Open-access ASSESSING SOFT SKILLS FOR SUPPLY CHAIN INTEGRATION

Evaluación de las competencias interpersonales para la integración de la cadena de suministro

ABSTRACT

The development of each relationship in the supply chain involves the participation of people who must be committed to the organization’s objectives. This research aims to develop an instrument to assess the level of development of soft skills that contribute to improving supply chain integration (SCI). The proposed instrument identifies different levels of difficulty in carrying out the same action within the competencies assessed, with the support of the Item Response Theory (IRT). This instrument can be used to identify the level of qualification of employees to promote SCI and serves as a guide in the development of training to qualify teams, periodic evaluations, and selection processes, making it possible to align the competencies of new hires with the work policy adopted by the company. Future research could include new competencies to expand the scale’s assessment capacity and keep up to date with market demands.

Keywords: Supply Chain Integration; soft skills; Item Response Theory; human resource management; evaluation.

RESUMEN

El desarrollo de cada relación en la cadena de suministro implica la participación de personas que deben estar comprometidas con los objetivos de la organización. Por ello, esta investigación pretende desarrollar un instrumento para evaluar el nivel de desarrollo de las competencias blandas que contribuyen a mejorar la integración de la cadena de suministro (SCI). El instrumento propuesto identifica diferentes niveles de dificultad en la realización de una misma acción, dentro de las competencias evaluadas, con el apoyo de la teoría de respuesta al ítem (TRI). Este instrumento puede utilizarse para identificar el nivel de calificación de los empleados para promover la SCI y sirve como guía en el desarrollo de capacitaciones para calificar equipos, evaluaciones periódicas y procesos de selección, posibilitando alinear las competencias de las nuevas contrataciones con la política de trabajo adoptada por la empresa. Futuras investigaciones podrían incluir nuevas competencias para ampliar la capacidad de evaluación de la escala y mantenerse al día con las demandas del mercado.

Palabras clave: Integración de la cadena de suministro; soft skills; teoría de la respuesta al ítem; gestión de recursos humanos; evaluación.

RESUMO

O desenvolvimento de cada relacionamento na cadeia de suprimentos implica a participação de pessoas que devem estar comprometidas com os objetivos da organização. Dessa forma, esta pesquisa visa desenvolver um instrumento para avaliar o nível de desenvolvimento de habilidades comportamentais (soft skills) que contribuem para a melhoria da integração da cadeia de suprimentos (ICS). O instrumento proposto identifica diferentes níveis de dificuldade na realização de uma mesma ação, dentro das competências avaliadas, com apoio da Teoria de Resposta ao Item (TRI). Esse instrumento pode ser utilizado para identificar o nível de qualificação dos colaboradores para promover a ICS e serve como guia no desenvolvimento de treinamentos para qualificação de equipes, avaliações periódicas, bem como em processos seletivos, permitindo alinhar as competências dos novos contratados com a política de trabalho adotada pela empresa. Pesquisas futuras podem incluir novas competências para ampliar a capacidade de avaliação da escala e manter-se atualizada com as demandas do mercado.

Palavras-chaves: Integração da cadeia de suprimentos; soft skills; Teoria de Resposta ao Item; gestão de recursos humanos; avaliação.

INTRODUCTION

Supply chain efficiency depends on the integration of intraand inter-organizational processes to ensure the continuous flow of materials, processes, and information. Companies that apply supply chain integration (SCI) cope better with market uncertainties and are able to react more quickly (Basana et al., 2024). However, each relationship, whether internal or external to the focus company, involves the participation of people who must be committed to the organization’s objectives. Therefore, it is necessary to develop skills that can strengthen productivity and excellence in increasingly complex and dynamic work environments (Pekkanen et al., 2020).

Supply chain professionals influence a company’s awareness of the value of its collaborative relationship with suppliers and customers, as they demonstrate that they know the importance of mutual commitment and cross-functional participation, further strengthening trust, and providing greater chances of developing closer relationships with their partners (Barnes & Liao, 2012). Therefore, to ensure the success of the SCI, companies must fully invest in the human dimension, developing training programs that broaden the skills of their employees, as well as developing a recruitment and selection process that manages to capture the necessary skills, such as flexibility, communication, trust, commitment, teamwork, and adaptability to change (Flöthmann, 2018; Gómez-Cedeño et al., 2015; Huo et al., 2015). In turn, professionals need to know what skills the market demands so that they can keep up to date.

