Influence of Psychological Capital on the Gross Performance of Telesales Salespeople in a Wholesaler-Distributor Company

Impacto do Capital Psicológico no Desempenho Bruto dos Vendedores de Televendas em uma Empresa Atacadista Distribuidor

Abstract

Raw performance and the factors that influence it is a relatively rare topic in Organizational and Work Psychology, especially in the context of Positive Organizational Behavior. This study aimed to verify whether psychological capital predicts the performance of salespeople in a wholesaler-distributor. The sample consisted of 237 salespeople who responded to the Inventory of Psychological Capital at Work (ICPT-25). The model test was performed through Structural Equation Modeling (SEM), which indicated that the proposed model, in which psychological capital predicted gross performance, was not corroborated. We argue that situational variables can be more robust predictors than individual variables. We propose that raw performance should be investigated with independent variables of a situational nature, such as leadership.

Keywords:
psychological capital; performance; telesales

RESUMO

O objetivo deste estudo foi verificar se capital psicológico pode influenciar o desempenho bruto de vendedores de televendas pertencentes a um atacadista distribuidor. A amostra final constituiu-se de 237 vendedores, sendo a maioria do sexo feminino (82,7%), com idade média de 30 anos (DP = 7,63), com tempo médio de trabalho na organização igual a 42,5 meses (DP = 47,5 meses) e o tempo médio na função de 46,7 meses (DP = 45,5 meses). O grau de escolaridade predominante é o segundo grau completo (40,5%). O instrumento utilizado foi o Inventário de Capital Psicológico no Trabalho (ICPT-25), composto por quatro fatores e com Alpha de Cronbach superior a 0,70 para cada um. A confiabilidade das escalas para a amostra do estudo também mostrou coeficientes superiores a 0,70. Os dados foram analisados por meio da estatística descritiva e o teste do modelo através da Modelagem por Equação Estrutural (MEE). Os resultados indicaram que as correlações entre capital psicológico e desempenho bruto foram baixas e as maiores correlações foram entre otimismo e esperança e autoeficácia com esperança. A MEE mostrou que o modelo proposto não foi confirmado revelando que capital psicológico não é um preditor significativo de desempenho dos vendedores que compuseram esta amostra. Recomenda-se que, em futuras pesquisas, desempenho bruto seja investigado com variáveis independentes de caráter situacionais, como liderança, por exemplo. O estudo apresentou contribuições para o meio acadêmico ao investigar desempenho bruto como variável dependente, o que é inovador dentro da POT. Para os gestores, o trabalho discute os fatores que podem determinar o desempenho visando o cumprimento de metas nas organizações.

Keywords:
capital psicológico; desempenho bruto; vendedores

According to Zanelli et al. (2014), the study of work has become a fertile field for social and behavioral sciences development. It became a transdisciplinary arena that facilitated the dialogue between these sciences. The authors state that performance, worker health, quality of life standards, and the impact of employment and working conditions on human life correspond to central research questions in Work and Organizational Psychology (WOP).

As psychology evolved as a study and application area, it focused on human deficits, weaknesses, and negative aspects (Palma et al., 2007). Psychology is not just the study of pathology, weakness, and damage but also quality and virtue; treatment is not just about correcting what is harmful but nurturing what is best (Seligman & Csikszentmihalyi, 2000).

Authors such as Martin Seligman criticize this sharply negative view of psychology, warning the need to redirect it to the positive side. Therefore, a new perspective in Psychology emerges, known as Positive Psychology, which focuses on studying strengths, virtues, and more positive aspects of life to develop self-fulfillment and the meaning of life for people who are already healthy and happy (Seligman & Csikszentmihalyi, 2000). From applications of Positive Psychology to the organizational context emerges Positive Organizational Behavior (COP), a concept proposed by Luthans (2002a) which directs the studies of psychological capabilities that influence organizational performance (Palma et al., 2007).

Organizational performance is one of the main ways people contribute to achieving organizations' goals and their own goals, with significant implications for career, well-being, and satisfaction (Bendassolli & Malvezzi, 2013; Imran & Shahnawaz, 2020). Therefore, reflecting on the performance concept, origins, evaluation methods, and the best ways to develop or improve it is highly relevant to academics and businesses.

