 2021). One important finding from this literature is that LM consists of both technical (hard) and human-related (soft) components (Hadid and Mansouri 2014). But despite a few early studies on the social and behavioural aspects of LM
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systems, these aspects have only recently received sig-
nificant research interest (Januszek et al. 2023). Stelson
et al. (2017) even argue that many LM implementation
efforts fail because managers primarily focus on technical
tools while neglecting crucial sociocultural factors such
as effective leadership and employee involvement. This
perspective is supported by recent research highlighting
the importance of addressing these sociocultural aspects.

For example, Duggan, Cormican, and McDermott (2023) emphasise that an understanding of individual-level factors, such as personality and affective organisational commitment, is fundamental to successful LM implementations. They found that employees with higher levels of affective commitment are likelier to adopt LM practices, especially when management involves top performers in LM initiatives. Similarly,Knapić, Rusjan, and Božič (2023) highlighted the importance of first-line employees (FLEs) in LM implementation, noting that factors such as cultural change, employee characteristics, and employee involvement are essential for the successful adoption of LM. Ahlstrand and Gautié (2023) further demonstrate that variations in employee involvement are significantly influenced by managerial orientations and labor-management relations, which can either enhance or hinder the implementation of LM practices.

Soft factors play significant roles in either inhibit-
ing or facilitating LM adoption. Employees’ resistance
due to fear of job loss or disruption in routine can
impede LM implementation. Effective change manage-
ment strategies are essential to address this resistance
(Sahu, Padhy, and Dhir 2022). Additionally, insufficient
commitment from top management can hinder LM prac-
tices. Leadership involvement is also critical for fostering
a culture that embraces LM principles (Duggan, Cormi-
can, and McDermott 2023). Knowledge and skills are
also crucial factors. Lack of adequate training and under-
standing of LM tools can lead to poor implementation.
Continuous education and skill development are neces-
sary to empower employees (Sakthi Nagaraj and Jeya-
paul 2021). The perceived complexity of LM tools can
discourage adoption, but simplifying tools and provid-
ing clear guidelines can mitigate this barrier (Swarnakar,
Tiwari, and Singh 2020). Financial constraints, such as
high upfront costs for LM implementation, can be a
deterrent, especially for small and medium-sized enter-
prises (SMEs). Financial planning and phased implemen-
tation can help manage these costs (Hashem and Aboel-
maged 2023). Meanwhile, factors facilitating LM adop-
tion include strong leadership and management commit-
ment. Leaders who actively promote and support LM
initiatives can significantly enhance adoption by lead-
ing by example and providing the necessary resources
(Bianco et al. 2023).

In general, effective human resource management (HRM) practices, including continuous training, involve- ment of external change agents, and fostering a learn- ing organisation, are essential in achieving LM success (Koemtzi et al. 2023). Prasad et al. (2020) investigate the significance of interdependency among 11 essential suc- cess criteria for implementing LM. They discovered that many facets of HRM are deemed highly significant prac- tices. One of these practices is employee involvement. According to Wickramasinghe (2016), employee involve- ment can be defined as the extent to which an individual develops a psychological connection with their assigned work responsibilities. This perspective emphasises the significance of employees’ psychological and emotional attachment to their positions, drawing attention to the underlying dimensions of workplace involvement that go beyond the mere fulfilment of tasks.

Employee involvement is crucial for the success of organisational initiatives, fostering a sense of ownership and engagement among the workforce (Brown and Cre- gan 2008). Numerous studies emphasise the importance of including employees in decision-making processes and organisational changes to achieve positive outcomes across various management areas and approaches, not just in LM. For example, Alsos and Trygstad (2023) explore the impact of participation structures on work- ers’ voices, revealing that the formality of meetings with management influences local trade union representatives’ assessment of their influence at the company level. Ritter, Small, and Christy (2023) investigate the role of leader- ship perceptions in performance appraisal involvement, finding that employee perceptions of leader prototypi- cality are critical in involving employees in the perfor- mance appraisal process. This engagement, in turn, pos- itively affects job and reward satisfaction. Temel, Lozano, and Barreiro-Gen (2022) investigate factors of employee involvement for organisational sustainability. They show that integrating employee involvement into organisa- tional systems improves sustainability efforts by lever- aging the human dimension of organisational systems. Kim (2023) explores participative management and its impact on organizational citizenship behavior (OCB) among public employees. The study finds that partic- ipative management enhances job involvement, lead- ing to higher OCB. Finally, Pun, Chin, and Gill (2001) found that management commitment, rewards and moti- vation were the most critical factors for employee involvement. Although employee involvement may be independent of intrinsic and extrinsic motivation, in the long term some motivation is needed to sustain involvement.

Specifically related to LM, recent research also high-
lights the significant role of employee involvement.
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For example, Hardcopf, Liu, and Shah (2021) investigate the influence of organisational culture, including employee involvement, on LM implementation, finding that a supportive organisational culture maximises quality, delivery, and flexibility improvements. Soliman and Saurin (2022) present a framework for understanding the gap between lean-as-imagined and lean-as-done, emphasising the importance of employee self-organisation, involvement, and resilience in LM implementation. Yadav et al. (2020) claim that employee involvement in shop-floor management is crucial for successful LM implementation. Buer et al. (2021) highlight the importance of employee involvement in both LM and digitalisation efforts to maximise benefits. Bittencourt, Alves, and Leão (2021) performed a systematic literature review on the role of lean thinking in Industry 4.0, in which employee involvement was identified as crucial for successful LM and Industry 4.0 integration.

It can be concluded that employee involvement plays a particularly significant role in LM implementation, where establishing a team-oriented environment is crucial for success. However, academic research has encountered challenges in studying the relationship between employee involvement and firm success. This challenge stems from the absence of a comprehensive measurement tool for assessing employee involvement despite its growing recognition of importance. A measurement scale for assessing employee involvement is essential to track the progress of employee involvement, providing substantial assistance to researchers, consultants, and managers alike. This scale allows stakeholders to make more informed and strategic decisions regarding their LM projects. Although studies have examined various features of employee involvement, they have predominantly focused on areas such as evaluating organisational-level stress management interventions or developing employer involvement measurement tools for HRM (Koemtzi et al. 2023). Other research explores the mediation of employee involvement and the moderation of mental involvement (Ababneh 2023). While research targeting worker involvement in LM exists, employee involvement often takes a backseat, serving as a secondary aspect rather than the primary focus in these studies. For instance, studies primarily aimed at measuring LM processes (e.g. Godinho Filho, Ganga, and Gunasekaran 2016; Herzog and Tonchia 2014) do not adequately address individual employees' thoughts, ideas, and feelings toward LM involvement. Therefore, a specialised measurement instrument that can accurately capture and evaluate employee involvement is needed, specifically in the context of LM. This is the gap explored by our research.

### 3. Research method

This study's methodological approach to scale development includes four steps, which are in line with the approach outlined by Gonella et al. (2023), Borges et al. (2021), Menor and Roth (2007), and Churchill (1979). We referred to it as a 'multi-dimensional measurement scale' with the intention to emphasise the structured approach we used to assess various constructs and items related to worker involvement in LM. This approach aligns with prevalent methods within the operations management field (for example, Menor and Roth 2007), where the construction and itemisation of scales are commonly outlined, albeit without assigning explicit weights to the constructs. Some recent examples follow this approach, such as Mota et al. (2025), Gonella et al. (2023), Borges et al. (2021), and Costa et al. (2021). However, some scale development designs, such as the one by Klein et al. (2023), go a step further by not only proposing constructs and items but also by assigning weights to the constructs based on empirical validation. Our approach did not include this weighting as we believed the importance of constructs could vary significantly across different industries and sectors, making a one-size-fits-all weighting scheme less applicable.

Some modifications were necessary to suit the specific context of employee involvement within LM. Our approach allows for a comprehensive research presentation, linking the design directly to the findings and enabling a more effective evaluation of validity and reliability. Moreover, it also minimises repetition and thus improves understanding of the steps performed and the results presented, and it is particularly well-suited for studies focusing on scale development since it enables a more coherent exposition of the scale's development procedure, mainly concerning psychometric characteristics and observed results (Costa et al. 2021; Kauppi and Luzzini 2022). Our four research steps are shown in Figure 1.

Step 1 involved mapping the components and identifying the scale's initial items. A systematic literature review was therefore conducted to identify the various practices associated with employee involvement in LM. A systematic literature review was chosen since it enables greater rigour and reproducibility (Durach, Kembro, and Wieland 2017). We followed the steps recommended by Tranfield, Denyer, and Smart (2003) and also adopted the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach (Moher et al. 2009) to structure the review process. In June 2024, three academic databases were queried: Scopus, Web of Science, and Engineering Village. These
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<figure><img src="image_22.png" /><figcaption>**Figure 1.** Stages of the methodological process.</figcaption></figure>

databases were selected for their comprehensive coverage of high-quality, peer-reviewed literature across various disciplines relevant to the study. As shown in Figure 2, the initial search identified 702 unique articles. After a detailed screening process, 322 articles were selected for full-text review, and among these, 60 were identified as addressing practices related to employee involvement in LM initiatives.

For the content analysis of the identified articles, the list of employee involvement practices proposed by Medeiros et al. (2023) was considered. The main result from the content analysis was a list of 15 employee involvement practices in LM and 72 items. A panel of four LM experts evaluated these 15 practices and 72 items. The purpose of this assessment was crucial in semantically assessing the practices and items while eliminating any confusing or redundant elements. Following the aforementioned changes, all 15 practices related to employee involvement were retained. However, six items were extracted, resulting in a final count of 66 items.

Step 2 evaluates the perceived adequacy of the measurement items of the constructs. For this, Q-sort was adopted to tentatively assess measurement item's reliability and validity (item-to-construct sorting analysis) and to identify potentially ambiguous items using multiple

rounds of item sorting to obtain responses from independent panels of informed judges (Menor and Roth 2007). During the Q-sort the judges are asked to classify items based on their similarity with definitions and descriptions of underlying construct categories following guidelines from DeVellis (2022), Costa et al. (2021), and Menor and Roth (2007). For each item-sorting round, judges were directed to carefully read the descriptions of each of the fourteen construct dimensions and to match each item to the one dimension that they felt was the best fit (Menor and Roth 2007).

This process involved four iterations. The panel of judges was composed of experts selected based on their extensive knowledge and experience in LM. They were sourced from three different groups: (i) former PhD students from the Industrial Engineering graduate programme (Operations Management area) at the Federal University of São Carlos, (ii) LinkedIn contacts of professors within the Operations Management field, specifically those specialising in LM, and (iii) authors of papers related to LM identified through Google Scholar and ResearchGate. To determine the number of judges in each round, we referred to Kauppi and Luzzini (2022) and Menor and Roth (2007). Menor and Roth (2007) employed 6 judges in stages 1 and 2, and 10 and 17 judges
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<figure><img src="image_24.png" /><figcaption>**Figure 2.** Article selection process.</figcaption></figure>

in stages 3 and 4, respectively. Kauppi and Luzzini (2022) employed 6 judges in rounds 1-3, and 5 judges in round 4. Bailey et al. (2019) further state that sample sizes in Q-sort are relatively small in contrast to the large sample sizes required by traditional quantitative methods. Thus, four rounds were conducted following the convenience sampling strategy, including 18 judges in each round, amounting to a total of 72 experts.

As indicated by Menor and Roth (2007), for each iteration three different inter-rater reliability measures were used: inter-judge agreement percentage, Cohen's kappa and Perrault and Leigh's Ir. The first is the ratio of item classification to the overall number of judgments performed (Moore and Benbasat 1991). The second assesses the degree to which the observed level of agreement among judges surpasses what would be anticipated by

random chance (Cao and Zhang 2013). According to Gonella et al. (2023), an index value between 0.40 and 0.60 and a kappa value between 0.20 and 0.40 are deemed reasonable. The third denotes the likelihood that two judges operating independently arrive at an adequate conclusion. According to Rossiter (2011), this estimate suggests that values below 0.6 or 0.5 require adjustments. To ensure the study's credibility, we also assessed substantive face validity using validity estimators, specifically the proportion of substantive agreement (PSA) and the coefficient of substantive validity (CSV). The PSA determines the percentage of raters assigning an item to a specific construct. This indicator's validity is measured on a scale from 0 to 1, with higher values indicating greater validity. The CSV assesses the degree to which judges associate an item with a certain construct as opposed to
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any other construct. The CSV scale spans from – 1 to 1, where higher positive values indicate higher levels of substantive validity (Anderson and Gerbing 1991). Finally, the overall placement ratio (OPR), which evaluates the quantity of items added in each defined construct, was calculated to detect any potential measurement errors. The literature highlighted that OPR values exceeding 70% signify a high level of reliability (Moore and Benbasat 1991). After completing this step, 14 practices and 54 corresponding items were selected.