However, there is a shortage of professionals with the skills to manage strategically important supply chain processes (Ellinger & Ellinger, 2014; Flöthmann, 2018; Sirisomboonsuk & Burns, 2023). The problem of finding professionals with skills that promote integration begins with the difficulty of identifying the necessary skills. No comprehensive framework can be adopted to develop supply chain competencies (Dubey et al., 2018; Fantozzi et al., 2024). This leads to insufficient resources to develop technical and strategic training for SCI professionals (Fernando & Wulansari, 2021).

Several studies have discussed the reformulation of management training courses to develop the necessary skills, reducing the shortage of qualified labor (Sirisomboonsuk & Burns, 2023; Wagner et al., 2019). However, the process of reformulating training courses is slow and may not reach the professionals who are already in the market. Moreover, even if they are taught in the classroom, some skills will only be well developed with experience and can be adjusted to the context and specificities of each sector (Bak et al., 2019).

Globalization and continuous technological innovations are making some knowledge and skills redundant, while the demand for new skills is increasing (Munkácsi & Krisztina, 2023). Technical skills and knowledge are necessary but not sufficient to guarantee optimum performance (Fantozzi et al., 2024). In this scenario of rapid change, soft skills are more adaptable and transferable, while hard skills can become obsolete and non-transferable from one situation to another (Bak et al., 2019).

In today’s dynamic environment, companies prefer to hire people with the right skills to adapt to change and learn rather than having people with a lot of technical knowledge who may soon become obsolete (Fantozzi et al., 2024). The challenge is to quickly integrate market demands with training courses so that they can adapt quickly and keep up to date (Munkácsi & Krisztina, 2023). In this sense, it is understood that education is a continuous process, and training in new skills must also be brought into organizations to ensure continuous learning that goes beyond formal education (Wagner et al., 2019).

Therefore, this research aims to develop an instrument to assess the level of development of soft skills that contribute to improving supply chain integration (SCI). It is understood that the development of specific skills is more complex than the development of technical skills, as they involve human behavior. In this sense, the proposed instrument identifies different levels of difficulty in carrying out the same action within the skills assessed, with the support of Item Response Theory (IRT). One of the main differentials of using IRT in this research is the possibility of including new items and skills, allowing the instrument to be expanded and constantly updated.

This instrument can be used to identify the level of qualification of employees to promote SCI and serve as a guide in the development of training to qualify teams, periodic evaluations, and selection processes. It allows the competencies of new hires to be aligned with the company’s work policy. In addition, it can be used to guide training courses and supply chain professionals seeking to be aligned with market demands.

SOFT SKILLS IN SUPPLY CHAIN INTEGRATION

Human resources are considered a key factor in the success of companies, as they transfer the needs of the market and consumers to the projects. Therefore, training aimed at integration and information exchange improves the quality of SCI (Quang et al., 2016).

By observing behaviors that indicate employees’ ability to work together, it is possible to identify the causes of the success or failure of SCI, promoting training aimed at the limitations of each work group. Market expectations concerning the degree of development of each skill can vary, and it is necessary to adapt, bearing in mind that soft skills have been primarily in demand and more difficult to develop in training processes (Munkácsi & Krisztina, 2023). In addition, some skills may be easier to develop than others. Thus, it is important to have an instrument that helps develop these skills and allows us to monitor the evolution of professionals in this respect.

SCI requires a change in day-to-day decision-making strategy, practices, and human interaction, which requires a change in the mindset and behavior of people within the organization. In addition to functional and logistical skills, managers need to develop a network perspective and manage cross-functional teams within and between organizations, considering costs, services, and time-based performance indicators (Wagner et al., 2019). This shift in perspective is increasingly present in the recruitment and selection processes for new employees, where key hiring decisions tend to focus on soft skills, while hard skills have become a standard requirement (Bak et al., 2019).

The literature presents a wide range of soft skills that can contribute to increasing the level of SCI, and some stand out because of the frequency with which they are mentioned (Figure 1). Some are more geared toward managerial roles but can be identified in any role, such as leadership, conflict management, coordination, and problem-solving, in addition to other skills necessary for any role, such as communication, trust, collaboration, cooperation, flexibility, teamwork, commitment, and knowledge sharing (Arantes et al., 2024).