Although there are several publications on performance, there is no consensus in the literature on its concept and measure (Fogaça et al., 2018). Therefore, it is essential to discuss the relevance of this topic for organizations that need improved performance to achieve their goals, deliver products and services and obtain competitive advantage (Queiroga, 2009).

As for sales performance, there are also inconsistencies in definition, measurement, and factors that would influence salespeople's performance, considering several aspects (Verbeke et al., 2010). Knowledge is related to sales as the leading influencer of performance, followed by the degree of adaptation, the ambiguity of roles, cognitive skills, and engagement at work, as reported in a meta-analysis performed by Verbeke et al. (2010).

In this study, we investigated whether Psychological Capital can be one of the factors influencing gross sales performance. Psychological Capital (or psycap) is defined as a positive psychological factor composed of the integration of several positive psychological capacities, currently defined as self-efficacy, optimism, hope, and resilience (Gomide et al., 2017; Luthans & Youssef, 2004; Luthans et al., 2007).

According to Vilaça et al. (2012), investing in employees' psychological capital can benefit organizational results. They state that higher levels of individual Psychological Capital are associated with higher productivity rates, more creativity, a more substantial number of organizational citizenship behaviors, fewer intentions to leave the organization, and a decrease in counterproductive behaviors.

Luthans et al. (2010) discuss the growing evidence that Psychological Capital (psycap) is significantly related to desired employee behaviors, attitudes, and performance. Additionally, research indicates that psycap has implications for combating stress, which would help facilitate a more positive organization. Antunes et al. (2013) also state that psycap can promote confidence and quality of life at work.

Thus, reinforcing the positive Psychological Capital within organizations is essential for workers' well-being and increasing results (Vilaça et al., 2012). However, there is a lack of studies investigating whether the development of Psychological Capital has a causal impact on workers' performance, weakening empirical evidence (Antunes et al., 2013; Tüzün et al., 2018). Gaps of literature justify the relevance of novel studies to better understand the relationship between psycap and performance in organizations.

Performance

Studies on work performance and variables have been developed for decades. Around the 1970s, researchers faced clarifying and expanding the job performance concept. Advances are mainly focused on specifying predictors and processes associated with individual performance (Campbell, 1990; Sonnentag & Frese, 2002). In the 1990s, the first attempt to structure a theoretical model to understand performance better appears in Campbell's work (Queiroga, 2009).

Campbell et al. (1993) distinguish performance determinants, predictors, and components. They proposed that determinants directly influence performance, while predictors have an indirect influence. Regarding determinants, performance represents a function of three individual determinants: declarative knowledge, procedural knowledge, and skills and motivation. According to Bendassolli and Malvezzi (2013), although this model has criticisms for not emphasizing situational factors, it remains a landmark in studies on work performance.

In 1993, Waldman and Avolio (1993) proposed a model of professional performance influenced by aspects associated with the environment, motivational factors, and ability. The first considers broader variables of the organizational system, such as leadership and working conditions, and the last two concern the individual level. The objective is to understand how temporal and evolutionary aspects are associated with performance. To this end, they suggest that the relationship between performance and its antecedents (motivation, skills, contextual factors) evolve, with variables having a temporal dimension. Therefore, this model reinforces the importance of context for understanding the performance and considering it as a dynamic and evolutionary phenomenon (Bendassolli & Malvezzi, 2013).

Frese and Zapf (1994), developed the model of active performance based on action regulation theory and two essential postulates: humans are beings of action, and this action is always guided by a goal so that they manage to regulate within their reach (Bendassolli & Malvezzi, 2013). According to this model, one who performs can change circumstances according to his interests, which makes him invest energy and persist even when facing environmental difficulties.

The model by Pulakos et al. (2000, 2002) was an adaptation of Campbell et al. (1993). They identified the need to insert components for dealing with individual adaptation strategies to the new conditions and work demands (Bendassolli & Malvezzi, 2013). The main contribution is the proposition that performance is not static - people must adapt to changes at work continually.