Step 3 applied a preliminary survey for refinement, purification, and pre-testing as in similar studies on scale development (e.g. Borges et al. 2021; Klein et al. 2023). The preliminary survey uses an exploratory sample and employs exploratory factor analysis (EFA) to assess and enhance the accuracy of the measures by identifying items with low loadings on the retained factors, thereby identifying candidates for exclusion from the construct (Anderson and Gerbing 1991; Lambert and Newman 2023). This purifies the scale based on a larger sample of target respondents, carrying out a pretest of the survey instrument (Borges et al. 2021; Gonella et al. 2023).

Initially, we identified 15 companies from our network that we previously knew were making efforts to implement LM. Through interviews with operations managers of these companies, we identified the level of LM implementation of these companies using the LM assessment tools developed by Pakdil and Leonard (2014) and Shah and Ward (2007). Subsequently, eight companies with the most advanced LM practices were selected for our study. We randomly chose three to participate in our pre-test. Through interviews with managers, shop floor sectors within these companies that are already implementing LM practices were identified. All employees from these sectors were invited to participate, resulting in 103 valid responses. As Forza (2002) highlighted, this non-probability sampling method is suitable for exploratory studies like ours.

We utilised the SurveyMonkey platform to effectively structure the questionnaire. The questionnaire, comprising 14 practices, was administered to our sample of 103 operators. Ensuring ethical standards, all respondents were provided with informed consent, the research objectives were outlined, and confidentiality was ensured. The questionnaire was organised into three categories: an informed consent section, a section for gathering demographic data, and a section aimed at collecting data on the extent of employee involvement in LM. This section utilised a seven-point Likert scale, allowing participants to express their agreement levels concerning various statements related to the analysed constructs and items. An analysis of the responses was then conducted to detect

possible problems in the operators' responses, such as straight-lining, missing data, or multivariate outliers. To identify these wrongs, the Z-score values were analysed and responses with Z-score values above 2.58 excluded (Faulkenberry 2022).

Thus, 94 responses were considered for the EFA, which was applied to evaluate convergent and discriminant validity. This statistical technique examines the underlying patterns or relationships for multiple items and determines whether the information can be condensed into a smaller set of factors (Field 2013; Hair et al. 2019). Remarkably, none of the practices or items needed to be discarded during this study phase, indicating the robustness of our construct and measure development process.

Step 4 validates the constructs. In addition to EFA, convergent and discriminant validity and internal consistency need to be assessed to further confirm the scale's constraint structure (Hair, Howard, and Nitzl 2020; Sim, Kaliannan, and Avvari 2021). For step 4, we initially took the five remaining companies chosen in step 3 and interviewed managers to identify shop floor areas that had already implemented LM practices. Subsequently, we invited all employees from these sectors to participate in the survey. By maintaining consistency in our sampling strategy, we aimed to ensure the reliability and robustness of our research outcomes. The survey questionnaire was administered from April to August 2023, yielding 206 replies. The instrument was developed to measure each operator/employee's individual engagement in LM. For this reason, all operators of shop floor areas that had already implemented LM practices were invited to participate in the survey. On average, 26.21% of the target population responded to the questionnaire across the five selected companies. Based on Z-score values we identified 30 multivariate outliers, which led to a refined sample size of 176 respondents.

Confirmatory Composite Analysis (CCA) was employed to analyse the obtained results. CCA is a recently proposed alternative approach to evaluate the reliability and validity in developing, adapting, and confirming measurement scales when using PLS-SEM (Hair, Howard, and Nitzl 2020; Manley et al. 2021). CCA is similar to confirmatory factor analysis (CFA). Borges et al. (2021) highlighted its capability for simultaneous measurement and structural model analysis, facilitating complex survey models, including moderation and mediation. It can be performed with small sample sizes and provides accurate measure estimates even in such instances (Hair, Howard, and Nitzl 2020; Manley et al. 2021). Other authors who use CCA to confirm measurement scales also emphasise the possibility of using small samples (e.g.
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Cefis and Carpita 2024; Homer 2023). The final step culminates in developing our suggested scale, consisting of 14 distinct practices and 48 items.

# 4. Scale development

## 4.1. Step 1: defining the construct's domain and determining items

The initial step in enhancing measurement techniques entails precisely defining construct domains. To achieve this, a systematic literature review was carried out. Through this review, we identified key claims from previous studies that served as potential components for our scale. This effort yielded a preliminary list of 15 practices and 72 items. To further refine these, we engaged four doctoral-level experts in LM. They rigorously examined each practice and item, assessing them against criteria like alignment with the scale's objectives, clarity, simplicity, conciseness, and removing ambiguous or overly technical terms per the guidelines set forth by Johnson and Morgan (2016). This phase also involved carefully renaming certain practices and items to enhance precision. Following this meticulous expert analysis and semantic refinement, the scale was updated to encompass 15 practices and 66 items as summarised in Table 1.

## 4.2. Step 2 – Item validity and reliability

During our four rounds of Q-sort, 72 experts were used. Following each performed round, estimators enhanced the suitability of the proposed scale. For example, items that had three features were removed: (1) poor validity estimators, (2) lacked unanimity among experts, and (3) were redundant. After the fourth round, the reliability and validity estimators were considered acceptable for the constructs and items analysed. The estimators calculated over the four rounds performed are shown in Tables 2 and 3. At the fourth round, the interjudge agreement percentage was greater than 62 and the kappa value was greater than 0.55, exceeding values considered reasonable (Gonella et al. 2023). The average of Perreault and Leigh's Ir was greater than 0.8, the OPR exceeded 70% (0.83%), and the validity estimators were greater than 0.82 (for PSA) and 0.68 (for CSV).

Many constructs and assertions were modified along the rounds, as indicated by MacKenzie, Podsakoff, and Podsakoff (2011). The primary objective was to enhance the clarity of the statements for the judges. Specific practices underwent name changes for greater clarity, including ‘Encouraging innovation and technologies,’ ‘making job resources available,’ ‘management support,’ and ‘aligning employee’s goals with the company’s goals.’

Additionally, the ‘Promoting employee-friendly policies (P3)’ practice was eliminated, removing four associated items. Another notable revision involved merging the practices of ‘intensive use of communication’ and ‘feedback mechanisms’ into a single practice named ‘intensive use of communication and feedback mechanisms’ due to their frequent confusion. This amalgamation resulted in the exclusion of six items. While the other practices remained intact, two items were removed during the sorting rounds because of their biased phrasing concerning other items, ensuring a more coherent and unbiased assessment tool. Consequently, after completing all rounds, the structure of the proposed scale was enhanced, resulting in a final scale comprising 14 practices and 54 items.

## 4.3. Step 3 – Measures refinement, purify, and pre-test

The third step of scale development involved conducting a pre-test to assess and improve the scale's accuracy. The educational backgrounds of the 94 survey participants are characterised as follows: 56% hold higher education degrees, 41% have successfully graduated from high school, and 3% preferred not to disclose their educational level. In terms of employment duration, 4% have been with their current company for less than a year, 16% for 1 to 4 years, 22% for 4 to 7 years, 23% for 7 to 10 years, and 34% for more than 10 years, with 1% opting not to respond. The participants were primarily from textile (57.4%) and paper (35%) industries. An Exploratory Factor Analysis (EFA) was performed to analyse the data using R software, leveraging packages such as psy, psych, MASS, and mirt. Results are summarised in Table 4.

Key metrics such as and Bartlett's test (BT) of sphericity were also calculated. The Kaiser–Meyer–Olkin (KMO) test yielded a result of 0.84, along with a χ² value of 611.92. KMO values below 0.50 are deemed unacceptable, while those ranging from 0.60 to 0.90 are considered adequate (Hair et al. 2019; Williams, Onsman, and Brown 2010). For the Bartlett's test (Bartlett 1950), the *p*-value was 2.2 × 10⁻¹⁶, which is below the indicated value of 0.01 (Hair et al. 2019). In addition, the measures of sampling adequacy (MSA) exceeded 0.70 (Cerny and Kaiser 1977).

For the process of factor extraction, we use principal axis factoring (PAF) to enhance the logical consistency of each defined construct (de Winter and Dodou 2012). To simplify the analysis process, we chose the Promax oblique rotation (Hendrickson and White 1964) and selected eigenvalues that exceeded 1 (J.-O. Kim and Mueller 1978). This rotation approach assesses if there exists independence among components, thereby
<figure><img src="image_27.png" /><figcaption>**Table 1.** List of employee involvement practices.</figcaption></figure>