Figure 1
Soft skills that contribute to SCI improvement

Commitment is mainly reflected in a strong belief in and acceptance of the organization’s goals and values, a willingness to exert considerable effort on behalf of the company, and a strong desire to remain part of the organization (Mowday & Steers, 1979). Building commitment takes time and skill on the part of managers. It creates an emotional bond between the employee and the organization, reducing turnover (Živković et al., 2021). Although employee commitment is rarely considered an antecedent to SCI, it explains several performance measures, such as flexibility, delivery, quality, inventory, and customer satisfaction, increasing internal integration and, consequently, external integration (Alfalla-Luque et al., 2015).

Trust consists of believing in the integrity of the partner (Ravand & Saremi, 2015). Uncertainty and risk are inherent to integration processes and negatively influence decision-making. However, trust is the willingness to take risks, making uncertainty a driving force for the growth of trust in relationships (Tejpal et al., 2013). Perceived trust motivates partners to act voluntarily, becoming a source of competitive advantage for the supply chain (Ha et al., 2011).

Communication is essential for understanding and cooperating closely with suppliers and customers, enabling full identification of customer requirements (Zhao et al., 2011). Sharing information clearly and objectively is essential to ensure the continuous flow of processes in the supply chain, as performance is not only driven by obtaining information and knowledge but also by how it is assimilated and applied in decision-making. Sharing information relevant to the process improves collaboration and, consequently, the operational efficiency and responsiveness of the entire supply chain, reducing errors and waste (Yu et al., 2013).

Cooperation consists of mutually understanding interactions between partners where there is a perception of each other’s needs, working together to increase customer satisfaction (Wei et al., 2012). The success of collaborative behavior is based on individual skills in interdependent roles, and it is important to develop interpersonal relationships in order to achieve positive results (Topolšek et al., 2010). Cooperative behaviors are reflected in the flexibility and adaptability of the partners, which are intensified with trust because of the long-term orientation in the relationship (Wei et al., 2012). Each team member has their own predefined activities, but collaboration involves the team’s ability to self-evaluate, check for work overload in any member, and redistribute activities (Salas et al., 2005). Coordination consists of organizing the activities of two or more groups so that they are aware of each other’s activities and work together efficiently (Singh, 2011). Activity coordination requires identifying that conditions have changed and responding to unexpected demands with a new strategy (Ibrahim et al., 2015; Salas et al., 2005).

Leadership consists of taking responsibility, clearly defining goals, guiding the team, inspiring confidence and security, delegating tasks and responsibilities, managing conflicts, solving problems, and seeking continuous improvement (Salas et al., 2005). The leader plays a very important role, as they have the power to strengthen or weaken the emotional bonds that give the team consistency (Madani & Rungsrisawat, 2019). Especially in times of crisis, such as the COVID-19 pandemic, the leader has a very important role in keeping teams active and motivated, helping people to adapt to the new working reality while remaining focused on the objectives initially set (Faria & Arantes, 2023).

Flexibility is the ability to identify that conditions have changed and respond to unexpected demands by defining a new strategy (Ibrahim et al., 2015; Salas et al., 2005). Adaptive individuals are more effective when the company’s objectives change, as they can learn any technical skill (Bak et al., 2019; Sirisomboonsuk & Burns, 2023). This characteristic requires a global vision of the team’s tasks, how changes can alter members’ roles, and the ability to recognize that changes are taking place (Salas et al., 2005).

Even if companies do not adopt formal team structures, collective work is closely associated with human behavior, and its characteristics must be observed (Montanari & Pilatti, 2012). However, people need to be willing to work together and know what kind of behavior is required to achieve these results. The level of integration depends on the level of collaborative behavior of employees from the various functional areas within the company (Topolšek et al., 2010).

METHODOLOGICAL PROCEDURES

Data collection

The basic instrument of the scale to measure the level of ability of individuals to contribute to increasing the level of SCI was developed based on the literature review, which went through three stages of theoretical evaluation: semantic analysis, expert analysis, and pilot test.

The first stage aims to check that the items are understandable to all members of the population. The semantic analysis must include individuals from the lowest to the highest ability levels to ensure that the items are understandable to all members and, at the same time, the language is not too simple (Pasquali, 1998).

The second stage involved the participation of six experts in the field of supply chain management and people management to assess how the questions were asked and their relationship with the skills the item was intended to assess. After making the adjustments indicated in the first two stages, a test version of the final questionnaire was applied to a small group of the target sample to identify the functionality of the chosen application method.

At the end of this stage, the final version of the instrument was approved by the Ethics Committee for Research with Human Beings of the Federal University of Santa Catarina (CEPSH-UFSC), with substantiated opinion no. 2.902.377.