Even considering all conceptual models on work performance, there is still a permanently perceived difficulty in understanding the performance concept. This lack of unity was confirmed by a bibliometric survey conducted by Fogaça et al. (2018). The results showed a variety of concepts and measures, with a preponderance of empirical definitions. Most of these empirical definitions were based on so-called “output measures,” mainly organizational results arising from organizational performance reports and evaluations. Most surveys employed objective measures such as sales history, test scores, and earned revenue.

In this same survey, it was also found that the focus on individual performance is predominant. Most research is still focused on investigating working conditions, with few studies including context variables (social, cultural, and environmental conditions in performance) in their analysis models (Fogaça et al., 2018).

Nevertheless, there is an agreement in performance studies regarding the need to differentiate the conceptualization of performance in terms of processes (actions, behavioral aspects) or outcomes (Bendassolli & Malvezzi, 2013; Sonnentag & Frese, 2002). From a process point of view, performance is analyzed in behavioral terms, that is, what people do while they are working. These would be the actions themselves. From an outcome point of view, performance refers to the consequences of individual actions (Bendassolli & Malvezzi, 2013).

A proposal for conceptualization, relatively well established in the WOP literature, is to delimit the concept of performance through the differentiation between task performance and contextual performance, proposed by Borman and Montowidlo (1993). According to Sonnentag and Frese (2002), task-oriented performance refers to how activities can contribute to an organization's technical issues. Contextual performance refers to behaviors not foreseen by the formal structure (Bendassolli & Malvezzi, 2013), that is, activities that do not contribute to technical aspects but offer organizational, social, and psychological support for the pursuit of organizational goals (Sonnentag & Frese, 2002).

In addition to task and context-oriented performance dimensions, Queiroga (2009) proposes two performance dimensions. The first focuses on proactive performance - a set of behaviors individuals perform in their work context to achieve organizational goals. The second focused on task performance, defined as behaviors focused on executing tasks performed in the work context.

In addition, identifying which factors (or predictors) contribute to increased performance at work is one of the essential aspects of management that can be critical to companies’ success and survival. For Fogaça et al. (2016), it is not possible to identify a clear trend when identifying performance predictors. Numerous variables are researched, such as organizational citizenship, personality traits, related team themes, and task elements. The most cited are work commitment, Psychological Capital, mental power, teamwork, and autonomy concerning individual competencies.

As for performance in the sales context, the research seeks to identify the factors that most influence the salesperson's performance. Their importance may vary according to the product type and the context where sales are made (Donassolo & Matos, 2012). From two meta-analyses (Churchill et al., 1985; Verbeke et al., 2010) on the influencers of salespeople's performance, it appears that the primary influencers of performance are: personal, organizational, and environmental factors, motivation, aptitude, levels of skills, perception of their role within the organization and the sales process, knowledge related to the degree of adaptation, role ambiguity, cognitive skills and engagement at work.

Both the studies by Churchill et al. (1985) and Verbeke et al. (2010) reached the same conclusion: there is no agreement among researchers on defining and measuring salespeople's performance, especially, which are the main factors that influence salespeople's performance. Even with this lack of consensus, Donassolo (2011) postulates it is preferable to use variables such as effort, sales skills, salesperson's perception of their role within the organization, and self-efficacy when seeking to understand sales performance's main predictors.

For this study, performance was considered a result, referring to the consequences of an individual's actions. Thus, gross performance was considered the percentage of sales target achievement.

Psychological Capital

Psychological Capital is part of the Positive Psychology perspective, a scientific and applied approach that aims to investigate people's strengths and promote their positive functioning (Snyder & Lopez, 2009). The science and practice of Positive Psychology are directed toward identifying and understanding human qualities and virtues and providing conditions for people to have a happy and more productive life (Snyder & Lopez, 2009). These human qualities and virtues are succinctly called Positive Psychological Capital or simply Psychological Capital (psycap) (Luthans, 2002b; Luthans et al., 2007).

For a psychological capacity to be encompassed in Psychological Capital, it must meet several criteria. Theoretically and empirically positive human capacities need to be measurable and defined in terms of state. This means that they are changeable and can develop, besides impacting behavior (Luthans, 2002b). The psychological capabilities that best meet these inclusion criteria are self-efficacy, optimism, hope, and resilience (Luthans et al., 2007).