<table><thead><tr><td>Practices</td><td>Definition</td><td>Reference</td></tr></thead><tbody><tr><td>Ensuring Alignment of Individual Behaviors with Company Goals</td><td>The company must ensure that employees' individual goals and behaviours are strategically aligned with the overarching goals of the organisation (Gagnon, Jansen, and Michael 2008).</td><td>Gagnon, Jansen, and Michael (2008), Ferrer, Calvo, and Santa (2023), Knapić, Rusjan, and Božić (2023).</td></tr><tr><td>Continuous development of employee's leadership skills</td><td>The company should focus on developing employees' leadership skills, including communication, listening, and providing feedback. This development is crucial as a higher degree of trust in leadership fosters proactive behaviours among frontline employees, encouraging them to exercise autonomy in their daily tasks to identify and implement systematic improvements in work processes (Anand, Chhajed, and Delfin 2012)</td><td>Anand, Chhajed, and Delfin (2012), Godinho Filho, Ganga, and Gunasekaran (2016), Dellve et al. (2018), Sisson (2019), Cortés Rodríguez, Gutierrez, and Fuentes-Fuentes (2023), Kaur Paposa et al. (2023), Knapić, Rusjan, and Božić (2023).</td></tr><tr><td>Promotion of teamwork</td><td>The company should form groups where each member contributes to decision-making. This approach ensures that decisions are made more systematically, integrated, and analytically (Grigg 2010).</td><td>Grigg (2010), Gollan et al. (2014), Stadnicka and Antosz (2015), Godinho Filho, Ganga, and Gunasekaran (2016), De Vries and Van der Poll (2018), Roslin et al. (2019), Neirotti (2020), Signoretti (2020), Beraldin, Danese, and Romano (2022), Narayanan et al. (2022), Kaur Paposa et al. (2023), Knapić, Rusjan, and Božić (2023).</td></tr><tr><td>Developing multifunctional workers</td><td>The company should implement cross-training, which ensures that employees gain experience in multiple activities, preventing their skills and knowledge from being confined to a single task (Cheng 1991).</td><td>Cheng (1991), Stadnicka and Antosz (2015), Hernandez-Matias et al. (2020), Narayanan et al. (2022), Kaur Paposa et al. (2023)</td></tr><tr><td>Driving suggestion programmes</td><td>The company should create an environment that fosters employee involvement by encouraging and rewarding improvement suggestions (Roslin et al. 2019).</td><td>Gollan et al. (2014), Marksberry, Church, and Schmidt (2014), Stadnicka and Antosz (2015), Godinho Filho, Ganga, and Gunasekaran (2016), Roslin et al. (2019), Kumar and Mathiyazhagan (2020), Neirotti (2020), Beraldin, Danese, and Romano (2022), Kaur Paposa et al. (2023)</td></tr><tr><td>Education programmes, training, and personal development</td><td>The company should offer programmes that promote employee knowledge and self-development. These can include internal training, consultancy-led sessions, and ongoing education such as external courses or postgraduate studies sponsored by the company (McLean, Jiju, and Dahlgaard 2017; Stadnicka and Antosz 2015).</td><td>Stadnicka and Antosz (2015), Brkic and Tomic (2016), Shokri, Waring, and Nabhani (2016), McLean, Jiju, and Dahlgaard (2017), Rachman and Ratnayake (2019), Roslin et al. (2019), Sisson (2019), Hernandez-Matias et al. (2020), Jing et al. (2020), Kumar and Mathiyazhagan (2020), Signoretti (2020), Tran, Pham, and Bui (2020), van Assen (2021), Cortés Rodríguez, Gutierrez, and Fuentes-Fuentes (2023), Ferrer, Calvo, and Santa (2023), Kaur Paposa et al. (2023), Knapić, Rusjan, and Božić (2023).</td></tr><tr><td>Encouraging innovation and technologies</td><td>The company should use information technology systems and active methodologies as a source of knowledge to inspire the involvement of employees with lean changes (Stadnicka and Antosz 2015).</td><td>Stadnicka and Antosz (2015), Knapić, Rusjan, and Božić (2023).</td></tr><tr><td>Intensive use of communication and feedback mechanism</td><td>The company should use informal conversations and meetings to involve employees once day-to-day operations can be improved through informal discussions and meetings (Neirotti 2020).</td><td>Roslin et al. (2019), Hernandez-Matias et al. (2020), Neirotti (2020), Signoretti (2020), Tran, Pham, and Bui (2020), Kaur Paposa et al. (2023), Knapić, Rusjan, and Božić (2023).</td></tr><tr><td>Making job resources available</td><td>The company should make all the resources required to perform a task available. Resources refer to the job's physical, psychological, social, or utilisation aspects (Grigg 2010)</td><td>Grigg (2010), McLean, Jiju, and Dahlgaard (2017), Stelson et al. (2017), Dellve et al. (2018).</td></tr><tr><td>Management support</td><td>The company should guarantee that management supports workers in performing improvement tasks (Bergquist and Westerberg 2014).</td><td>Bhattacharya (2015), Roslin et al. (2019), Dellve et al. (2018), Rachman and Ratnayake (2019), Hernandez-Matias et al. (2020), Signoretti (2020), Tran, Pham, and Bui (2020), González-Boubeta, Portela-Caramés, and Prado-Prado (2021), Tezel, Koskela, and Tzortopoulos (2023), Mathiyazhagan, Gnanavelbabu, and Agarwal (2022), Ferrer, Calvo, and Santa (2023).</td></tr><tr><td>Promoting employee-friendly policies</td><td>The company should Promoting employee-friendly policies such as an enabling environment for employees who want to participate and are highly involved in lean-centered activities through interpersonal relationships, improved quality of life at work, and balance between work and personal life (Roslin et al. 2019).</td><td>Beraldin, Danese, and Romano (2019), Roslin et al. (2019), Malik and Abdallah (2020), Ferrer, Calvo, and Santa (2023).</td></tr><tr><td>Promoting empowerment and autonomy to employees</td><td>The company should empower employees to perform and operate correctly because liberty allows them to use creative decision-making and managing objectives to improve processes (Roslin et al. 2019).</td><td>Bergquist and Westerberg (2014), Marin-Garcia and Bonavia (2015), Stadnicka and Antosz (2015), Roslin et al. (2019), van Assen and de Mast (2019), Narayanan et al. (2022), Kaur Paposa et al. (2023), Knapić, Rusjan, and Božić (2023).</td></tr></tbody></table>

(continued).
<figure><img src="image_28.png" /><figcaption>**Table 1. Continued.**</figcaption></figure>

<table><thead><tr><th>Practices</th><th>Definition</th><th>Reference</th></tr></thead><tbody><tr><td>Providing career advancement opportunities to employees</td><td>The company should provide career opportunities to employees for their future growth in the utilisation based on their merits and performances (Bhattacharya 2015).</td><td>Bhattacharya (2015), Ferrer, Calvo, and Santa (2023).</td></tr><tr><td>Using Kaizen events</td><td>The company should use Kaizen events to solve problems regularly and increase workers' participation in making improvements. Impressed by the outcome, the employees strive to keep the event alive through the daily Kaizen methodology, increasing future employee participation (Vo, Kongar, and Suárez Barraza 2019).</td><td>Rachman and Ratnayake (2019), Vo, Kongar, and Suárez Barraza (2019), Kumar and Mathiyazhagan (2020).</td></tr><tr><td>Using reward-based incentives</td><td>The company should provide rewards to workers, beyond fixed remuneration like salary and annual raises, in the form of variable pay tied to performance. This approach fosters increased collective efforts, professionalism, and flexibility among employees (Wickramasinghe and Wickramasinghe 2016).</td><td>Brkic and Tomic (2016), Wickramasinghe and Wickramasinghe (2016), McLean, Jiju, and Dahlgaard (2017), Ferrer, Calvo, and Santa (2023).</td></tr></tbody></table>

Source: Elaborated by the authors.

<table><caption>**Table 2.** Comparison of interrater reliability and validity estimators.</caption><thead><tr><th>Reliability and Validity Estimators</th><th>First Round<sup>a</sup></th><th>Second Round</th><th>Third Round</th><th>Fourth Round</th></tr></thead><tbody><tr><td>Interjudge agreement percentage (%)</td><td>46-69</td><td>68-73</td><td>68-75</td><td>62-70</td></tr><tr><td>Cohen's k</td><td>0.13-0.45</td><td>0.51-0.56</td><td>0.55-0.58</td><td>0.55-0.64</td></tr><tr><td>Perreault and Leigh's Ir</td><td>0.60-0.80</td><td>0.78-0.82</td><td>0.79-0.84</td><td>0.80-0.86</td></tr><tr><td>Proportion of substantive validity (p<sub>sa</sub>)</td><td>0.70</td><td>0.79</td><td>0.80</td><td>0.82</td></tr><tr><td>Coefficient of substantive validity (c<sub>sv</sub>)</td><td>0.30</td><td>0.44</td><td>0.52</td><td>0.68</td></tr><tr><td>Overall Placement Ratio (OPR)</td><td>68%</td><td>76%</td><td>83%</td><td>83%</td></tr></tbody></table>

Source: Elaborated by the authors.

<table><caption>**Table 3.** Overall Placement Ratios for each practice.</caption><thead><tr><th>Practice</th><th>First Round</th><th>Second Round</th><th>Third Round</th><th>Fourth Round</th></tr></thead><tbody><tr><td>Using Kaizen events</td><td>60%</td><td>80%</td><td>78%</td><td>82%</td></tr><tr><td>Driving suggestion programmes</td><td>70%</td><td>88%</td><td>77%</td><td>79%</td></tr><tr><td>Promotion of teamwork</td><td>31%</td><td>47%</td><td>76%</td><td>79%</td></tr><tr><td>Ensuring alignment of individual behaviours with company goals</td><td>51%</td><td>77%</td><td>80%</td><td>82%</td></tr><tr><td>Intensive use of communication and feedback mechanisms</td><td>70%</td><td>85%</td><td>92%</td><td>96%</td></tr><tr><td>Empowerment (job autonomy)</td><td>96%</td><td>96%</td><td>91%</td><td>91%</td></tr><tr><td>Education programmes, training, and personal development</td><td>70%</td><td>78%</td><td>78%</td><td>94%</td></tr><tr><td>Promoting employee-friendly policies</td><td>66%</td><td>66%</td><td>68%</td><td>71%</td></tr><tr><td>Using reward-based incentives</td><td>70%</td><td>76%</td><td>76%</td><td>77%</td></tr><tr><td>Providing career advancement opportunities to employees</td><td>77%</td><td>70%</td><td>84%</td><td>92%</td></tr><tr><td>Multifunctional employee (job rotation)</td><td>73%</td><td>79%</td><td>80%</td><td>85%</td></tr><tr><td>Making job resources available</td><td>70%</td><td>75%</td><td>75%</td><td>77%</td></tr><tr><td>Management support</td><td>70%</td><td>72%</td><td>75%</td><td>77%</td></tr><tr><td>Continuous development of employee's leadership skills</td><td>79%</td><td>82%</td><td>83%</td><td>85%</td></tr><tr><td>Encouraging innovation and technologies</td><td>69%</td><td>72%</td><td>74%</td><td>80%</td></tr><tr><td><strong>Average</strong></td><td>68%</td><td>76%</td><td>78%</td><td>82%</td></tr></tbody></table>

Source: Elaborated by the authors.

enhancing the interpretability of results (Reise, Waller, and Comrey 2000).

In general, a communality (h2) value of at least 0.5 is deemed adequate (Hair et al. 2019). Using a conservative approach, we also kept items close to values of 0.5, and items considered essential for employee involvement in LM with inadequate values. Thus, even though 'P13.2' (I often feel satisfied with achievements achieved by the work team of which I am part) and 'P15.3' (I agree that my company invests in new technologies, such as comprehensive modelling tools and specialty software, in

LM efforts) had communalities equal to 0.29, they were maintained.

The threshold for the factorial load to be used is influenced by the size of the sample, with larger sample sizes allowing for lower factorial loads (Hair et al. 2019). Accordingly, items that should be removed are those with loading values below 0.50 (as a threshold), cross-loadings exceeding 0.50 on the same factor, and communalities under 0.50 (Costello and Osborne 2005). Maintaining a conservative approach, we kept loadings at approximately 0.3. The analysis yielded a diagonal matrix with values

<footnote><sup>a</sup> Independent samples of n judges per sorting round: round 1, n = 18; round 2, n = 18; round 3, n = 18; round 4, n = 18</footnote>
<figure><img src="image_29.png" /><figcaption>**Table 4.** Cross-loadings and Exploratory Factor Analysis (EFA).</figcaption></figure>

