Daiana Colledani
Egidio Robusto
Pasquale Anselmi- 1Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Padua, Italy
- 2Department of Psychology, Sapienza University of Rome, Rome, Italy
Introduction: Soft skills, also known as transversal skills, have gained significant attention in the organizational context due to their positive impact on various work-related outcomes. The present study aimed to develop and validate the Multiple Soft Skills Assessment Tool (MSSAT), a short self-report instrument that evaluates interpersonal skills (initiative-resourcefulness, assertiveness, conflict management), interpersonal communication skills, decision-making style (adaptive and maladaptive), and moral integrity.
Methods: The scale development process involved selecting and adapting relevant items from existing scales and employing a cross-validation approach with a large sample of workers from diverse organizational settings and job positions (N = 639). In the first step, 28 items were carefully chosen from an item pool of 64 items based on their content, factor loadings, item response theory analyses, differential item functioning, and fit statistics. Next, the structure of the resulting scale was evaluated through confirmatory factor analyses.
Results: The MSSAT demonstrated gender invariance and good reliability and validity. The results of a network analysis confirmed the relationships between soft skills and positive work-related outcomes. Notably, interpersonal communication skills and moral integrity emerged as crucial skills.
Discussion: The MSSAT is a valuable tool for organizations to assess the soft skills of their employees, thereby contributing to design targeted development programs.
Introduction
Soft skills, often referred to as transversal, non-technical or social skills, comprise a wide range of personal qualities, behaviors, and competencies that go beyond technical expertise. Yorke (2006) characterizes them as a blend of dispositions, understandings, attributes, and practices. Their multifaceted nature is reflected in the literature, which abounds with models and taxonomies delineating various soft skills. These encompass a wide spectrum of abilities, such as conflict resolution, decision-making, presentation skills, teamwork, communication skills, relationship management, leadership, adaptability, problem-solving, ethics, and values (e.g., Cimatti, 2016; Khaouja et al., 2019; Soto et al., 2022; Verma and Bedi, 2008).
While labor market studies have traditionally focused on technical skills and knowledge, currently there is a growing recognition of the importance of soft skills (Balcar, 2016; Ciappei and Cinque, 2014; Eshet, 2004; Seligman, 2002). This shift in focus stems from a deeper understanding of the positive impact of soft skills on successful careers and employability (Charoensap-Kelly et al., 2016; Salleh et al., 2010; Sharma, 2018; Styron, 2023; Yahyazadeh-Jeloudar and Lotfi-Goodarzi, 2012). A large body of research suggests that employees with strong soft skills not only improve their job performance (Ibrahim et al., 2017), but are also less prone to poor psychophysical health and burnout (Rosa and Madonna, 2020; Semaan et al., 2021). In addition, soft skills were found to increase individual drive and passion, which promotes overall productivity and organizational growth (Murugan and Sujatha, 2020; Nugraha et al., 2021).
Given the recognized contribution of soft skills in the promotion of successful careers, their role in enhancing employability, and their potential for improvement through appropriate training programs, soft skills have become a topic of great interest to human resource professionals, and their assessment has become a standard practice in personnel selection and training design (Charoensap-Kelly et al., 2016; Gibb, 2014; Salleh et al., 2010; Sharma, 2018; Styron, 2023).
Having available a tool that reliably and effectively measures key soft skills in different organizational contexts would be of great value in a number of ways. From an applied perspective, it could facilitate recruitment and selection processes by enabling the effective and efficient assessment of soft skills required for success in candidates (Asefer and Abidin, 2021; Nickson et al., 2012). This is particularly relevant in modern times, as the labor market is dynamic and constantly seeking individuals with the employability skills required by workplaces. However, the literature reports a significant gap between the soft skills desired by employers and the level of these skills among candidates and new hires, often resulting in many positions remaining vacant (Abelha et al., 2020; Hurrell, 2016; Jackson and Bridgstock, 2018; Nisha and Rajasekaran, 2018). The skills gap is a recognized talent management challenge (McDonnell, 2011). In addition, a scale that efficiently assesses key soft skills would also facilitate ongoing monitoring providing valuable information on employees’ strengths and areas for improvement, which would be useful in designing targeted training and development programs that foster both personal and professional growth (Adhvaryu et al., 2023; Widad and Abdellah, 2022). Using a scale that efficiently assesses soft skills within the organization can also be useful in inspiring initiatives to improve organizational culture, including diversity and inclusion efforts, conflict resolution, and employee well-being programs (Juhász et al., 2023). Moreover, understanding the soft skills profiles of team members can help managers assemble balanced teams with complementary strengths, thereby fostering better collaboration, innovation, and problem solving. Finally, from a broader perspective, a scale that reliably assesses key soft skills in different organizational contexts can facilitate their study in real-world settings, leading to a more nuanced understanding of how they interact and affect organizational contexts. Moreover, it would promote the study of the transferability of these competencies across contexts, roles and sectors, helping to identify which soft skills have value universally and which are context-specific. In fact, although the soft skills required for different job profiles vary to some extent, some core soft skills are considered essential in most contemporary business environments and sectors (Alsabbah and Ibrahim, 2013; Kyllonen, 2013; Paddi, 2014).