Bandura (1997) defines self-efficacy as “beliefs in the individual's abilities to organize and execute the necessary course of action to produce something” (p. 3). Based on this definition, Luthans and Youssef (2004) conceptualize self-efficacy as the belief in the ability to mobilize the motivation, cognitive resources, and course of action necessary to successfully perform a specific task in each context.

Snyder et al. (1991) define hope as “a positive motivational state based on an interactively derived sense of successful agency (goal-directed energy) and pathways (goal-achievement planning )” (p. 287). In their learned optimism theory, Snyder, and Lopez (2009) state that the optimist makes external, variable, and specific attributions to explain events with a failure character and, on the other hand, makes internal, stable, and global attributions to positive events. Scheier and Carver (1985) described the definition of optimism as the stable tendency “to believe that good things will happen, instead of bad things” (p. 219), because when an objective has sufficient value, the individual will produce an expectation of achieving it.

Resilience is someone's ability, in the face of adversity, to recover or rollback from a setback or failure (Luthans, Avey, & Patera, 2008; Luthans & Youssef, 2007). Luthans and Youssef (2004) complement resilience as the belief that one can recover from conflicting and adverse situations, maintaining balance and responsibility.

These four positive psychological components, when combined, have been theoretically and empirically demonstrated to be a central higher-order factor (Luthans & Youssef, 2004). It is then assumed that there are greater correlations among constructs that compose psycap with performance and job satisfaction than of any individual component alone, as the combined motivational effects are broader and more impactful than any of the constructions individually (Luthans et al., 2007).

The concept of Psychological Capital, in the organizational context, is defined by Martins et al. (2011) as the positive mental state that contemplates the personal sense of confidence in personal success at work (efficacy), the vision of a promising future in the professional scenario (optimism), persistence in achieving professional goals and ability to redesign them (hope) and ability to strengthen and resist in the face of adversity (resilience) that may arise in professional life.

According to Luthans, Norman, et al. (2008), a relevant aspect of the conceptualization of Psychological Capital is that it can be understood as a moderately stable “state”. It is not dispositional or fixed as personality traits or central traits of self-assessment, which can be modified by experience and developed in training. This way, individual and organizational performance is improved by developing positive capacities such as self-confidence, hope, optimism, and resilience.

Many organizations focus on the development of employees' Psychological Capital because it is considered one of the critical factors in increasing the level of productivity and self-development regarding an organization's competitiveness (Nafei, 2015). Based on these considerations, the objective of this research was to test a model in which Psychological Capital explains gross performance at work in a sample of salespeople (telesales) of a wholesaler-distributor company.

Method

Description of the Company and the Studied Sector

The investigated company is a wholesaler-distributor based in the interior of Minas Gerais. Its main activity consists of serving small and medium-sized retailers and distributing around 14,000 items from the primary consumer goods, besides durable products industries in food, beverages, bazaar, stationery, electronics, housewares, building materials, veterinary products, and tools.

The company has four sales channels. The traditional sales channel, made up of autonomous sales representatives (ASRs), is responsible for the most revenue. The channels called B2B and B2C are aimed at purchases made over the internet and Telesales (sales by telephone) were where the research was conducted.

The Telesales channel has approximately 500 employees, including telemarketing operators (vendors), leadership, and administrative support. Sales are conducted in an "Active Manner" (sellers call customers to offer products) and "Receptive" (sellers receive calls from customers to purchase).

Sellers deal with sales, customers, and items per order goals according to their time in service, that is, goals are standardized and growing each month, according to the learning curve, considered every six months. After this period, goals are set according to the potential of each salesperson's customer base and also according to their history of results from previous months. Performance is monitored throughout the work period through standardized assessments conducted by leaders. The hierarchical structure of the area is composed of Managers, Coordinators, Supervisors, Monitors, and Telemarketing Operators (vendors). The company itself provided the information in this section through meetings and internal reports provided to the researchers.

Participants

For this study, the model was tested using Structural Equation Modeling (SEM). A total of 237 participants responded to the survey. According to Pilati and Laros (2007), the sample size for structural analysis should be between 200 and 500 individuals.