<table><thead><tr><th rowspan="2">Items</th><th rowspan="2">P1</th><th rowspan="2">P10</th><th rowspan="2">P11</th><th rowspan="2">P12</th><th rowspan="2">P13</th><th rowspan="2">P14</th><th rowspan="2">P15</th><th rowspan="2">P2</th><th rowspan="2">P4</th><th rowspan="2">P5</th><th rowspan="2">P6</th><th rowspan="2">P7</th><th rowspan="2">P8</th><th rowspan="2">P9</th><th colspan="3">EFA</th></tr><tr><th>Factor 1</th><th>Factor 2</th><th>h2</th></tr></thead><tbody><tr><td>P1.1</td><td><strong>0.825</strong></td><td>0.419</td><td>0.378</td><td>0.472</td><td>0.536</td><td>0.483</td><td>0.473</td><td>0.439</td><td>0.470</td><td>0.538</td><td>0.323</td><td>0.470</td><td>0.441</td><td>0.419</td><td>0.550</td><td>0.330</td><td>0.570</td></tr><tr><td>P1.2</td><td><strong>0.839</strong></td><td>0.371</td><td>0.431</td><td>0.483</td><td>0.424</td><td>0.405</td><td>0.366</td><td>0.396</td><td>0.476</td><td>0.454</td><td>0.331</td><td>0.425</td><td>0.465</td><td>0.365</td><td>0.440</td><td>0.400</td><td>0.500</td></tr><tr><td>P1.3</td><td><strong>0.731</strong></td><td>0.274</td><td>0.460</td><td>0.554</td><td>0.462</td><td>0.525</td><td>0.354</td><td>0.493</td><td>0.422</td><td>0.541</td><td>0.237</td><td>0.251</td><td>0.426</td><td>0.456</td><td>0.710</td><td></td><td>0.560</td></tr><tr><td>P1.4</td><td><strong>0.663</strong></td><td>0.423</td><td>0.330</td><td>0.354</td><td>0.332</td><td>0.274</td><td>0.156</td><td>0.275</td><td>0.361</td><td>0.335</td><td>0.440</td><td>0.444</td><td>0.346</td><td>0.204</td><td></td><td>0.650</td><td>0.470</td></tr><tr><td>P1.5**</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.750</td><td>0.470</td></tr><tr><td>P10.1</td><td>0.379</td><td><strong>0.808</strong></td><td>0.292</td><td>0.476</td><td>0.421</td><td>0.444</td><td>0.432</td><td>0.449</td><td>0.438</td><td>0.533</td><td>0.333</td><td>0.390</td><td>0.318</td><td>0.426</td><td>0.500</td><td>0.320</td><td>0.490</td></tr><tr><td>P10.2</td><td>0.391</td><td><strong>0.833</strong></td><td>0.283</td><td>0.520</td><td>0.436</td><td>0.392</td><td>0.481</td><td>0.509</td><td>0.391</td><td>0.440</td><td>0.352</td><td>0.436</td><td>0.423</td><td>0.495</td><td>0.500</td><td>0.400</td><td>0.580</td></tr><tr><td>P10.3</td><td>0.299</td><td><strong>0.568</strong></td><td>0.119</td><td>0.241</td><td>0.201</td><td>0.149</td><td>0.194</td><td>0.117</td><td>0.294</td><td>0.174</td><td>0.431</td><td>0.531</td><td>0.216</td><td>0.211</td><td></td><td>0.760</td><td>0.490</td></tr><tr><td>P11.1</td><td>0.487</td><td>0.352</td><td><strong>0.840</strong></td><td>0.406</td><td>0.476</td><td>0.398</td><td>0.372</td><td>0.436</td><td>0.372</td><td>0.433</td><td>0.255</td><td>0.348</td><td>0.495</td><td>0.442</td><td>0.360</td><td>0.400</td><td>0.410</td></tr><tr><td>P11.2</td><td>0.429</td><td>0.223</td><td><strong>0.835</strong></td><td>0.445</td><td>0.491</td><td>0.448</td><td>0.330</td><td>0.324</td><td>0.374</td><td>0.534</td><td>0.283</td><td>0.201</td><td>0.462</td><td>0.373</td><td>0.530</td><td></td><td>0.380</td></tr><tr><td>P11.3</td><td>0.262</td><td>0.155</td><td><strong>0.644</strong></td><td>0.188</td><td>0.308</td><td>0.086</td><td>0.056</td><td>0.077</td><td>0.273</td><td>0.214</td><td>0.384</td><td>0.322</td><td>0.318</td><td>0.130</td><td></td><td>0.720</td><td>0.440</td></tr><tr><td>P12.1</td><td>0.495</td><td>0.397</td><td>0.356</td><td><strong>0.721</strong></td><td>0.451</td><td>0.529</td><td>0.434</td><td>0.455</td><td>0.359</td><td>0.525</td><td>0.230</td><td>0.374</td><td>0.323</td><td>0.534</td><td>0.600</td><td></td><td>0.520</td></tr><tr><td>P12.2</td><td>0.541</td><td>0.448</td><td>0.403</td><td><strong>0.826</strong></td><td>0.494</td><td>0.589</td><td>0.476</td><td>0.476</td><td>0.562</td><td>0.604</td><td>0.276</td><td>0.378</td><td>0.503</td><td>0.541</td><td>0.750</td><td></td><td>0.670</td></tr><tr><td>P12.3</td><td>0.459</td><td>0.465</td><td>0.386</td><td><strong>0.736</strong></td><td>0.437</td><td>0.350</td><td>0.359</td><td>0.331</td><td>0.488</td><td>0.436</td><td>0.432</td><td>0.560</td><td>0.537</td><td>0.404</td><td>0.310</td><td>0.580</td><td>0.580</td></tr><tr><td>P12.4</td><td>0.218</td><td>0.342</td><td>0.210</td><td><strong>0.553</strong></td><td>0.227</td><td>0.159</td><td>0.107</td><td>0.234</td><td>0.275</td><td>0.189</td><td>0.383</td><td>0.340</td><td>0.385</td><td>0.266</td><td></td><td>0.630</td><td>0.400</td></tr><tr><td>P12.5</td><td>0.438</td><td>0.436</td><td>0.316</td><td><strong>0.761</strong></td><td>0.418</td><td>0.591</td><td>0.476</td><td>0.555</td><td>0.478</td><td>0.570</td><td>0.271</td><td>0.312</td><td>0.450</td><td>0.513</td><td>0.750</td><td></td><td>0.620</td></tr><tr><td>P13.1</td><td>0.452</td><td>0.398</td><td>0.485</td><td>0.519</td><td><strong>0.871</strong></td><td>0.472</td><td>0.460</td><td>0.468</td><td>0.329</td><td>0.564</td><td>0.199</td><td>0.306</td><td>0.532</td><td>0.435</td><td>0.780</td><td></td><td>0.640</td></tr><tr><td>P13.2</td><td>0.353</td><td>0.388</td><td>0.284</td><td>0.280</td><td><strong>0.484</strong></td><td>0.175</td><td>0.200</td><td>0.216</td><td>0.305</td><td>0.235</td><td>0.355</td><td>0.369</td><td>0.293</td><td>0.122</td><td></td><td>0.560</td><td>0.290</td></tr><tr><td>P13.3</td><td>0.510</td><td>0.369</td><td>0.479</td><td>0.476</td><td><strong>0.867</strong></td><td>0.462</td><td>0.471</td><td>0.530</td><td>0.283</td><td>0.536</td><td>0.163</td><td>0.240</td><td>0.496</td><td>0.497</td><td>0.800</td><td></td><td>0.640</td></tr><tr><td>P14.1</td><td>0.362</td><td>0.364</td><td>0.332</td><td>0.430</td><td>0.408</td><td><strong>0.773</strong></td><td>0.480</td><td>0.517</td><td>0.288</td><td>0.503</td><td>0.129</td><td>0.198</td><td>0.236</td><td>0.582</td><td>0.910*</td><td></td><td>0.690</td></tr><tr><td>P14.2</td><td>0.547</td><td>0.488</td><td>0.419</td><td>0.580</td><td>0.460</td><td><strong>0.862</strong></td><td>0.546</td><td>0.542</td><td>0.628</td><td>0.558</td><td>0.282</td><td>0.384</td><td>0.444</td><td>0.579</td><td>0.790</td><td></td><td>0.660</td></tr><tr><td>P14.3</td><td>0.457</td><td>0.290</td><td>0.329</td><td>0.566</td><td>0.396</td><td><strong>0.837</strong></td><td>0.490</td><td>0.489</td><td>0.493</td><td>0.518</td><td>0.120</td><td>0.267</td><td>0.377</td><td>0.594</td><td>0.910*</td><td></td><td>0.710</td></tr><tr><td>P15.1</td><td>0.374</td><td>0.376</td><td>0.221</td><td>0.404</td><td>0.390</td><td>0.529</td><td><strong>0.847</strong></td><td>0.567</td><td>0.391</td><td>0.454</td><td>0.119</td><td>0.249</td><td>0.319</td><td>0.595</td><td>0.850</td><td></td><td>0.590</td></tr><tr><td>P15.2</td><td>0.380</td><td>0.415</td><td>0.326</td><td>0.424</td><td>0.331</td><td>0.284</td><td><strong>0.675</strong></td><td>0.326</td><td>0.478</td><td>0.394</td><td>0.493</td><td>0.487</td><td>0.474</td><td>0.396</td><td>0.330</td><td>0.520</td><td>0.520</td></tr><tr><td>P15.3</td><td>0.206</td><td>0.322</td><td>0.247</td><td>0.233</td><td>0.302</td><td>0.389</td><td><strong>0.674</strong></td><td>0.385</td><td>0.243</td><td>0.226</td><td>0.111</td><td>0.154</td><td>0.163</td><td>0.426</td><td>0.590</td><td></td><td>0.290</td></tr><tr><td>P15.4</td><td>0.361</td><td>0.427</td><td>0.283</td><td>0.498</td><td>0.501</td><td>0.612</td><td><strong>0.781</strong></td><td>0.631</td><td>0.355</td><td>0.428</td><td>0.049</td><td>0.161</td><td>0.292</td><td>0.636</td><td>0.950*</td><td>-0.450</td><td>0.750</td></tr><tr><td>P2.1</td><td>0.418</td><td>0.412</td><td>0.336</td><td>0.432</td><td>0.315</td><td>0.245</td><td>0.320</td><td><strong>0.711</strong></td><td>0.303</td><td>0.271</td><td>0.453</td><td>0.416</td><td>0.407</td><td>0.382</td><td></td><td>0.620</td><td>0.480</td></tr></tbody></table>

(continued).
<figure><img src="image_30.png" /><figcaption>**Table 4. Continued.**</figcaption></figure>