The final instrument consists of 41 objective questions (Table 1), each with five response options (never, rarely, sometimes, most of the time, always). If the respondent did not have enough information to answer a particular item, they could also choose the option “I do not know, or I prefer not to give an opinion.”

Table 1
Research questionnaire

This survey applies to all in the job market or looking for work. The questionnaire was uploaded to the Survey Monkey platform and distributed via social networks and email lists. A total of 1,273 responses were received, 51% female and 49% male. The applicability of the instrument to different areas of activity is reflected in the sample, which includes professionals from different areas of activity, such as engineering, administration, accounting, physiotherapy, law, and psychology, among others. In addition, 33% of the sample comprises students, reflecting that the survey is also applicable to those who do not yet have experience but wish to prepare themselves to meet the skills required by the market.

As for the level of education, 54% have incomplete or ongoing higher education, 16% have completed higher education, 20% have a postgraduate degree, and 9% have technical, secondary, or elementary education.

Building the scale

The construction of scales using Item Response Theory (IRT) does not require representative samples of a specific population. Within the scale’s target audience, it is only necessary to obtain respondents at all levels of the scale in order to correctly calibrate all the items. The criteria that most influence the definition of the appropriate sample size in IRT are the model to be used and the distribution of respondents in terms of their level of ability in relation to the latent trait.

The IRT proposes ways of representing the relationship between the probability of an individual responding positively to an item and their level in this latent trait. In the case of this research, it is possible to establish a measure of people’s level of ability to contribute to the improvement of SCI based on the probability of the individual responding positively to the items that represent this type of behavior.

In IRT, the construct (or what is being measured) is called a latent trait since this characteristic is considered the basis that influences the answers given to the items that seek to measure this construct (Reise et al., 2005). In practical terms, IRT makes it possible to measure aspects that cannot be observed directly by estimating discrimination and difficulty parameters for a set of items.

This theory aims to determine an information function for each item, considering that each one provides a certain amount of information for different intervals of the latent trait, thus forming a measurement scale where a level of difficulty is assigned to each item (Reise et al., 2005). Thus, the conclusion does not depend on the instrument as a whole but on each item individually.

There are several item response models. The choice of the most appropriate one depends on the nature of the item (whether dichotomous or polytomous), the number of populations involved, and the number of latent traits being measured. The model used in this research was the Graded Response Model (GRM), proposed by Samejima (1969). In this model, the probability of the individual j with ability θ choosing a category k is given by equation 1, where ai is the estimated discrimination parameter for each item i, bik represents the difficulty of the k-th category of item I, and θ represents the latent trait.

(1) P i , k ( θ ) = 1 1 + e - a i ( θ j - b i , k ) - 1 1 + e - a i ( θ j - b i , k + 1 )

By applying this model, it is possible to define a level of difficulty for each response category of a set of items with graded responses, positioning them on an interpretable scale. Based on the positioning of the items on the scale, you can identify what each skill level represents and define the steps to progress from one level to the next.

The discrimination (ai) and difficulty (bi) parameters were estimated using Multilog® software. The discrimination parameter shows the item’s correlation with the latent trait, serving as an indicator of the item’s “quality.” Its value should be positive and ideally greater than 0.70. The higher the value of a, the greater the item’s ability to discriminate. The difficulty parameter of each response category indicates, as its name suggests, how difficult the item’s response categories are. The response categories of items with a higher “b” parameter demand a higher level of skill from individuals in order to contribute to improving SCI.

According to the GRM, items with four answer options can be positioned on up to three levels of the scale, while items with three categories can be positioned on up to two levels as long as they meet the probability criteria defined for positioning.

The point on the scale defined for setting the category is where the probability of answering is ≥ 60%, with the probability of answering the immediately preceding level being ≤ 50%, considering a distance of 0.5 standard deviations between the levels. Only items with parameter a ≥ 1 are positioned on the scale.

Once the positioning of the items on the scale has been defined, an interpretation is made for each level, defining what it means for the individual to be situated at that point on the scale and the criteria to be positioned at higher levels.

RESULTS AND DISCUSSION

Parameter estimation

The instrument used five response categories, but the frequency of responses in the first categories was very low. Although all the items were answered in all the categories, each category must have at least 30 responses in order to be able to estimate the parameters. Therefore, items 1, 2, 3, 4, 5, 7, 8, 13, 14, 15, 18, 24, 30, 31, 32, 33, 36, 37, 38, 39, and 41 had the first three categories of answers (never, rarely, and sometimes) grouped into one. Items 6, 9, 10, 11, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 34, 35, 36, and 40 had the first two categories (never and rarely) grouped, adding the responses from the indicated categories to better fit the data to the model.