This work aims to develop and validate the Multiple Soft Skills Assessment Tool (MSSAT), a short self-report instrument designed to efficiently and reliably measure key soft skills in different organizational contexts. MSSAT focuses on four relevant soft skills domains, namely interpersonal skills, communication skills, decision-making style, and moral integrity. These skills were chosen because they are widely accepted in different taxonomies and are considered important in different professional positions (Khaouja et al., 2019; Soto et al., 2022). The decision to limit the number of items for each skill was driven by the practical need of organizations to obtain quick and accurate assessments. In fact, short assessment tools are valuable to organizations because they facilitate accurate responses and minimize the time required to complete the assessment (Burisch, 1984; Fisher et al., 2016; Sharma, 2022). MSSAT is expected to be a useful tool for applications in the aforementioned contexts.
The development and validation of the MSSAT closely adhere to best practices in the literature (Boateng et al., 2018; Hinkin, 1998). First, the dimensions of interest are identified and an initial pool of items is constructed to assess them. Next, the item pool is administered to an appropriate sample of individuals, and the items to be included in the scale are selected. Finally, the psychometric properties of the scale, including dimensionality, reliability, validity, and nomological network, are assessed. Psychometrically sound measures (e.g., Colledani et al., 2024; Hinkin and Schriesheim, 1989; Kumar and Beyerlein, 1991; Tassé et al., 2016) have been obtained using these practices.
Section “Soft Skills of Interest and Item Pool” describes the identification of the soft skills of interest and the construction of an item pool from which the items were selected to develop the MSSAT. Section “Method” describes the procedures employed to select the items and to validate the scale.
Soft skills of interest and item pool
To develop the MSSAT, the soft skills of interest were identified and an item pool was constructed, consisting of items selected from instruments available in the literature. This method of test development is described, for instance, in Boateng et al. (2018) and Hinkin (1995). The items were drawn from instruments not specifically designed for the organizational context and were carefully reformulated by the authors of this study (experts in the fields of organizational psychology and psychometrics) to ensure their suitability for effectively assessing soft skills in organizational settings.
Interpersonal skills
Three interpersonal skills were considered in the development of the MSSAT because of their profound impact on professional success: initiative-resourcefulness, assertiveness, and conflict management. Initiative-resourcefulness denotes the ability to engage with new and interesting people, as well as the ability to present oneself appropriately. This skill is crucial in organizational settings and is often associated with greater job satisfaction and better performance (Agba, 2018; Akla and Indradewa, 2022; Nadim et al., 2012). Assertiveness denotes the ability to pursue one’s rights, will, and needs in a firm but non-aggressive manner. It is a crucial skill that can reduce conflict and decrease stress, burnout, and turnover intentions (Butt and Zahid, 2015; Ellis and Miller, 1993; Tănase et al., 2012). Finally, conflict management denotes the ability to prevent interpersonal conflict and manage conflict situations effectively. Consistent with the literature, conflict is an inevitable occurrence in organizations and often results in reduced employee and organizational flourishing (Awan and Saeed, 2015; Henry, 2009). However, when properly managed, conflict can be used to drive change and improve employee satisfaction and organizational performance.
To construct the subscales measuring these three interpersonal skills, 22 items were extracted from three of the five subscales included in the Interpersonal Competence Questionnaire (Buhrmester et al., 1988), namely from Initiating Relationships, Expressing Displeasure with Others’ Actions, and Managing Interpersonal Conflicts. Since the items were originally developed to assess these interpersonal skills in the context of peer relationships, they were reformulated for use in the organizational context.
Communication skills
Communication is the process of exchanging information to reach a common understanding. Effective interpersonal communication skills are critical to achieving organizational goals and fostering professional success. Research has consistently demonstrated the critical role of interpersonal communication skills in boosting job and team performance (Dehghan and Ma’toufi, 2016; Keerativutisest and Hanson, 2017). Furthermore, research has shown that good communication skills increase organizational commitment and buffer the onset of emotional exhaustion while promoting self-actualization (Bambacas and Patrickson, 2008; Emold et al., 2011; Paksoy et al., 2017).
To construct the scale measuring communication skills, nine items were selected from the Interpersonal Communication Satisfaction Inventory (Hecht, 1978). This 19-item scale measures satisfaction with interpersonal communication by assessing respondents’ reactions to recent conversations in which they have been involved. Although the scale does not directly assess communication skills, it does include several items that refer to functional communication behaviors. In addition, since communication satisfaction is often associated with competent communication, it may serve as an indirect indicator of communication competence (Hecht, 1978). The nine items selected for inclusion in the MSSAT were chosen because they were considered most appropriate for assessing communication skills in a professional context.