<table><thead><tr><th rowspan="2">Items</th><th rowspan="2">P1</th><th rowspan="2">P10</th><th rowspan="2">P11</th><th rowspan="2">P12</th><th rowspan="2">P13</th><th rowspan="2">P14</th><th rowspan="2">P15</th><th rowspan="2">P2</th><th rowspan="2">P4</th><th rowspan="2">P5</th><th rowspan="2">P6</th><th rowspan="2">P7</th><th rowspan="2">P8</th><th rowspan="2">P9</th><th colspan="3">EFA</th></tr><tr><th>Factor 1</th><th>Factor 2</th><th>h2</th></tr></thead><tbody><tr><td>P2.2**</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.700</td><td>0.410</td></tr><tr><td>P2.3**</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.750</td><td>0.500</td></tr><tr><td>P2.4</td><td>0.392</td><td>0.385</td><td>0.272</td><td>0.452</td><td>0.515</td><td>0.671</td><td>0.642</td><td><strong>0.805</strong></td><td>0.351</td><td>0.567</td><td>0.031</td><td>0.140</td><td>0.293</td><td>0.578</td><td>1010*</td><td>-0.450</td><td>0.830</td></tr><tr><td>P4.1</td><td>0.342</td><td>0.465</td><td>0.338</td><td>0.400</td><td>0.226</td><td>0.227</td><td>0.220</td><td>0.228</td><td><strong>0.638</strong></td><td>0.271</td><td>0.516</td><td>0.540</td><td>0.417</td><td>0.189</td><td></td><td>0.790</td><td>0.610</td></tr><tr><td>P4.2</td><td>0.460</td><td>0.255</td><td>0.324</td><td>0.432</td><td>0.340</td><td>0.513</td><td>0.424</td><td>0.320</td><td><strong>0.748</strong></td><td>0.472</td><td>0.272</td><td>0.406</td><td>0.453</td><td>0.406</td><td>0.600</td><td></td><td>0.490</td></tr><tr><td>P4.3</td><td>0.429</td><td>0.409</td><td>0.310</td><td>0.500</td><td>0.285</td><td>0.509</td><td>0.434</td><td>0.385</td><td><strong>0.793</strong></td><td>0.427</td><td>0.203</td><td>0.337</td><td>0.374</td><td>0.410</td><td>0.620</td><td></td><td>0.590</td></tr><tr><td>P5.1</td><td>0.355</td><td>0.382</td><td>0.325</td><td>0.367</td><td>0.418</td><td>0.367</td><td>0.435</td><td>0.429</td><td>0.328</td><td><strong>0.678</strong></td><td>0.231</td><td>0.312</td><td>0.325</td><td>0.388</td><td>0.500</td><td></td><td>0.390</td></tr><tr><td>P5.2</td><td>0.506</td><td>0.455</td><td>0.414</td><td>0.568</td><td>0.507</td><td>0.617</td><td>0.526</td><td>0.540</td><td><strong>0.834</strong></td><td>0.143</td><td>0.246</td><td>0.413</td><td>0.605</td><td>0.880</td><td></td><td>0.700</td><td>0.610</td></tr><tr><td>P5.3</td><td>0.533</td><td>0.402</td><td>0.432</td><td>0.540</td><td>0.507</td><td>0.555</td><td>0.399</td><td>0.434</td><td>0.448</td><td><strong>0.840</strong></td><td>0.274</td><td>0.314</td><td>0.458</td><td>0.471</td><td>0.750</td><td></td><td>0.610</td></tr><tr><td>P5.4</td><td>0.382</td><td>0.320</td><td>0.403</td><td>0.397</td><td>0.349</td><td>0.297</td><td>0.182</td><td>0.259</td><td>0.317</td><td><strong>0.567</strong></td><td>0.300</td><td>0.314</td><td>0.315</td><td>0.220</td><td></td><td>0.410</td><td>0.360</td></tr><tr><td>P5.5</td><td>0.494</td><td>0.452</td><td>0.418</td><td>0.581</td><td>0.472</td><td>0.480</td><td>0.343</td><td>0.414</td><td>0.451</td><td><strong>0.765</strong></td><td>0.374</td><td>0.384</td><td>0.426</td><td>0.432</td><td>0.550</td><td></td><td>0.530</td></tr><tr><td>P6.1</td><td>0.404</td><td>0.384</td><td>0.356</td><td>0.375</td><td>0.276</td><td>0.234</td><td>0.302</td><td>0.332</td><td>0.347</td><td>0.346</td><td><strong>0.813</strong></td><td>0.480</td><td>0.372</td><td>0.347</td><td></td><td>0.640</td><td>0.440</td></tr><tr><td>P6.2</td><td>0.314</td><td>0.284</td><td>0.247</td><td>0.253</td><td>0.218</td><td>0.030</td><td>0.112</td><td>0.189</td><td>0.274</td><td>0.154</td><td><strong>0.781</strong></td><td>0.425</td><td>0.436</td><td>0.097</td><td></td><td>0.850</td><td>0.610</td></tr><tr><td>P6.3</td><td>0.291</td><td>0.379</td><td>0.299</td><td>0.333</td><td>0.201</td><td>0.218</td><td>0.213</td><td>0.170</td><td>0.329</td><td>0.229</td><td><strong>0.714</strong></td><td>0.379</td><td>0.355</td><td>0.186</td><td></td><td>0.730</td><td>0.480</td></tr><tr><td>P6.4</td><td>0.296</td><td>0.421</td><td>0.234</td><td>0.340</td><td>0.185</td><td>0.177</td><td>0.161</td><td>0.199</td><td>0.400</td><td>0.322</td><td><strong>0.814</strong></td><td>0.474</td><td>0.402</td><td>0.152</td><td></td><td>0.850</td><td>0.630</td></tr><tr><td>P7.1</td><td>0.403</td><td>0.552</td><td>0.304</td><td>0.460</td><td>0.305</td><td>0.254</td><td>0.290</td><td>0.275</td><td>0.410</td><td>0.306</td><td>0.519</td><td><strong>0.795</strong></td><td>0.419</td><td>0.279</td><td></td><td>0.790</td><td>0.660</td></tr><tr><td>P7.2</td><td>0.390</td><td>0.418</td><td>0.258</td><td>0.317</td><td>0.270</td><td>0.271</td><td>0.273</td><td>0.260</td><td>0.453</td><td>0.309</td><td>0.374</td><td><strong>0.729</strong></td><td>0.328</td><td>0.242</td><td></td><td>0.580</td><td>0.390</td></tr><tr><td>P7.3</td><td>0.379</td><td>0.338</td><td>0.256</td><td>0.440</td><td>0.289</td><td>0.279</td><td>0.258</td><td>0.262</td><td>0.454</td><td>0.337</td><td>0.391</td><td><strong>0.756</strong></td><td>0.420</td><td>0.325</td><td></td><td>0.680</td><td>0.600</td></tr><tr><td>P8.1**</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.710</td><td>0.430</td></tr><tr><td>P8.2**</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.520</td><td>0.810</td><td>0.570</td></tr><tr><td>P8.3</td><td>0.389</td><td>0.438</td><td>0.311</td><td>0.524</td><td>0.373</td><td>0.395</td><td>0.401</td><td>0.384</td><td>0.463</td><td>0.388</td><td>0.400</td><td>0.381</td><td><strong>0.764</strong></td><td>0.351</td><td>0.430</td><td></td><td>0.340</td><td>0.430</td></tr><tr><td>P8.4**</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.430</td><td>0.700</td><td>0.420</td></tr><tr><td>P8.5</td><td>0.324</td><td>0.242</td><td>0.423</td><td>0.388</td><td>0.426</td><td>0.214</td><td>0.241</td><td>0.223</td><td>0.385</td><td>0.246</td><td>0.344</td><td>0.375</td><td><strong>0.708</strong></td><td>0.279</td><td></td><td>0.590</td><td>0.420</td></tr><tr><td>P8.6</td><td>0.486</td><td>0.272</td><td>0.502</td><td>0.409</td><td>0.509</td><td>0.330</td><td>0.292</td><td>0.370</td><td>0.392</td><td>0.499</td><td>0.345</td><td>0.377</td><td><strong>0.726</strong></td><td>0.250</td><td>0.400</td><td></td><td>0.430</td><td>0.490</td></tr><tr><td>P9.1</td><td>0.338</td><td>0.457</td><td>0.293</td><td>0.511</td><td>0.386</td><td>0.541</td><td>0.561</td><td>0.530</td><td>0.380</td><td>0.453</td><td>0.179</td><td>0.321</td><td>0.315</td><td><strong>0.837</strong></td><td>0.730</td><td></td><td>0.490</td><td>0.780</td></tr><tr><td>P9.2</td><td>0.424</td><td>0.405</td><td>0.378</td><td>0.556</td><td>0.448</td><td>0.671</td><td>0.622</td><td>0.588</td><td>0.395</td><td>0.529</td><td>0.117</td><td>0.220</td><td>0.310</td><td><strong>0.846</strong></td><td>0.960*</td><td></td><td>0.600</td><td>0.600</td></tr><tr><td>P9.3</td><td>0.458</td><td>0.486</td><td>0.433</td><td>0.557</td><td>0.425</td><td>0.585</td><td>0.588</td><td>0.515</td><td>0.420</td><td>0.506</td><td>0.367</td><td>0.402</td><td>0.393</td><td><strong>0.863</strong></td><td>0.690</td><td></td><td>0.690</td><td>0.690</td></tr><tr><td>Percentage of variance explained</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>29.0</td><td></td><td>25.0</td><td></td></tr><tr><td>Cumulative explained variance percentage</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>29.0</td><td></td><td>54.0</td><td></td></tr></tbody></table>

Note(s): * Item with load exceeding 0.9; ** Items to delete in step 4 due to low-loading
<figure><img src="image_31.png" /></figure>

exceeding 0.5, suggesting the adequacy of our sample. With a focus on refining the model by evaluating individual items, and following the guidelines proposed by Watts et al. (2020), items were considered for exclusion if they had loadings lower than 0.30 and cross-loadings higher than 0.50 on the same factor. Nevertheless, it is essential to acknowledge that the literature advises exercising prudence when removing items during this EFA analysis. It is crucial to consider the item's content relevance, even if the loadings in the factor extraction phase are unsuitable. Finally, the explained variance used to quantify the model's reliability (Smedslund, Arnulf, and Smedslund 2022) identified that employee involvement in LM presented a total variance of 54.00%, with Factor 1 accounting for 29.00% and Factor 2 accounting for 25.00%.

### 4.4. Step 4 – Constructs and measures validation

The fourth step focuses on scale validation. The educational backgrounds of the 176 survey participants are characterised as follows: 57% of the respondents had completed higher education, 36% had finished high school, and 7% chose not to disclose their educational level. Regarding tenure at their respective companies, 6% had been employed for less than one year, 18% for 1 to 4 years, 25% for 4 to 7 years, 17% for 7 to 10 years, and 30% for over ten years, while 4% preferred not to specify their duration of employment. Participants represented various industrial sectors, being electronics (31.7%) and textile (25.6%) the most prominent, followed by chemical (14.2%), paper (14.6%), and automotive (13.7%) industries. The SmartPLS 3 software, employing the Partial Least Squares (PLS) method, was utilised to determine the reliability and validity of the proposed scale, adhering to the recommendations by Hair, Howard, and Nitzl (2020) for conducting the CCA with PLS-SEM.

Composite Reliability (CR) was employed to assess the internal consistency among items in each construct, with CR values above 0.7 confirming this consistency. Convergent validity was established when the Average Variance Extracted (AVE) exceeded 0.5. The AVE value relates to the loading of each construct's item, which should be at least 0.7, indicating that the latent variable (construct) significantly explains the variance of each indicator. Loadings between 0.40 and 0.69 were considered for potential removal from the model. Removing a low-loading indicator typically increases CR and AVE (Hair et al. 2014). Discriminant validity among constructs was assessed through cross-loading analysis and the Fornell-Larcker and HTMT criteria. The Fornell-Larcker criterion demonstrates discriminant validity for the first-order constructs. According to this criterion, the

square root of the AVE value, which is found on the diagonal of the table of values, should be higher than the correlations with the horizontal and vertical constructs (Hair, Howard, and Nitzl 2020). Meanwhile, we obtained values for the HTMT ratios less than the 0.85 thresholds based on the multitrait-multimethod (MTMM) matrix. Table 5 shows the results of this analysis.

Numerous iterations were conducted during the assessment of the measurement model to ensure satisfactory reliability and validity. Within each iteration, variables were removed to enhance the constructs' reliability and validity. Items P1.5, P2.2, P2.3, P8.1, P8.2, and P8.3, exhibiting low factor loadings, were excluded. Following these exclusions, the AVE values for all practices exceeded 0.500, confirming convergent validity. The significance of the items, with loading values ranging from 0.482 to 0.87, was evaluated at *p* < 0.01 using bootstrapping with 5,000 subsamples (Streukens and Leroi-Werelds 2016). Collinearity issues were absent, as indicated by the variance inflation factor (VIF) values being below 5 (Hair et al. 2019). The constructs' convergent validity and reliability were affirmed once the CR values exceeded 0.7 and AVE values surpassed 0.5, well above the minimum required thresholds. Results are summarised in Table 6.

Finally, we applied the Standardized Root Mean Square Residual (SRMR) as a key metric for model fit evaluation. This approach aligns with the methodology suggested by Henseler, Hubona, and Ray (2016), who advocate for using SRMR in PLS models to gauge model adequacy. Following the guidelines proposed by Hu and Bentler (1999), a model is considered well-fitted if it achieves standardised SRMR values below 0.08. Our model demonstrated an SRMR value of 0.048, qualifying as acceptable within these parameters. This outcome echoes the findings of Henseler, Hubona, and Ray (2016), who emphasised the importance of avoiding model specification errors. The data thus provide robust statistical evidence affirming the scale's validity. Consequently, we propose a refined scale comprising 14 practices and 48 items, as detailed in Table 7. This scale is specifically designed to accurately measure employee involvement in LM settings. Table A1 in the appendix shows the items that were excluded from the scale.