Most participants were women (82.7%) with a mean age of 30.2 years (SD = 7.6 years). The level of education ranged from complete elementary school to graduate school, with high school education predominating (40.5%). The average working time in the organization was 42.5 months (SD = 47.5 months), and the time in the position was 46.7 months (SD = 45.6 months). Respondents who did not occupy a management position also predominated (93.6%), and most worked the morning shift (54.0%).

Instruments

The instrument used was the Psychological Capital at Work Inventory (ICPT5), constructed and validated by Siqueira et al. (2014). It consists of 25 sentences (items), with responses given on a five-point Likert scale (ranging from 1 = strongly disagree to 5 = strongly agree), distributed across four dimensions: hope (six items); resilience (six items); optimism (five items) and self-efficacy (eight items). Precision indices, calculated by Cronbach's Alpha, were equal to 0.86 (hope); 0.87 (resilience); 0.87 (optimism), and 0.87 (self-efficacy), respectively.

An Identification Form was also used, composed of personal characteristics such as gender, age, and level of education, and functional characteristics such as time of work in the organization, time in the role, work shift, and if it was the first job.

Procedures

First, we contacted the Manager of the Telesales area via telephone to propose a meeting. After this contact, a meeting was scheduled and held with the Coordinator of the Telesales area when all the research objectives and procedures were explained, and the date for the beginning of the research was defined. These procedures were adopted to request permission for the research accomplishment.

The survey was carried out in one day in the work environment, and two working shifts. The morning shift was from 7:00 a.m. to 8:00 a.m., and the afternoon shift was from 6:40 p.m. to 7:40 p.m. The coordinator opened the meeting by explaining the research goals. Subsequently, these goals were clarified, mainly their strictly academic nature and voluntary participation. It was declared that the withdrawal could happen anytime during the survey completion. After agreeing to collaborate, participants were instructed on how to fill in the Free and Informed Consent Form, the Identification Form, and the Psychological Capital Inventory. After disclosure, each team leader distributed envelopes containing the research instrument. Thus, the forms were applied simultaneously to all salespeople and leaders who were at their jobs on the day of collection.

The Free and Informed Consent Term, the Identification Form, and the Psychological Capital Inventory were printed and identified at the bottom of the page on the right side by a number (code) that made it possible to identify the subject for data crossing. This number (code) corresponded to the same number in the table sent by the company with the raw performance results of each survey respondent. A copy of the Free and Informed Consent Term was filed following the Research Ethics Council instructions and Resolution No. 510/16 of the National Health Council.

One month after the survey completion, the Coordinator of the Telesales area sent the researchers, by email, the performance report of the salespeople for the last three months, from the start date of data collection.

Data Analysis

The answers to the questionnaires and the raw performance report formed a data file. Statistical analyses were carried out with the help of the SPSS - Statistical Package for Social Sciences, version 22. Exploratory data analysis was conducted to verify the accuracy of data entry, missing answers, extreme cases, normality of variables, and verification of the necessary assumptions for multivariate techniques.

The crossing of data on the Psychological Capital of each employee with their respective gross performance was made from a number (code) inserted in each questionnaire. This number (code) corresponded to the same number on the raw performance report for each survey respondent. However, in the database, the answers to the questionnaires were treated statistically, such that there are no names but codes. The objective was to analyze the groups' responses, not individuals, which diminished identification risks.

All statistical assumptions (normality, linearity, and multicollinearity) were verified and showed acceptable indices. The sample was described using descriptive statistics (means, standard deviations, and frequencies). Correlations between variables were verified using Pearson's correlation. Scales' reliability for this sample was calculated using Cronbach's Alpha.

After preliminary data analysis, each scale's means and standard deviations were calculated, and the correlation coefficients analyzed the level of association between variables. Subsequently, the model test was conducted through Structural Equation Modeling (SEM), a modeling technique to verify the validity of theories that propose hypothetical relationships between variables (Marôco, 2014). Parameter estimation by the maximum likelihood method (ML) was used. To verify the predictive power of Psychological Capital on performance, we used the statistical package AMOS (Analysis of Moment Structures), version 21.

Results

In the preliminary verification of the data, it was observed that omissions remained below the percentage of 5%, as defined by Tabachnick and Fidell (2019). The average of the sample data replaced the missing data. Also, no typing errors were identified in the data composition of any variable.