Decision-making
Extensive research has identified a strong relationship between poor decision-making skills and detrimental employee outcomes, including increased risk-taking, maladaptive coping mechanisms, perceived stress, emotional exhaustion, depersonalization, and sleep disturbance (Allwood and Salo, 2012; Del Missier et al., 2012; Valieva, 2020; Weller et al., 2012). In contrast, robust decision-making skills have been found to enable the formulation of adaptive goals and the adoption of appropriate actions to achieve them (Byrnes, 2013; Byrnes et al., 1999), and have been consistently recognized as a critical factor in organizational effectiveness, employee engagement, work commitment, and job satisfaction (Ceschi et al., 2017).
The 22 items of the Melbourne Decision-making Questionnaire (DMQ; Mann et al., 1997) were used to construct the decision-making style measure to be included in the MSSAT. The DMQ is a well-known instrument that assesses decision-making in terms of four primary styles: vigilance, hypervigilance, buck-passing, and procrastination. Vigilance involves a series of processes aimed at clarifying goals, seeking information, exploring and evaluating alternatives, and ultimately making a decision. Vigilance is considered the only decision-making style that allows for functional and rational choices (Mann et al., 1997). In contrast, hypervigilance involves a frantic search for solutions, often driven by a sense of time pressure. Decision makers in this condition tend to impulsively adopt hastily devised solutions, usually aimed at providing immediate relief. Hypervigilance is characterized by a heightened emotional state, which leads to a disregard for the full range of consequences. Buck-passing is the act of shifting the responsibility for making a decision to someone else. This behavior refers to the act of avoiding personal responsibility and shifting the buck to others. Additionally, procrastination involves replacing high-priority tasks with lower-priority activities and engaging in pleasurable distractions. This behavior can lead to the postponement or avoidance of important decisions.
The decision-making measure of the MSSAT was constructed using all the DMQ items because of their generic wording, which makes them applicable to different contexts (including the workplace), and the brevity of the subscales (each containing 5 or 6 items).
Integrity
Integrity has received considerable attention in the literature on personnel selection in recent decades (Schmidt and Hunter, 1998). Early studies focused mainly on the positive impact of ethical leadership (De Carlo et al., 2020; Engelbrecht et al., 2017). However, subsequent research has also established the positive role of employee integrity. For instance, employee integrity has been shown to be positively correlated with improved performance (Inwald et al., 1991; Luther, 2000; Ones et al., 1993; Posthuma and Maertz, 2003) and negatively associated with counterproductive work behaviors (Van Iddekinge et al., 2012).
To assess integrity, 12 items were selected from the 18-item Integrity Scale developed by Schlenker (2008). This scale measures the value placed on ethical behavior, adherence to principles despite temptation or personal cost, and refusal to justify unethical behavior, and includes facets of integrity such as truthfulness and honesty. Higher scores indicate greater commitment to ethical principles and higher levels of integrity (Schlenker, 2008). The 12 items considered in the development of the MSSAT measure of integrity are those most appropriate for assessing integrity in organizational contexts.
The resulting item pool consists of 64 items that were carefully selected by the authors of the present study based on their relevance to the organizational contexts, and in some cases reformulated to optimize their appropriateness to work contexts. This item pool constitutes the basis for the construction of the MSSAT.
Method
Participants
A total of 639 participants (mean age = 38.08; SD = 1.45; males = 352, 55.1%) were recruited through snowball sampling procedure. The majority of them were white-collar workers (N = 269, 42.1%), followed by blue-collar workers (N = 90, 14.1%). Other occupations included shop assistants (N = 26, 4.1%), health professionals (N = 73, 11.4%), and teachers and educators (N = 71, 11.1%). The remaining 17.2% (N = 110) comprised managers, professionals, freelancers, and entrepreneurs from various sectors. The majority of participants worked full-time (N = 346, 75.87%, over 30 h per week) and had a high level of seniority (N = 249, 39.97%, up 10 years; N = 390, 61.03%, over 10 years).
Participants did not receive any compensation for their involvement in the study, and the only inclusion criterion was being a worker aged 18 years or above. Prior to access to the online survey, participants were asked to provide electronic informed consent, which explained the purpose of the study, the estimated duration of the task, and the option to withdraw consent at any time during the study. Participation in the study was anonymous and voluntary. The study was conducted in adherence to the ethical principles for research outlined in the Declaration of Helsinki and approved by the local committee for psychological research of the University of Padua.
Materials and procedure
All participants were presented with an online survey consisting of a series of closed-ended questions regarding their demographics (e.g., including age, gender, professional sector, and contract type), the 67 items of the item pool, and three additional scales measuring burnout job satisfaction, and performance.
Job satisfaction was evaluated using the scale developed by Dazzi et al. (1998). The scale comprises six items (e.g., “I feel satisfied with my work”) with higher scores indicating greater satisfaction with one’s work (Cronbach’s α = 0.82 in the current sample). Burnout was measured using the Qu-Bo test developed by De Carlo et al. (2008/2011). The instrument consists of nine items, divided into three subdimensions of three items each: exhaustion (e.g., “I feel burned out from my work”), cynicism (e.g., “My work has no importance”), and reduced sense of personal accomplishment (e.g., “I feel incapable of doing my job”). In this study, a total scale score was computed, with higher scores indicating higher levels of burnout (Cronbach’s α = 0.84 in the current sample).