This result could be attributed to the low level of difficulty of the items for the population under study, considering the high level of education of the respondents. Another possibility is that the participants are motivated by a cultural tendency to give “socially acceptable” answers, leading them to mark the option they believe to be the most acceptable answer without adequately assessing their attitude to each situation (Podsakoff et al., 2003). In addition, individuals often have a distorted view of their ability, making an overly positive assessment of themselves.

The reliability analysis of the instrument, measured using Cronbach’s Alpha, indicated an α = 93%, which indicates a high level of reliability of the data. All the items were calibrated correctly, reaching convergence at 74 cycles. The discrimination parameters were above 0.70, except for items 1 and 4, indicating that the items were able to discriminate between respondents in terms of their level of ability to contribute to increasing the level of SCI. As such, no items needed to be excluded in the calibration process. The estimated discrimination, difficulty parameters, and the standard errors for each estimate are shown in Table 2.

Table 2
Estimated item parameters

Among the items with the highest discrimination parameter (a), items 29 (1.890), 40 (1.745), 27 (1.693), 20 (1.613), and 17 (1.604) stand out. This parameter reflects the quality of the items used to assess the observed behavior. The items mentioned are related to leadership skills (27, 29), teamwork (40), and cooperation (17, 20). The instrument provides good indicators for assessing these skills.

Among the items with the highest level of difficulty, items 10, 26, and 12 stand out in the “always” category since the latent trait is cumulative. Item 10 asks at what level the respondent controls the amount of informal conversation at work. This point deserves attention from managers, as spending too much time on informal conversations can mean a loss of productivity. However, this does not mean that there should not be any informal conversation in the workplace; rather, a balance should be sought so that there is a friendly coexistence without affecting the performance of both parties. In some cases, informality can even be beneficial for integrating internal and external relationships, as it makes people closer and more open to dealing with more critical issues. Gligor and Autry (2012) point out that relationships between companies are affected by how their employees relate to each other, even outside the workplace.

The second item with the highest difficulty parameter, item 26, asks whether the respondent believes that people always see them as a leader. One aspect of this item’s difficulty is that it asks about someone else’s opinion of the respondent. The item could ask whether the individual sees themselves as a leader. However, one of the aspects of leadership is to self-assess how the actions taken influence the other team members. Thus, this item aims to propose a slightly more in-depth assessment of the respondent’s leadership skills.

The next question that stands out among the highest levels of difficulty in the test is item 12: “When you disagree with a superior, do you present your point of view?” This behavior is associated with communication skills in the sense of assessing whether the respondent is able to have an open conversation with a superior, even when they disagree with their point of view. This does not mean ignoring the hierarchy. On the contrary, presenting a different point of view to improving supply chain activities demonstrates involvement with the organization’s objectives. However, managers must be open to receiving criticism and discussing ideas so that behaviors of this kind contribute to increasing the level of SCI.

Positioning of items on the scale

Once the parameters have been estimated, the next step is to position the item categories on the scale. To place the items on the scale, only those with a discrimination parameter ≥ 1.00 were considered. For this reason, items 1, 3, 4, 7, 9, 10, 11, 12, 19, 23, 24, and 37 were not placed on the scale.

Table 3 shows the position of the items on the scale, relating them to the skills that promote the improvement of SCI. Out of the skills listed in the initial set of items, only the coordination skill does not make the final scale. This is a very strong skill in management positions, which requires coordinating activities between functions to ensure integration in the internal and external links of the supply chain. As the instrument developed for this research is aimed at people in all positions within the organization, only two items on coordination were included in the instrument, representing behaviors that would fit all positions. It is natural that the final scale has little information on this skill.

Table 3
Positioning of items on the scale

You can observe the order of the items by difficulty level, considering the response frequency to the respective item. In some cases, there is a significant difference between the categories. Item 26, for example, was placed at level -1.5 when answered “sometimes,” at level 0 for “most of the time,” and at level 2 for “always.” This indicates that to reach the maximum level in this aspect of leadership ability, it is necessary to move up several levels on the scale. Some items show a smaller range of variation on the scale, such as item 40, which goes through levels -2.0, -1.0, and 0.5, which may be an easier behavior to develop. This aspect reflects how soft skills are more complex to develop (Munkácsi & Krisztina, 2023). Although a person has attitudes that reflect a certain skill, performing it at an optimum frequency requires more effort. Similarly, there are other behaviors related to the same verified skill, some not yet included in this first stage of the research, which may present a higher level of difficulty.