## 5. Discussion

This article's foremost contribution lies in developing and validating a scale specifically designed to measure employee involvement in LM. This advancement addresses a crucial need identified in the literature, particularly the importance of understanding employee involvement levels for implementing and sustaining LM
<figure><img src="image_32.png" /><figcaption>**Table 5.** Fornell-Larcker and HTMT criterion.</figcaption></figure>

<table><thead><tr><th></th><th>P1</th><th>P10</th><th>P11</th><th>P12</th><th>P13</th><th>P14</th><th>P15</th><th>P2</th><th>P4</th><th>P5</th><th>P6</th><th>P7</th><th>P8</th><th>P9</th></tr></thead><tbody><tr><th>P1</th><td><strong>0.768</strong></td><td>0.719*</td><td>0.697*</td><td>0.769*</td><td>0.815*</td><td>0.710*</td><td>0.573*</td><td>0.834*</td><td>0.823*</td><td>0.781*</td><td>0.553*</td><td>0.745*</td><td>0.819*</td><td>0.597*</td></tr><tr><th>P10</th><td>0.479</td><td><strong>0.746</strong></td><td>0.519*</td><td>0.818*</td><td>0.823*</td><td>0.644*</td><td>0.740*</td><td>0.803*</td><td>0.839*</td><td>0.746*</td><td>0.720*</td><td>0.838*</td><td>0.717*</td><td>0.727*</td></tr><tr><th>P11</th><td>0.523</td><td>0.327</td><td><strong>0.778</strong></td><td>0.620*</td><td>0.842*</td><td>0.545*</td><td>0.486*</td><td>0.842*</td><td>0.713*</td><td>0.695*</td><td>0.532*</td><td>0.565*</td><td>0.839*</td><td>0.538*</td></tr><tr><th>P12</th><td>0.612</td><td>0.577</td><td>0.470</td><td><strong>0.725</strong></td><td>0.811*</td><td>0.791*</td><td>0.663*</td><td>0.846*</td><td>0.815*</td><td>0.816*</td><td>0.558*</td><td>0.770*</td><td>0.809*</td><td>0.791*</td></tr><tr><th>P13</th><td>0.579</td><td>0.494</td><td>0.559</td><td>0.574</td><td><strong>0.762</strong></td><td>0.721*</td><td>0.741*</td><td>0.822*</td><td>0.700*</td><td>0.842*</td><td>0.455*</td><td>0.653*</td><td>0.815*</td><td>0.668*</td></tr><tr><th>P14</th><td>0.560</td><td>0.468</td><td>0.440</td><td>0.642</td><td>0.512</td><td><strong>0.825</strong></td><td>0.809*</td><td>0.831*</td><td>0.813*</td><td>0.804*</td><td>0.277*</td><td>0.493*</td><td>0.631*</td><td>0.809*</td></tr><tr><th>P15</th><td>0.454</td><td>0.521</td><td>0.361</td><td>0.538</td><td>0.518</td><td>0.614</td><td><strong>0.748</strong></td><td>0.804*</td><td>0.755*</td><td>0.651*</td><td>0.345*</td><td>0.515*</td><td>0.635*</td><td>0.823*</td></tr><tr><th>P2</th><td>0.530</td><td>0.521</td><td>0.396</td><td>0.581</td><td>0.556</td><td>0.625</td><td>0.649</td><td><strong>0.759</strong></td><td>0.807*</td><td>0.838*</td><td>0.724*</td><td>0.836*</td><td>0.839*</td><td>0.838*</td></tr><tr><th>P4</th><td>0.567</td><td>0.507</td><td>0.442</td><td>0.610</td><td>0.393</td><td>0.584</td><td>0.502</td><td>0.432</td><td><strong>0.729</strong></td><td>0.804*</td><td>0.681*</td><td>0.842*</td><td>0.805*</td><td>0.686*</td></tr><tr><th>P5</th><td>0.617</td><td>0.544</td><td>0.534</td><td>0.669</td><td>0.612</td><td>0.639</td><td>0.518</td><td>0.567</td><td>0.543</td><td><strong>0.744</strong></td><td>0.439*</td><td>0.601*</td><td>0.763*</td><td>0.715*</td></tr><tr><th>P6</th><td>0.423</td><td>0.475</td><td>0.369</td><td>0.423</td><td>0.285</td><td>0.222</td><td>0.263</td><td>0.294</td><td>0.436</td><td>0.348</td><td><strong>0.781</strong></td><td>0.791*</td><td>0.742*</td><td>0.318*</td></tr><tr><th>P7</th><td>0.513</td><td>0.576</td><td>0.359</td><td>0.537</td><td>0.379</td><td>0.352</td><td>0.360</td><td>0.349</td><td>0.575</td><td>0.417</td><td>0.566</td><td><strong>0.760</strong></td><td>0.841*</td><td>0.516*</td></tr><tr><th>P8</th><td>0.550</td><td>0.440</td><td>0.558</td><td>0.605</td><td>0.593</td><td>0.436</td><td>0.432</td><td>0.453</td><td>0.566</td><td>0.525</td><td>0.496</td><td>0.514</td><td><strong>0.733</strong></td><td>0.585*</td></tr><tr><th>P9</th><td>0.482</td><td>0.530</td><td>0.437</td><td>0.639</td><td>0.495</td><td>0.706</td><td>0.696</td><td>0.641</td><td>0.470</td><td>0.586</td><td>0.265</td><td>0.372</td><td>0.401</td><td><strong>0.849</strong></td></tr></tbody></table>

Note(s): Elements in bold and on the diagonal, as well as those below the diagonal, represent the Fornell-Larcker values. The values above the diagonal indicate the HTMT ratios.

Source: elaborated by the authors.

practices, as highlighted by van Assen (2021). Previous research has identified the importance of employee involvement for LM implementation (Marin-Garcia and Bonavia 2015) and factors or practices that drive employee involvement in LM efforts (Hernandez-Matias et al. 2020; van Assen 2021). However, to the best of our knowledge, in the LM literature, there was still no proposed metric to assess employee involvement in LM efforts effectively. The proposed scale holds consequently significant potential for future research, enabling more precise tracking of operator involvement in LM, particularly within companies actively implementing LM. The scale is vital for organisations to evaluate and improve their employee involvement initiatives. Its utility in academic and practical contexts marks a significant advancement in the study and application of LM principles. We next reflect on the scale development, before we outline implications for research and practice.

## 5.1. Reflection on the development process

The validation process began with a broad set of 15 practices and 72 items, which were carefully refined to a final scale of 14 practices and 48 items. This condensed scale is more practical, potentially reducing respondent burden and enhancing response rates. While more concise, the final scale retains robustness, validity, and reliability, making it a user-friendly and effective tool for measuring employee involvement in LM. While creating a more concise scale, a primary focus was on maintaining its validity and reliability. Refining the scale eliminated redundancy within the initial set of items. Many items were found to measure the same underlying constructs or concepts, reflecting their prevalent representation in the literature. We streamlined the scale by removing these redundant items, enhancing its brevity while capturing each construct's essential aspects.

Regarding the practice of 'Ensuring alignment of individual behaviors with company goals' (P1), we sought to assess whether the employees' goals were aligned with the company's goals and whether these goals are well understood and accepted by employees (Gagnon, Jansen, and Michael 2008). For this practice, one item was not confirmed during the validation process – P1.5, which refers to 'I am often able to achieve the objectives set by my managers about my work'. This item was possibly not confirmed due to its similarity with item P1.2, 'I consider that my individual goals are aligned with the objectives of the next task/activity/process (internal customer view),' in which both items deal with the operator's objectives attendance.

Regarding the 'Making job resources available' (P2) practice, the scale items seek to measure, from the perspective of the operators, whether the company provides financial and non-financial resources such as time and dedicated rooms for improvement teams, using skills and encouraging participation in a non-arbitrary way (Delive et al. 2018). For this practice, two items were not confirmed during the validation process: P2.2 'I often have access to a dedicated room for meetings and dedicated spaces for teams to post communications' and P2.3 'I agree that my company seeks to use top talent in LM efforts. In addition, participation is always encouraged, but not required'. Item P2.2 was possibly not confirmed due to the practice referring to the availability of resources and the item being directed to the company's infrastructure. Item P2.3 was possibly not guaranteed due to its similarity with item P7.2: 'I agree that my company is concerned with continuously monitoring employee leadership skills and takes actions to improve these skills,' referring to the practice 'Continuous development of employee's leadership skills' in which both emphasise the development and encouragement of operators' skills.
<figure><img src="image_33.png" /><figcaption>**Table 6.** Outer loadings and VIF values.</figcaption></figure>

<table><thead><tr><th></th><th>B</th><th>X</th><th>sd</th><th>t-test</th><th>p-value</th><th>CR</th><th>AVE</th><th>VIF</th></tr></thead><tbody><tr><td><strong>P2</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.797</td><td>0.663</td><td></td></tr><tr><td>P2.1</td><td>0.711</td><td>0.708</td><td>0.060</td><td>11.805</td><td>0.000</td><td></td><td></td><td>2.222</td></tr><tr><td>P2.4</td><td>0.805</td><td>0.804</td><td>0.038</td><td>20.952</td><td>0.000</td><td></td><td></td><td>1.025</td></tr><tr><td><strong>P4</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.772</td><td>0.532</td><td></td></tr><tr><td>P4.1</td><td>0.638</td><td>0.634</td><td>0.067</td><td>9.485</td><td>0.000</td><td></td><td></td><td>1.109</td></tr><tr><td>P4.2</td><td>0.748</td><td>0.747</td><td>0.050</td><td>15.113</td><td>0.000</td><td></td><td></td><td>1.188</td></tr><tr><td>P4.3</td><td>0.793</td><td>0.793</td><td>0.041</td><td>19.373</td><td>0.000</td><td></td><td></td><td>1.262</td></tr><tr><td><strong>P5</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.859</td><td>0.553</td><td></td></tr><tr><td>P5.1</td><td>0.678</td><td>0.679</td><td>0.040</td><td>17.063</td><td>0.000</td><td></td><td></td><td>1.378</td></tr><tr><td>P5.2</td><td>0.834</td><td>0.833</td><td>0.023</td><td>35.558</td><td>0.000</td><td></td><td></td><td>2.012</td></tr><tr><td>P5.3</td><td>0.840</td><td>0.838</td><td>0.028</td><td>30.161</td><td>0.000</td><td></td><td></td><td>2.245</td></tr><tr><td>P5.4</td><td>0.567</td><td>0.565</td><td>0.063</td><td>8.940</td><td>0.000</td><td></td><td></td><td>1.228</td></tr><tr><td>P5.5</td><td>0.765</td><td>0.764</td><td>0.037</td><td>20.851</td><td>0.000</td><td></td><td></td><td>1.695</td></tr><tr><td><strong>P6</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.862</td><td>0.611</td><td></td></tr><tr><td>P6.1</td><td>0.813</td><td>0.813</td><td>0.031</td><td>26.237</td><td>0.000</td><td></td><td></td><td>1.618</td></tr><tr><td>P6.2</td><td>0.781</td><td>0.777</td><td>0.044</td><td>17.875</td><td>0.000</td><td></td><td></td><td>1.722</td></tr><tr><td>P6.3</td><td>0.714</td><td>0.714</td><td>0.044</td><td>16.036</td><td>0.000</td><td></td><td></td><td>1.372</td></tr><tr><td>P6.4</td><td>0.814</td><td>0.811</td><td>0.035</td><td>23.079</td><td>0.000</td><td></td><td></td><td>1.725</td></tr><tr><td><strong>P15</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.833</td><td>0.557</td><td></td></tr><tr><td>P15.1</td><td>0.847</td><td>0.845</td><td>0.026</td><td>32.731</td><td>0.000</td><td></td><td></td><td>1.886</td></tr><tr><td>P15.2</td><td>0.675</td><td>0.677</td><td>0.042</td><td>16.074</td><td>0.000</td><td></td><td></td><td>1.23</td></tr><tr><td>P15.3</td><td>0.674</td><td>0.671</td><td>0.056</td><td>12.060</td><td>0.000</td><td></td><td></td><td>1.444</td></tr><tr><td>P15.4</td><td>0.781</td><td>0.780</td><td>0.032</td><td>24.174</td><td>0.000</td><td></td><td></td><td>1.496</td></tr><tr><td><strong>P7</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.804</td><td>0.578</td><td></td></tr><tr><td>P7.1</td><td>0.795</td><td>0.796</td><td>0.034</td><td>23.479</td><td>0.000</td><td></td><td></td><td>1.293</td></tr><tr><td>P7.2</td><td>0.729</td><td>0.726</td><td>0.060</td><td>12.220</td><td>0.000</td><td></td><td></td><td>1.222</td></tr><tr><td>P7.3</td><td>0.756</td><td>0.757</td><td>0.049</td><td>15.370</td><td>0.000</td><td></td><td></td><td>1.238</td></tr><tr><td><strong>P9</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.885</td><td>0.721</td><td></td></tr><tr><td>P9.1</td><td>0.837</td><td>0.837</td><td>0.025</td><td>34.073</td><td>0.000</td><td></td><td></td><td>1.744</td></tr><tr><td>P9.2</td><td>0.846</td><td>0.845</td><td>0.026</td><td>32.132</td><td>0.000</td><td></td><td></td><td>1.721</td></tr><tr><td>P9.3</td><td>0.863</td><td>0.863</td><td>0.022</td><td>39.061</td><td>0.000</td><td></td><td></td><td>1.778</td></tr><tr><td><strong>P10</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.788</td><td>0.558</td><td></td></tr><tr><td>P10.1</td><td>0.808</td><td>0.809</td><td>0.033</td><td>24.818</td><td>0.000</td><td></td><td></td><td>1.288</td></tr><tr><td>P10.2</td><td>0.833</td><td>0.833</td><td>0.027</td><td>30.650</td><td>0.000</td><td></td><td></td><td>1.334</td></tr><tr><td>P10.3</td><td>0.568</td><td>0.565</td><td>0.069</td><td>8.257</td><td>0.000</td><td></td><td></td><td>1.114</td></tr><tr><td><strong>P11</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.821</td><td>0.608</td><td></td></tr><tr><td>P11.1</td><td>0.840</td><td>0.841</td><td>0.026</td><td>32.271</td><td>0.000</td><td></td><td></td><td>1.39</td></tr><tr><td>P11.2</td><td>0.835</td><td>0.834</td><td>0.030</td><td>27.830</td><td>0.000</td><td></td><td></td><td>1.408</td></tr><tr><td>P11.3</td><td>0.644</td><td>0.639</td><td>0.063</td><td>10.178</td><td>0.000</td><td></td><td></td><td>1.238</td></tr><tr><td><strong>P12</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.846</td><td>0.526</td><td></td></tr><tr><td>P12.1</td><td>0.721</td><td>0.720</td><td>0.044</td><td>16.279</td><td>0.000</td><td></td><td></td><td>1.489</td></tr><tr><td>P12.2</td><td>0.826</td><td>0.826</td><td>0.028</td><td>29.671</td><td>0.000</td><td></td><td></td><td>1.991</td></tr><tr><td>P12.3</td><td>0.736</td><td>0.733</td><td>0.043</td><td>17.073</td><td>0.000</td><td></td><td></td><td>1.663</td></tr><tr><td>P12.4</td><td>0.553</td><td>0.551</td><td>0.081</td><td>6.820</td><td>0.000</td><td></td><td></td><td>1.403</td></tr><tr><td>P12.5</td><td>0.761</td><td>0.761</td><td>0.038</td><td>19.896</td><td>0.000</td><td></td><td></td><td>1.688</td></tr><tr><td><strong>P13</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.798</td><td>0.58</td><td></td></tr><tr><td>P13.1</td><td>0.871</td><td>0.870</td><td>0.025</td><td>35.248</td><td>0.000</td><td></td><td></td><td>1.748</td></tr><tr><td>P13.2</td><td>0.484</td><td>0.482</td><td>0.080</td><td>6.056</td><td>0.000</td><td></td><td></td><td>1.045</td></tr><tr><td>P13.3</td><td>0.867</td><td>0.866</td><td>0.025</td><td>35.204</td><td>0.000</td><td></td><td></td><td>1.746</td></tr><tr><td><strong>P14</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.864</td><td>0.68</td><td></td></tr><tr><td>P14.1</td><td>0.773</td><td>0.773</td><td>0.036</td><td>21.390</td><td>0.000</td><td></td><td></td><td>1.435</td></tr><tr><td>P14.2</td><td>0.862</td><td>0.862</td><td>0.021</td><td>40.786</td><td>0.000</td><td></td><td></td><td>1.649</td></tr><tr><td>P14.3</td><td>0.837</td><td>0.836</td><td>0.030</td><td>28.331</td><td>0.000</td><td></td><td></td><td>1.656</td></tr><tr><td><strong>P1</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.851</td><td>0.59</td><td></td></tr><tr><td>P1.1</td><td>0.825</td><td>0.825</td><td>0.031</td><td>26.423</td><td>0.000</td><td></td><td></td><td>1.752</td></tr><tr><td>P1.2</td><td>0.839</td><td>0.839</td><td>0.031</td><td>27.149</td><td>0.000</td><td></td><td></td><td>1.896</td></tr><tr><td>P1.3</td><td>0.731</td><td>0.729</td><td>0.041</td><td>17.829</td><td>0.000</td><td></td><td></td><td>1.352</td></tr><tr><td>P1.4</td><td>0.663</td><td>0.660</td><td>0.055</td><td>11.995</td><td>0.000</td><td></td><td></td><td>1.312</td></tr><tr><td><strong>P8</strong></td><td></td><td></td><td></td><td></td><td></td><td>0.777</td><td>0.537</td><td></td></tr><tr><td>P8.3</td><td>0.764</td><td>0.766</td><td>0.034</td><td>22.289</td><td>0.000</td><td></td><td></td><td>1.189</td></tr><tr><td>P8.4</td><td>0.708</td><td>0.703</td><td>0.058</td><td>12.182</td><td>0.000</td><td></td><td></td><td>1.205</td></tr><tr><td>P8.5</td><td>0.726</td><td>0.725</td><td>0.049</td><td>14.965</td><td>0.000</td><td></td><td></td><td>1.134</td></tr></tbody></table>