The items pertaining soft-skills, job satisfaction, and burnout employed a 4-point response scale (1 = “Completely disagree” to 4 = “Completely agree”).
Finally, job performance was evaluated using four items. Two of them (e.g., “How would you rate your job performance in the past year?”; “Over the past year, how did your supervisors and/or co-workers rate your job performance?”) used a 10-point Likert scale format (1 = “Very poor performance”) to 10 (“Very good performance”), while the other two items (e.g., “Please indicate the percentage of your work goals achieved during the past year”; “Please indicate the percentage to which your manager and/or co-workers believe you have been successful in achieving your work goals over the past year”) asked participants to express their work goal achievement as a percentage from 10% (“Very poor performance”) to 100% (“Very good performance”; in the current sample, Cronbach’s α = 0.86).
Data analysis
A two-step approach was used to construct and validate the MSSAT. In the first step, the data sample was divided into two parts. The first part (calibration dataset; N = 319) was analyzed to identify the best items, four for each dimension, for inclusion in the MSSAT, while the second part (validation dataset; N = 320) was used to confirm the factorial structure of the scale. In the second step, the entire dataset (N = 639) was analyzed to examine the measurement invariance of the instrument across gender, as well as its reliability, validity, and nomological network. The sizes of the calibration and validation datasets, as well as the size of the entire dataset, are appropriate according to the common criteria of at least four participants for each item (Hinkin, 1998; Rummel, 1970) and at least 300 participants overall (Boateng et al., 2018; Comrey, 1988; Guadagnoli and Velicer, 1988).
Development of the MSSAT
To select the most appropriate items for inclusion in the MSSAT, a subsample comprising responses from 319 participants was used (mean age = 38.60 years, SD = 14.39; males = 128, 40.1%). The subsample was obtained by randomly dividing the entire dataset into two parts. The items related to the four soft skills domains, namely interpersonal skills (initiative-resourcefulness, assertiveness, conflict management), interpersonal communication skills, decision-making style (vigilance, hypervigilance, procrastination, and buck-passing), and integrity, were analyzed separately using the following procedure. First, the minimum average partial (MAP; Velicer, 1976) test was used to evaluate the dimensionality of each scale. The data of each scale were then analyzed using factor analysis and item response theory (IRT). In particular, exploratory factor analysis (EFA) and the graded response model (GRM; Samejima, 1969) were applied. These methods are commonly used in scale development (Colledani et al., 2018a; Colledani et al., 2019a, 2019b; Colledani et al., 2018b; Lalor et al., 2016; Tassé et al., 2016; Zanon et al., 2016) and provide a convenient framework for evaluating numerous relevant attributes of the items, such as location on the latent trait continuum, discriminability, item fit, and differential item functioning (DIF).
In this work, item fit was evaluated using the signed chi-square test (S-χ2; Orlando and Thissen, 2000). A significant p-value for the S-χ2 of an item indicates that the responses to the item have a poor fit to the IRT model, a condition referred to as misfit. Gender DIF was assessed through ordinal logistic regression analyses. This approach uses the estimates of an IRT model (the GRM in this study) to quantify the trait levels of participants (i.e., their level in each of the soft skills under consideration) and a dichotomous variable to indicate their group membership (i.e., male vs female). To identify uniform DIF, only the impact of trait levels and group membership (gender group) on the item responses was considered, while to identify nonuniform DIF, the interaction term between trait levels and group membership was also considered. For items exhibiting DIF (uniform, nonuniform, or both), the McFadden Pseudo-R2 was used to determine the magnitude of the effect. Values less than 0.035, between 0.035 and 0.07, and larger than 0.07 denote negligible, moderate, and large effect sizes, respectively (Jodoin and Gierl, 2001).
Item selection followed a two-step approach. First, the items exhibiting misfit or gender DIF were excluded. Second, four items were selected for each scale based on three criteria: the size of the factor loadings on the target dimension (and non-substantial factor loadings on non-target dimensions), the appropriateness of their location on the latent trait continuum (as indicated by the GRM threshold parameters), and the relevance of the item content to the dimension. Keeping four items per dimension is commonly recommended in the literature (Harvey et al., 1985; Hinkin, 1998) and ensures that the instrument is short.
The analyses described in this section were performed using the packages “psych” (Revelle, 2024), “mirt” (Chalmers et al., 2018), and “lordif” (Choi, 2016) for the open-source statistical environment R (R Core Team, 2018).