People who never or rarely perform the behaviors presented in the instrument do not contribute to improving the level of SCI. They are likely to find it difficult to stay in the job market, and it is recommended that they seek professional qualifications to adapt to the needs of companies today. The literature shows that it is not uncommon to find people with a low level of development in these skills, given the rapid changes in the market and the difficulty of training courses and professionals to keep up with these demands (Fernando & Wulansari, 2021).

Based on the positioning of the items on the scale, it is possible to interpret what it means to be at each level, considering what the set of items at that level represents in the assessment of the latent trait. The respondents’ assessment, depending on the level they are at on the scale, can be classified as having a low, medium, or high contribution to increasing the level of SCI. Table 4 shows the interpretation according to this classification based on the evaluation of the items placed in each interval.

Table 4
Scale interpretation

Up to level -2.0, there is a low contribution to increasing the level of SCI, with most of the behaviors at these levels only being performed “sometimes.” From this level up to level -0.5, a medium level of contribution is identified, with most of the behaviors being carried out “most of the time” and “always.” From level 0 onward, there is a high level of contribution to the improvement of the SCI, with practically all of the behaviors being “always” carried out by the people positioned at these levels, including those related to suppliers and customers.

Although it is an excellent indicator for SCI to have people positioned at the maximum level of the scale, reaching this point does not mean that the individual has nothing more to evolve in this sense. Many other behaviors that contribute to improving SCI can be added, including the items in this survey instrument that were not fixed on the scale. In addition, expectations of competencies are constantly changing, and other skills can be added to the instrument, broadening its evaluative potential (Munkácsi & Krisztina, 2023).

CONCLUSIONS

The rapid evolution of the market forces companies to adapt quickly to remain competitive, not just in their individual activities but throughout their supply chain. Mastering technical skills and knowledge is no longer enough to deal with constant change. It is necessary to develop soft skills and behavioral abilities that contribute to better performance of activities and provide a more consistent and lasting competitive advantage.

Therefore, companies need to know how to identify and assess these soft skills to build their teams according to the necessary abilities, whether in recruitment and selection processes or to carry out training with the workforce already hired. Likewise, training courses and supply chain professionals need to know what skills the market demands to keep up to date.

Recent research has discussed the inclusion of soft skills in the training process for supply chain managers, but this is a slow process and may not reach professionals already in the market. Although this knowledge is included in all training courses, companies need ways of assessing their work teams.

This research aimed to develop an instrument to assess the skill level of individuals in an organization in order to help raise the level of SCI. A questionnaire was applied that presents behaviors related to the skills of commitment, trust, communication, coordination, leadership, flexibility, conflict management, and teamwork. Although it was developed with a focus on SCI, all companies are part of a supply chain, and all sectors of a company need to work in an integrated way to improve the chain’s performance. As such, the skills that favor integration relate to various sectors of companies, such as marketing, purchasing, or production. The improvement of these sectors and the better integration between them implies an improvement in SCI.

Based on this tool, it is possible to list behaviors by level of difficulty, identify which skills are most difficult to develop, develop specific activities, and target the specific needs of each work group.

Future research could include new behaviors related to the skills already discussed, seeking to achieve higher levels of development in each of them. In addition, other skills could be included in the scale already developed.

Although it is possible to identify many other skills in the literature, including them in a single phase of the research is unfeasible, as it would generate an excessively long questionnaire. Applying instruments that are too long impairs the quality of the responses, reducing the reliability of the results. In this respect, using IRT is an advantage, as it is possible to add new items to the already calibrated questionnaire. Thus, it is enough to develop new items in the same format adopted in this investigation and apply them to new samples, associating them with the existing instrument. However, this assessment tool represents a major step forward, as it is the first to assess the individual competencies needed to improve SCI.

Another aspect to be considered in future research is to apply the questionnaire to the manager or team leader of the person being assessed. This could avoid the bias of overly positive self-assessment. Also, comparing whether there are significant differences when the questionnaire is answered by someone else could provide new insights.

  • Evaluated through a double-anonymized peer review.
  • The Peer Review Report is available at this link

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Edited by

  • Associate Editor:
    Arda Yenipazarli

Publication Dates

  • Publication in this collection
    02 Dec 2024
  • Date of issue
    2024

History

  • Received
    30 Jan 2024
  • Accepted
    31 July 2024
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E-mail: rae@fgv.br
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