Source: Elaborated by the authors.

Regarding the 'management support' (P4) practice, the scale items seek to understand how employees perceive the support and availability of management to help with matters related to LM (Bergquist and Westerberg 2014; Dellve et al. 2018). The practice 'Intensive use of

communication and feedback mechanisms' (P5) was also confirmed, and the scale items seek to measure the usage of formal and informal conversations to involve employees in daily decisions in the company (Ahlstrand and Gautié 2023; Klein et al. 2023).
<figure><img src="image_34.png" /><figcaption>**Table 7.** Multi-item measurement scale.</figcaption></figure>

<table><thead><tr><th>Practice</th><th>Code</th><th>Items</th></tr></thead><tbody><tr><td rowspan="4">Ensuring alignment of individual behaviours with company goals (P1)</td><td>P1.1</td><td>I consider that my individual goals are aligned with the company's objectives.</td></tr><tr><td>P1.2</td><td>I consider that my individual goals are aligned with the objectives of the following task/activity/process (internal customer view)</td></tr><tr><td>P1.3</td><td>I frequently participate in meetings to align the objectives of my sector, area of expertise, or process.</td></tr><tr><td>P1.4</td><td>I agree/I am aware of the results that my managers define for my work.</td></tr><tr><td rowspan="2">Making job resources available (P2)</td><td>P2.1</td><td>I often have enough time for myself and my team to carry out our lean responsibilities.</td></tr><tr><td>P2.4</td><td>I often have technology resources my company provides to use in lean implementation/efforts.</td></tr><tr><td rowspan="2">Management support (P4)</td><td>P4.1</td><td>I often have support from my managers in carrying out improvement tasks</td></tr><tr><td>P4.2</td><td>I agree that in my company, senior managers allocate financial and non-financial resources (such as time) to lean efforts.</td></tr><tr><td rowspan="5">Intensive use of communication and feedback mechanisms (P5)</td><td>P4.3</td><td>I can often rely on my senior managers, who always assist us when we have problems.</td></tr><tr><td>P5.1</td><td>I can often communicate with anyone in the company, whether they are from another area, process, or department.</td></tr><tr><td>P5.2</td><td>I often participate in informal conversations with my managers about issues and improvement initiatives.</td></tr><tr><td>P5.3</td><td>I frequently communicate formally (in meetings and emails) with my managers about issues and improvement initiatives.</td></tr><tr><td>P5.4</td><td>I often participate in informal conversations with my co-workers about issues and improvement initiatives.</td></tr><tr><td rowspan="2">Education programmes, training, and personal development (P6)</td><td>P6.1</td><td>I often participate in training programmes that provide me with knowledge and self-development</td></tr><tr><td>P6.2</td><td>I often participate in technical training in lean production.</td></tr><tr><td rowspan="2">Continuous development of employee's leadership skills (P7)</td><td>P6.3</td><td>I often attend training to develop teamwork skills (e.g. communication and interpersonal skills)</td></tr><tr><td>P6.4</td><td>I often participate in training with adequate quality.</td></tr><tr><td rowspan="4">Use of kaizen events (P8)</td><td>P7.1</td><td>I am often encouraged to develop leadership skills by my company, which encourages me to use my autonomy on a day-to-day basis.</td></tr><tr><td>P7.2</td><td>I agree that my company is concerned with continuously monitoring employee leadership skills and takes action to improve these skills.</td></tr><tr><td>P7.3</td><td>I often rely on our leaders to challenge existing ways of doing work and share knowledge about different ways of doing work.</td></tr><tr><td>P8.4</td><td>I am often motivated/enjoyed to participate in kaizen events.</td></tr><tr><td rowspan="2">Using reward-based incentives (P9)</td><td>P8.5</td><td>I often draw on lessons from previous kaizen events to solve new problems.</td></tr><tr><td>P8.6</td><td>I frequently engage in product and process improvement efforts.</td></tr><tr><td rowspan="2">Providing career advancement opportunities to employees (P10)</td><td>P9.1</td><td>I often receive, in addition to fixed compensation (salary and annual raises), rewards with a variable payment that depends on performance, collective effort, professionalism, and flexibility.</td></tr><tr><td>P9.2</td><td>I frequently receive recognition for my achievements and rewards for innovative ideas.</td></tr><tr><td rowspan="2">Driving suggestion programmes (P11)</td><td>P9.3</td><td>I am often rewarded for group achievements.</td></tr><tr><td>P10.1</td><td>I often advance in my career based on merit and performance</td></tr><tr><td rowspan="2">Empowerment (job autonomy) (P12)</td><td>P10.2</td><td>I often have excellent opportunities to improve my career.</td></tr><tr><td>P10.3</td><td>My company often guides me to develop skills that help me improve and succeed in my career.</td></tr><tr><td rowspan="4">Promotion of teamwork (P13)</td><td>P11.1</td><td>I frequently suggest ideas for improvement suggestion programmes.</td></tr><tr><td>P11.2</td><td>I frequently make suggestions for product and process improvements.</td></tr><tr><td>P11.3</td><td>I often make suggestions for improving the conditions of the work environment.</td></tr><tr><td>P12.1</td><td>I often feel empowered or empowered to make decisions about my work.</td></tr><tr><td rowspan="4">Multifunctional employee (job rotation) (P14)</td><td>P12.2</td><td>I often have the autonomy to actively contribute to making decisions about my area, process, or department.</td></tr><tr><td>P12.3</td><td>I often have autonomy to use my skills and abilities to perform and/or improve my work.</td></tr><tr><td>P12.4</td><td>My manager often delegates powers to me to make decisions about my work.</td></tr><tr><td>P12.5</td><td>I am often tasked by my manager with making decisions about the area or process in which I work.</td></tr><tr><td rowspan="2">Encouraging innovation and technologies (P15)</td><td>P13.1</td><td>I often get involved in decision-making work teams.</td></tr><tr><td>P13.2</td><td>I often feel satisfied with the achievements achieved by the work team I am part of.</td></tr><tr><td rowspan="2"></td><td>P13.3</td><td>I often contribute to the group decision-making process.</td></tr><tr><td>P14.1</td><td>I am often trained in more than one area of work</td></tr><tr><td rowspan="2"></td><td>P14.2</td><td>I often receive cross-training, ensuring I am not an expert in just one activity.</td></tr><tr><td>P14.3</td><td>I often participate in cross-training to develop multiple skills.</td></tr><tr><td rowspan="4"></td><td>P15.1</td><td>I agree that my company uses information technology systems as a source of knowledge to inspire and engage me in lean change.</td></tr><tr><td>P15.2</td><td>I am often involved in activities that use active methodologies as a source of knowledge to inspire me to engage in lean change.</td></tr><tr><td>P15.3</td><td>I agree that my company invests in new technologies, such as comprehensive modelling tools and specialty software, in lean efforts.</td></tr><tr><td>P15.4</td><td>I frequently use collaborative technology with tools and systems designed and made available by my company to facilitate group work in the office and remotely.</td></tr></tbody></table>

**Source:** Elaborated by the authors.
<figure><img src="image_35.png" /></figure>

Regarding the practice 'Education programs, training, and personal development' (P6) the scale items seek to measure the employee's perception of their participation in training and development of skills for work and the continuous 'development of employee's leadership skills' (P7) practice seeks to measure how much operators feel encouraged to use autonomy in their day-to-day tasks to seek and make systematic improvements in work processes (Anand, Chhajed, and Delfin 2012).