Validation of the MSSAT
The factor structure of the resulting scale was verified through a confirmatory factor analysis (CFA) conducted on a second subsample (N = 320; mean age = 37.53, SD = 13.83; males = 130, 40.6%). The model was run using Mplus7 (Muthén and Muthén, 2012) and the robust maximum likelihood estimator (MLR; Yuan and Bentler, 2000). Multiple-group CFAs were also performed on the entire dataset (N = 639) to test configural (same configuration of significant and nonsignificant factor loadings), metric (equality of factor loadings), and scalar (equality of both factor loadings and item intercepts) invariance across gender. To evaluate the goodness of fit of the CFAs models, several fit indexes were inspected: χ2, comparative fit index (CFI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA). A good fit is indicated by nonsignificant (p ≥ 0.05) χ2 values. Since this statistic is sensitive to sample size, the other fit measures were also inspected. CFI values close to 0.95 (0.90–0.95 for reasonable fit), and SRMR and RMSEA smaller than 0.06 (0.06–0.08 for reasonable fit) were considered indicative of adequate fit (Marsh et al., 2004). For testing the equivalence of nested models in gender invariance, the chi-square difference test (Δχ2) and the test of change in CFI (ΔCFI) were used. Invariance is indicated by a nonsignificant Δχ2 and by ΔCFIs lower than or equal to |0.01| (Cheung and Rensvold, 2002).
Reliability was verified through composite reliability (CR) coefficients. CR is conceptually similar to Cronbach’s α as it represents the ratio of true variance to total variance, but it is often considered a better index of internal consistency (Raykov, 2001). A CR value of at least 0.6 denotes satisfactory internal consistency (Bagozzi and Yi, 1988; Bentler, 2009).
Construct validity was verified by examining correlations between the seven soft skills of the MSSAT and three other constructs: job satisfaction, self-reported performance, and burnout. Based on the findings in the literature, initiative-resourcefulness, assertiveness, conflict management, communication skills, integrity, and adaptive decision making were expect to correlate positively with job satisfaction and self-reported performance, and negatively with burnout (e.g., Agba, 2018; Akla and Indradewa, 2022; Butt and Zahid, 2015; Byrnes, 2013; Ceschi et al., 2017; Ones et al., 1993; Keerativutisest and Hanson, 2017). The reverse pattern of correlations was expected for the maladaptive facets of decision-making, which were expected to correlate negatively with job satisfaction and self-reported performance, and positively with burnout (e.g., Allwood and Salo, 2012; Ceschi et al., 2017; Valieva, 2020).
To further explore the relationships between soft skills and the three considered work outcomes, network analysis was run. This approach involves estimating a network structure consisting of nodes and edges. The nodes represent the objects under analysis (i.e., scores on the considered scales), while the edges represent the relationships between them (i.e., regularized partial correlations between nodes, given all the other nodes in the network; Epskamp and Fried, 2018). Network analysis is a valuable approach, as it allows for easily exploring the interplay and interconnections among a large number of variables within a theoretical network. In this work, the network structure was built by including the scores on all the soft skills scales and the scores on the measures of burnout, job satisfaction, and self-reported performance. Based on the findings in the literature, soft skills are expected to promote better work outcomes (job satisfaction and better performance) while buffering the occurrence of burnout through direct associations and complex patterns of interconnections (Akla and Indradewa, 2022; Dehghan and Ma’toufi, 2016; Inwald et al., 1991; Tănase et al., 2012; Valieva, 2020). Therefore, network analysis is expected to provide additional contributions beyond simple correlational analyses by unveiling how soft skills interact in promoting positive work outcomes. This in turn would help to define the nomological network of soft skills (Bagozzi, 1981). In running network analysis, three common centrality indices were computed, namely betweenness, strength, and closeness. Strength centrality indicates the extent to which a node is connected with the other nodes within the network (the strength, in absolute value, of the direct connection of a node to the other nodes). High strength centrality indicates that a node is connected to many other network nodes. Closeness centrality evaluates how much a node is close to the other nodes in the network, including indirect connections. Nodes with large closeness centrality values are characterized by short paths linking them to the other nodes. Betweenness centrality captures the role of a node in connecting the other nodes within the network (Epskamp et al., 2018). Large values indicate that a node serves as a ‘bridge’ between the nodes in the network. These indices quantify the relevance of each variable in relation to the other variables within the network and provide additional relevant information beyond what is observed in other analyses. In this work, network analysis was run on the total sample (N = 639) using the EBICglasso (Extended Bayesian Information Criterion Graphical Least Absolute Shrinkage and Selection Operator; Foygel and Drton, 2010; Friedman et al., 2008) estimation method (tuning parameter was set to 0.5). Edges weights (i.e., partial correlation coefficients) were interpreted according to Ferguson’s (2016) guidelines, where values less than or equal to 0.2, from 0.2 to 0.5, and larger than 0.5 are considered as small, moderate, and large, respectively.
Results
Development of the MSSAT
Concerning the scale evaluating interpersonal skills, in line with expectations, the MAP test suggested to retain three factors. Consequently, an EFA and a multidimensional GRM with three factors were run. The results are reported in Table 1. All items showed substantial factor loadings on the intended dimension and non-substantial factor loadings on the other factors. No one item showed misfit, while six items showed uniform gender DIF (Init_16, Init_21, Init_36, Asse_32, Conf_15, and Conf_30; Init_16 exhibited nonuniform DIF as well). However, the effect size of DIF was negligible for all of them (Table 1). Since no item showed cross-loadings, misfit or noticeable DIF, four items were selected for each subscale by considering the magnitude of factor loadings, the item location on the latent trait continuum, and the item content. Following these criteria, items Init_11, Init_16, Init_21 and Init_36 were selected for the initiative-resourcefulness subscale; items Asse_2, Asse_12, Asse_22, and Asse_37 were selected for the assertiveness subscale; and items Conf_10, Conf_20, Conf_25, and Conf_35 were selected for the conflict management subscale.
Table 1. EFA factor loadings, GRM parameter estimates, fit indices, and gender DIF statistics for the 21 items of the three interpersonal skills subscales (calibration dataset, N = 319).
Concerning the scale evaluating communication skills, in line with expectations, the MAP test suggested to retain one factor. Therefore, a single factor EFA and a unidimensional GRM were run. The results are reported in Table 2. All items reported substantial factor loadings on the intended dimension and no item showed uniform or nonuniform gender DIF. However, item Comm_2 exhibited misfit. After having excluded this item from the pool, the four items required to compose the interpersonal communication skill scale were selected considering the magnitude of their factor loadings, their location on the latent trait continuum, and their content. Following these criteria, items Comm_1, Comm_8, Comm_9, and Comm_10 were selected.
Table 2. EFA factor loadings, GRM parameter estimates, fit indices, and gender DIF statistics for the nine items of the interpersonal communication skills scale (calibration dataset, N = 319).
With regard to the decision-making scale, the MAP test suggested to retain two factors. This result was unexpected because the items were anticipated to tap the four dimensions of vigilance, hypervigilance, buck-passing, and procrastination. However, following MAP indications, EFA and exploratory multidimensional GRM with two factors were run. The results demonstrated that all the items related to vigilance loaded onto a single common factor, while the remaining items, which were related to hypervigilance, buck-passing, and procrastination, loaded onto the second factor. In other words, the results showed a structure where the adaptive style of decision-making loaded on one factor and the three maladaptive styles were grouped on a single common dimension. In the maladaptive decision-making factor, two items (items MDM_7, and MDM_20 tapping, respectively, the buck-passing and hypervigilance facets of maladaptive decision-making) showed misfit and three items exhibited uniform gender DIF (MDM_12, MDM_17, and MDM_22, which tapped the buck-passing, procrastination, and hypervigilance facets of maladaptive decision-making, respectively) of negligible size (Table 3). In the adaptive decision-making factor, no item showed misfit or DIF. Since only two items from the maladaptive decision-making style were excluded from the selection due to misfit, the eight items to include in the two final decision-making subscales were selected based on the magnitude of their factor loadings, their location on the latent trait continuum, and their content. The items selected for the adaptive subscale were ADM_1, ADM_3, ADM_4, and ADM_6, while those selected for the maladaptive subscale were MDM_11, MDM_13, MDM_15, and MDM_21 (MDM_13 and MDM_15 pertain to procrastination, MDM_11 to buck-passing, and MDM_21 to hypervigilance).
Table 3. EFA factor loadings, GRM parameter estimates, fit indices, and gender DIF statistics for the 22 items of the two decision-making subscales (calibration dataset, N = 319).
Finally, for the integrity scale, in line with expectations, the MAP test suggested a unidimensional structure. Therefore, a single factor EFA and a unidimensional GRM were run. The results are reported in Table 4. All items reported substantial loadings on the latent factor. Item Inte_6 exhibited misfit while items Inte_2 (uniform) and Inte_18 (uniform and nonuniform) showed gender DIF of negligible size. After having discarded Inte_2 due to misfit, the four items of the short integrity scale were selected considering the magnitude of their factor loadings, their location on the latent trait continuum, and the item content. Following these criteria, items Inte_1, Inte_3, Inte_12, and Inte_17 were selected.
Table 4. EFA factor loadings, GRM parameter estimates, fit indices, and gender DIF statistics for the 12 items of the integrity scale (calibration dataset, N = 319).
Validation of the MSSAT
The factor structure of the scale built on the first subsample was tested through CFA on the second subsample (N = 320; the items of the MSSAT are available in the Appendix). In particular, a 7-factor structure with four indicators for each dimension was specified. The model showed satisfactory fit indices: χ2(329) = 476.107, p < 0.001; CFI = 0.927; RMSEA = 0.037 [0.030, 0.045]; SRMR = 0.059. All items loaded with large coefficients on the intended factor and factor intercorrelations were moderate in size (Table 5). Tested on the entire dataset (N = 639), measurement invariance (configural, metric, and scalar) across gender was also supported (Table 6). Internal consistency was satisfactory for all scales (CRs from 0.69 to 0.78, see Table 5; Cronbach’s α from 0.67 to 0.76, see Table 7).
Table 5. CFA factor loadings, factor correlations, and composite reliability coefficients (validation dataset, N = 320).
Table 6. Fit indices of multiple-groups factor analyses run to test the gender invariance of the MSSAT (entire dataset; N = 639).
Table 7. Descriptive statistics, reliability, correlations between all variables (entire dataset, N = 639).