The 'Using Kaizen events' (P8) practice seeks to measure the participation and voluntary motivation of operators in improvement events (Mathiyazhagan, Gnanavel-babu, and Agarwal 2022; Vo, Kongar, and Suárez Barraza 2019). For this practice, three items were confirmed during the validation process: P8.1 'I often participate in kaizen events', P8.2 'I often voluntarily participate in continuous improvement (kaizen) events,' and P8.3 'I am often encouraged to participate in improvement events to solve complex problems.' Other items were possibly not confirmed because two items had the same meaning as the others items about the same practice: P8.4 'I am often motivated/enjoyed to participate in kaizen events,' P8.5 'I often draw on lessons from previous kaizen events to solve new problems' and P8.6 'I frequently engage in product and process improvement efforts.' The large number of items saying the same thing indicates the widespread presence of similar concepts in the literature.

The 'Reward-based incentives' (P9) practice measures operators' satisfaction with individual and group incentives for innovative ideas (Boxall and Winterton 2018). The practice 'Providing career advancement opportunities to employees' (P10) seeks to measure how operators feel they can progress in their career based on their merit and performance and how much they perceive that the company is concerned with offering opportunities for their development and professional success (Ferrer, Calvo, and Santa 2023). 'Driving suggestion programs' (P11) practice seeks to measure how much operators suggest new ideas for the product, process, and development of their work (Beraldin, Danese, and Romano 2022; Roslin et al. 2019).

The 'Promotion of Teamwork' (P13) practice measures how satisfied operators are with being involved and contributing to decision groups (Beraldin, Danese, and Romano 2022; Narayanan et al. 2022). The 'Multifunctional employee (job rotation)' practice (P14) measures whether operators perceive that they are trained in more than one area and that, with this, they develop multiple skills (Narayanan et al. 2022; Signoretti 2020). Finally, the 'Encouraging innovation and technologies' (P15) practice seeks to measure how much operators perceive the company's investment in technologies and how much

this contributes to information and increased knowledge through collaborative technologies (Moica et al. 2019; Pereira and Liker 2021).

## 5.2. Theoretical contributions and managerial implications

Employee involvement is one of the fundamental pillars for effective LM implementation (Alhuraish, Robledo, and Kobi 2017; Bortolotti, Boscari, and Danese 2015; Godinho Filho, Ganga, and Gunasekaran 2016). The proposed scale enhances measurement precision by providing a nuanced and comprehensive measure of employee involvement in LM, addressing a critical gap in existing research tools. By capturing various dimensions of involvement, researchers can gain deeper insights into how different aspects of employee involvement impact LM success and how they evolve. Additionally, this scale serves as a valuable tool for comparative studies across different industries, geographical regions, or organisational sizes, enabling the identification of best practices and the development of generalised principles applicable to diverse LM environments.

The scale's robustness allows for its use in longitudinal research to track changes in employee involvement over time, aiding in understanding the long-term impacts of different LM interventions and the sustainability of employee involvement strategies. Furthermore, with precise metrics provided by the scale, researchers can design targeted interventions to enhance employee involvement and subsequently evaluate the effectiveness of these interventions, contributing to the development of evidence-based strategies for LM implementation. Our proposed scale also facilitates multidisciplinary research, integrating insights from organisational behaviour, HMR, and industrial engineering. This fosters a holistic understanding of LM practices and their broader organisational impacts. Researchers can use the scale to test various hypotheses about employee involvement and LM outcomes, exploring relationships between employee involvement and productivity, quality improvements, or innovation within LM environments.

This scale offers invaluable insights for industry managers and decision-makers. It functions as a comprehensive diagnostic tool, enabling organisations to assess and understand the current state of employee involvement in their LM processes. By identifying prevalent practices and areas needing improvement, the scale guides managers in developing targeted strategies to enhance employee involvement, thereby facilitating a more effective implementation of LM methodologies. The strategic implementation of these practices, as guided by the scale, can reduce resistance to change, heighten employee
<figure><img src="image_36.png" /></figure>

involvement, and improve overall productivity. Such advancements are crucial for fostering a culture of continuous improvement and a deeper commitment to LM principles in the organisation. Furthermore, the scale serves as a potent benchmarking tool. It allows organisations to measure their practices against industry standards and conduct comparative analyses within their sector and across different industries. This benchmarking capability is vital for organisations seeking to understand their competitive position and identify strategic areas for improvement. It is also important to note that the application of the scale goes beyond the immediate operational benefits. By enabling a more engaged and motivated workforce, the scale contributes to improving the overall quality of work life for employees. This, in turn, aligns with broader societal goals of sustainable and ethical business practices, underscoring the role of employee well-being in the success of LM initiatives.

## 6. Conclusions

This study introduces a multi-dimensional scale designed to measure employee involvement, specifically in LM environments. Unlike previous research broadly addressing employee involvement, it provides a targeted approach within the LM context. The scale encapsulates diverse practice categories, ranging from resource allocation and personnel training to enhancing employee experiences, formalising practices, and aligning with organisational goals. This study stands out for its methodological rigour, employing a systematic approach and robust techniques, which include a literature review and empirical validation, thereby creating a tool that accurately measures employee involvement in LM across various industries.

While our study provides valuable insights into the concept of employee involvement within organisational contexts, it is important to acknowledge certain limitations that may guide future research endeavours. First, we applied non-random sampling techniques for survey distribution. Nonetheless, this limitation was partially offset by the significant number of valid responses collected: 94 from the initial survey and 176 from the subsequent one. Future research would gain from adopting different sampling strategies, like simple random, systematic, or stratified random sampling, to increase the representativeness of the results. Secondly, future research could focus on conducting more extensive data collection within specific sectors. This would enable the determination of appropriate weights for the constructs, thereby providing managers with a more tailored and effective tool for implementing the developed scale. Thirdly, our decision to replace instances of 'employee engagement' with 'employee involvement' was made to align with the

specific focus of our study. However, this shift may have limited our exploration of broader engagement-related constructs, such as those outlined in the Kahn model (Kahn 1990), which primarily address dimensions of meaningfulness, safety, and availability more relevant to engagement rather than involvement. Therefore, future research could benefit from examining the applicability of existing models, like the Kahn model, or developing new frameworks tailored specifically to understanding and enhancing employee involvement and engagement. Finally, our scale only measures the effect of interventions. Future research is needed to use our scale to actually test interventions. Our scale can, for example, serve as a valuable tool for evaluating the effectiveness of change management efforts to enhance HRM involvement. Using our validated instrument, researchers can quantitatively measure the level of HRM involvement before and after implementing change management strategies. This would enable a systematic assessment of the impact of various interventions, such as training programmes, communication strategies, and leadership initiatives. This provides actionable insights for organisations by identifying areas where HRM involvement may be lacking, or interventions have been successful. Our measurement scale can ultimately facilitate evidence-based decision-making in the change management process, helping organisations optimise their practices to drive successful LM transformations.

## Disclosure statement

No potential conflict of interest was reported by the author(s).

## Notes on contributors

<figure><img src="image_37.png" /></figure>

Nayara Cardoso de Madeiros is an assistant professor in the Industrial Engineering course at the Federal University of Piauí, Brazil. She holds a doctorate in Industrial Engineering from the Federal University of São Carlos (UFSCar). Her research areas are employee involvement practices in lean manufacturing and measuring this. She teaches courses related to operation management.

<figure><img src="image_38.png" /></figure>

Moacir Godinho Filho is Full professor at EM Normandie Business School. He is also affiliated with the Graduate Program in Industrial Engineering at the Federal University of São Carlos. He has published over 200 papers in Supply Chain Management and Operations Management in the last 20 years. He has experience in teaching, researching, and consulting projects in Production Planning and Control, Logistics, Lean Manufacturing, lead time reduction in several companies in Brazil and abroad.
<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>

**Mario Henrique Callefi** is an Associate Researcher in the Chair of Factory Planning and Intralogistics at Chemnitz University of Technology. Prior studies include PhD degree in Industrial Engineering from the Department of Production Engineering at the Federal University of São Carlos (UFScar), and experience in simulation projects. He is a member of SC4.0, an international research network in Supply Chain 4.0.

<figure><img src="image_41.png" /></figure>

**Gilberto Miller Devós Ganga** is an Associate Professor in the Department of Production Engineering at the Federal University of São Carlos (UFScar). He holds a Master and Doctorate in Production Engineering from the São Carlos School of Engineering (EESC), University of São Paulo (USP). Professor Ganga has published articles in journals with a selective editorial policy. He is a member of SC4.0, an international research network in Supply Chain 4.0 (http://www.supplychain4.org), in the context of Industry 4.0.

<figure><img src="image_42.png" /></figure>

**Jonhatan Magno Norte da Silva** is a professor at the Federal University of Alagoas (UFAL) and the lead researcher at the Group of Ergonomics and New Tools (GENT/UFAL). Over the past decade, he has published more than 60 papers on Operations Management, Human Factors, and Industrial Sustainability. His research focuses on Industrial Management, Ergonomics, Artificial Intelligence, Applied Statistics, and Programming Algorithms in Industry 4.0 and 5.0.

<figure><img src="image_43.png" /></figure>

**Matthias Thürer** contributed to the improvement, simplification and integration of material flow control systems, and their integration with Industry 4.0. Apart from Operations Management, Matthias is also interested in social and philosophical issues including system theory, cybernetics, causality, and philosophy of science.

<figure><img src="image_44.png" /></figure>

**Léony Luis Lopes Negrão** is a professor in the Department of Production Engineering, State University of Pará (Brazil). He graduated from the State University of Pará, holds a master's degree from the Federal University of Pará, and a PhD from the Federal University of São Carlos (Brazil). He has experience in Production Engineering, focusing on the planning, design, and control of production systems, working on the following topics: production planning and control, lean manufacturing, cost management, and project evaluation.

<figure><img src="image_45.png" /></figure>

**Fabiane Letícia Lizarelli** is an Associate Professor at the Production Engineering Department on Federal University of São Carlos (UFScar). She holds B.Sc., M.Sc., and Ph.D. degrees from the Federal University of São Carlos, São Carlos, Brazil, in 2005, 2008, and 2013, respectively. Her research interests are in continuous improvement programmes such as Six Sigma, Lean, and Lean Six Sigma, and she has been studying the relationship of these programmes with Industry 4.0 technologies, mainly Big Data Analytics. She has studied and published in renowned and refereed journals and conferences on these topics for the past ten years.

## Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

## ORCID

Nayara Cardoso de Medeiros ID http://orcid.org/0000-0003-1620-4318

Moacir Godinho Filho ID http://orcid.org/0000-0001-6150-1333

Mario Henrique Callefi ID http://orcid.org/0000-0001-8698-0043

Gilberto Miller Devós Ganga ID http://orcid.org/0000-0001-9328-5727

Jonhatan Magno Norte da Silva ID http://orcid.org/0000-0003-4084-9511

Matthias Thürer ID http://orcid.org/0000-0002-2705-969X

Léony Luis Lopes Negrão ID http://orcid.org/0000-0003-3059-894X

Fabiane Letícia Lizarelli ID http://orcid.org/0000-0002-8959-9982

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# Appendix

<table><caption>Table A1. Items excluded from scale.</caption><thead><tr><td>Practice</td><td>Code</td><td>Items</td></tr></thead><tbody><tr><td>Ensuring alignment of individual behaviours with company goals (P1)</td><td>P1.5</td><td>I can often achieve the objectives my managers set for my work.</td></tr><tr><td rowspan="2">Making job resources available (P2)</td><td>P2.2</td><td>I often have access to a dedicated meeting room and dedicated spaces for teams to post communications.</td></tr><tr><td>P2.3</td><td>I agree that my company seeks to use top talent in lean efforts. In addition, participation is always encouraged but not required.</td></tr><tr><td rowspan="3">Using Kaizen events (P8)</td><td>P8.1</td><td>I often participate in kaizen events</td></tr><tr><td>P8.2</td><td>I often voluntarily participate in continuous improvement (kaizen) events.</td></tr><tr><td>P8.3</td><td>I am often encouraged to participate in improvement events to solve complex problems.</td></tr></tbody></table>

Source: Elaborated by the authors.