All correlations between the seven soft kills, job satisfaction, self-reported performance, and burnout were consistent with expectations (Table 7). In particular, the soft skills were positively associated with job satisfaction and performance, and negatively associated with burnout. The only exception was the maladaptive facet of decision-making, which showed an inverted pattern of relationships, as expected. Although these correlations were weak in strength, they were statistically significant and in line with expectations. This result supports the construct validity of the seven soft skills subscales.
The network structure deriving from the 10 variables entered in the model (the seven soft skills measured with the MSSAT, plus burnout, job satisfaction, and performance) is represented in Figure 1. The structure includes 10 nodes (i.e., one for each variable) and 36/45 non-zero edges (sparsity of 0.200). Overall, the analysis showed edges of small to moderate size (average weights = |0.09|; see Table 8). The examination of the network structure revealed that soft skills are all interrelated with each other and associated with burnout, job satisfaction, and performance, in the expected directions. Moreover, the analysis revealed that soft skills impact work outcomes not only through direct associations but also through their interplay. For instance, with regard to performance, a significant positive edge was observed only with communication skills. However, communication skills are also associated with interpersonal skills (initiative-resourcefulness, assertiveness, conflict management), and with the adaptive facet of decision making. This pattern of relationships among the variables suggests that the positive associations of adaptive decision-making and interpersonal skills with performance that emerged in the correlational analyses may be attributed to the role of interpersonal communication skills, which may serve as a bridge connecting them. Analogously, only two soft skills are directly associated with job satisfaction, namely communication skills and integrity. However, these two variables are linked to many other skills within the network structure, enabling them to connect different skills to job satisfaction. Communication skills are particularly important in linking job satisfaction with interpersonal skills (i.e., initiative-resourcefulness, assertiveness, and conflict management) and the adaptive facet of decision making. In contrast, integrity is relevant in linking job satisfaction with decision-making (both adaptive and maladaptive), initiative-resourcefulness, and conflict management. Finally, regarding burnout, only two direct associations were observed with the two facets of decision making. Specifically, as predicted, the maladaptive facet showed a positive association, while the adaptive facet showed a negative association. The examination of the network structure reveals that adaptive and maladaptive decision making are also linked to other soft skills, serving as a bridge between them and burnout. In particular, the adaptive facet of decision-making negatively links burnout with communication, initiative-resourcefulness, conflict management and integrity, while the maladaptive facet of the construct positively links burnout with assertiveness and integrity.
Figure 1. Network structure (entire dataset, N = 639). Network originating from the seven soft skills measured using the MSSAT, plus burnout, job satisfaction, and performance. Solid lines indicate positive connections while dotted lines indicates negative connections. Thicker lines represent stronger connections while thinner represent weaker connections. For the sake of simplicity, only significant (p < 0.05) coefficients ≥ |0.08| were reported in the figure. INIT, Initiative-resourcefulness; ASSE, assertiveness; CONF, conflict management; COMM, interpersonal communication skills; ADM, adaptive decision making; MDM, maladaptive decision making; INTE, integrity; PERF, performance; BURN, burnout; JOBS, job satisfaction.
Table 8. Weight matrix and centrality measures from network analysis (entire dataset, N = 639).
The inspection of centrality indices suggests that interpersonal communication skills, maladaptive decision-making, and integrity are crucial skills for workers’ performance and well-being. These variables, in fact, showed the largest values of betweenness, closeness, and strength centrality (Table 8), indicating that they had the strongest paths with the other variables within the network and a crucial role in connecting them.
Discussion
In this work, a scale for assessing soft skills in organizational contexts was developed and validated following best practices in the literature (Boateng et al., 2018; Hinkin, 1998). An initial pool of 64 items was created by selecting and adapting items from existing instruments (Buhrmester et al., 1988; Hecht, 1978; Mann et al., 1997; Schlenker, 2008). The initial item pool was analyzed using detailed item-level methods on data from a first sample of individuals. These analyses allowed for identifying the four best items for each dimension to be included in the MSSAT.
The instrument consists of 28 items (see “Appendix”) that assess seven soft skills—initiative-resourcefulness, assertiveness, conflict management, adaptive decision-making, maladaptive decision-making, communication, and integrity—organized into four main domains (interpersonal relations, communication, decision-making, and integrity). The factor structure of the scale was confirmed on a second independent sample. All subscales showed satisfactory reliability and full scalar invariance across gender was supported. This last property is particularly valuable, as it allows for confidently using the scale to assess both men and women, and to make meaningful comparisons between them (Anselmi et al., 2022; Colledani, 2018; Fagnani et al., 2021; Vandenberg and Lance, 2000). The validity of the scale was verified by examining the correlations between the scores on the seven soft skills and measures of burnout, performance, and job satisfaction. The results showed correlations that were consistent with expectations (e.g., Dehghan and Ma’toufi, 2016; Keerativutisest and Hanson, 2017; Posthuma and Maertz, 2003; Valieva, 2020), supporting the construct validity of the MSSAT. Regarding validity, a further contribution of this work is the examination of the nomological network of soft skills with respect to the three work-related outcomes considered, which was carried out using network analysis. Network analysis represents a novel yet effective approach for exploring the nomological network of a large set of variables, as it allows for the simultaneous investigation of the complex network of interactions that connect variables. Overall, the analysis confirmed the positive role of soft skills in improving employee performance and well-being. Moreover, it revealed that soft skills are not only directly related to work outcomes, but also through complex patterns of relationships. Communication skills, maladaptive decision-making, and integrity were identified as pivotal resources based on the analysis of centrality indices. These skills are directly related to positive work outcomes and also serve to connect many other soft skills in the network. In particular, the results emphasize the critical role of communication skills and suggest that they can be viewed as key competencies capable of supporting the development of other soft skills, which ultimately contribute to professional flourishing.
Another interesting finding of the present work pertains to the substantial influence of integrity in work contexts. Indeed, this variable has long been recognized as beneficial and relevant in organizational contexts (Inwald et al., 1991; Luther, 2000; Ones et al., 1993; Posthuma and Maertz, 2003), but its role has not been deeply investigated. The results of the present work show that integrity is strongly related to many other soft skills and thus may play an important role in determining employee satisfaction.
Although the results of the present work are interesting, further investigation is needed. Future studies should focus on confirming the nomological network that emerged in this analysis and should also attempt to confirm our findings in different occupational or cultural contexts.
One of the strengths of the developed scale is its deliberately general wording. This feature makes the scale applicable in various work contexts and could potentially expand its usefulness to non-work settings, such as schools. However, further research is needed to determine the suitability of the instrument in such contexts. For example, future research could test the invariance of the instrument across different job positions and settings (e.g., organizational versus school).
Future studies would be devoted to developing a shorter version of the MSSAT, as well as another version that assesses additional key soft skills. Professionals in different organizational contexts could administer only those subscales that assess the soft skills most relevant to their area of interest.
Although further studies are needed to confirm the validity of the MSSAT, the scale appears to be a promising tool for assessing soft skills in organizational settings. It should be noted that the main limitation of the present work is its exclusive reliance on self-reported data, which are susceptible to biases such as social desirability. Future research should test the validity of the scale by considering other, more objective measures, such as peer, supervisor and manager ratings on outcome measures (e.g., work performance, absenteeism), or implicit measures (e.g., Carton and Hofer, 2006; Colledani and Camperio Ciani, 2021; Harris and Schaubroeck, 1988). In addition, the cross-sectional nature of this study limits the understanding of the predictive validity of the MSSAT. Longitudinal research is essential to examine the scale ability to predict long-term outcomes such as career advancement, and would support the usefulness of the instrument as an effective tool for personnel selection and training design.
Despite the limitations mentioned above, the MSSAT emerges as a valuable tool for organizations due to its ability to quickly assess numerous soft skills with sufficient validity and reliability. These skills have been shown to be relevant in promoting better job satisfaction and work-related outcomes. The development of the MSSAT has significant implications for organizational practice. Assessing and developing soft skills is essential for personnel selection, career advancement, employability, and positive work-related outcomes (e.g., Agba, 2018; Akla and Indradewa, 2022; Hogan et al., 2013; Nusrat and Sultana, 2019; Poláková et al., 2023). By identifying areas for improvement, the MSSAT may help organizations to tailor training programs that enhance employees’ soft skills and consequently their professional flourishing.
Data availability statement
The datasets analyzed for this study can be found in the OSF repository: https://osf.io/6rqcm/?view_only=f9fcdef687454451b0593c6661bcf1a3.
Ethics statement
The studies involving humans were approved by the School of Psychology, University of Padova. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their electronic informed consent to participate in this study.
Author contributions
DC: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing, Data curation, Formal analysis. ER: Conceptualization, Investigation, Writing – review & editing. PA: Conceptualization, Investigation, Writing – review & editing, Methodology, Writing – original draft.
Funding
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Open Access funding provided by Università degli Studi di Padova | University of Padua, Open Science Committee.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Appendix
Below you will read a series of statements describing common ways of behaving and thinking.
For each statement, please indicate the extent to which it reflects the way you usually behave and think.
Please, indicate your level of agreement with each statement, remembering that there are no right or wrong answers or tricks, only answers that do or do not correspond to your way of being and doing.
Answer quickly, without taking too long to think about the possible meanings of the statements.
Please, remember to answer all questions.
When answering the questions, please refer to the following scale:
1 = Strongly disagree
2 = Moderately disagree
3 = Moderately agree
4 = Strongly agree
The Italian version of the scale is available upon request from the corresponding author.
Keywords: soft skills, performance, job satisfaction, burnout, network analysis, measurement invariance, item response theory
Citation: Colledani D, Robusto E and Anselmi P (2024) Assessing key soft skills in organizational contexts: development and validation of the multiple soft skills assessment tool. Front. Psychol. 15:1405822. doi: 10.3389/fpsyg.2024.1405822
Edited by:
Abira Reizer, Ariel University, IsraelCopyright © 2024 Colledani, Robusto and Anselmi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Pasquale Anselmi, cGFzcXVhbGUuYW5zZWxtaUB1bmlwZC5pdA==