From the visual verification, no significant outliers were identified. According to the parameters Miles and Shevlin (2001) defined, data normality was verified through the asymmetry and kurtosis indices, which considered the indices between 1 and 2 as standard. Most of the asymmetry and kurtosis values remained within acceptable parameters. Pasquali (2015) and Tabachnick and Fidell (2019) state that in large samples, above 200 cases, asymmetry and kurtosis deviations have less impact on data normality since the larger the sample, the greater the possibility that variable means are normally distributed.

According to Hair et al. (2005), alpha values from Cronbach are consistent indicators for the scale's reliability analysis. Even without an absolute standard, Alpha values equal to or greater than 0.70 indicate acceptable reliability.

As shown in Table 1, all scale precision indices in this sample were satisfactory, as they reached values above 0.70; therefore, all variables were included in the analyses. Table 2 contains the means and standard deviations between the study variables.

Table 1
Reliability of Scales for the Study Sample

Instrument/Authors	Factors	Cronbach’s Alpha of validation	Cronbach’s Alpha for this sample
ICPT - Psychological Capital at Work Inventory (Siqueira et al., 2014)	Hope	0.86	0.71
	Resilience	0.87	0.83
	Optimism	0.87	0.78
	Self-efficacy	0.87	0.76

Table 2
Descriptive Statistics of the Study Variables

Variables	M	SD	Minimum	Maximum
Performance	100.42	52.83	29	435
Hope	4.32	0.45	2.83	5.00
Resilience	3.39	0.79	1.00	5.00
Optimism	4.32	0.55	1.80	5.00
self-efficacy	4.21	0.43	2.88	5.00

Regarding the performance dimension, the data were calculated as a percentage of the achievement of the sales target. For the company studied, values below 79.99% of sales target coverage are considered unsatisfactory performance; from 80% to 100% is considered satisfactory performance, and above 100% is considered high performance. In Table 2, it is observed that the average performance of the sector is 100.42 with a standard deviation of 52.83, indicating that most salespeople are meeting the sales target stipulated by the company, that is, they are performing satisfactorily.

As for hope, one of the Psychological Capital capacities, a higher mean (M = 4.32, SD = 0.45) was obtained than the midpoint of the response scale (value = 3). This indicates that salespeople expect to have enough knowledge to grow on the job and enough energy and experience to succeed. Also, they expect to find ways to show the boss that they do the job well and achieve their goals in the workplace.

Resilience reached a higher mean (M = 3.39, SD = 0.79) than the midpoint of the response scale (value = 3). Thus, it is observed that sellers perceive themselves as resilient. They identify as capable of getting stronger after facing layoffs, changes, losses, difficulties, intrigue, and envy at work.

Regarding optimism, the mean was also higher (M = 4.32, SD = 0.55) than the midpoint of the response scale (value = 3). Salespeople believe that everything will work out for them at work, that better days will come, and good things will happen as well as they hope to have plans and that tomorrow will be better.

The salespeople's self-efficacy (M = 4.21, SD = 0.43) also reached an average above the midpoint of the response scale (value = 3). These results indicate that salespeople believe they can solve problems, fulfill obligations, master new technology and procedures, perform complex tasks, and be creative at work. Besides, they believe they get stronger after facing challenges and that they can think of many ways to solve a problem at work.

To analyze the magnitude of the correlations between the variables, Miles and Shevlin (2001) classify the characteristic intervals for each type of correlation as low, those between 0.10 and 0.29, as moderate or median between 0.30 and 0.49, and as high values greater than 0.50. Table 3 presents the correlation coefficients (Pearson's r ) between variables.

Table 3
Pearson's Correlation (r) of Variables.

Variable	1	2	3	4	5	6	7	8
1. Age	1
2. DegreeInst	0.11	1
3. Worktime	0.53**	0.19**	1
4. TimeFun	0.46**	0.05	0.70**	1
5. Performance	0.11	0.10	0.29**	0.18**	1
6. Hope	0.00	0.06	0.07	0.02	0.18**	1
7. Resilience	0.13*	0.16**	0.11	0.09	-0.00	0.30**	1
8. Optimism	0.05	0.03	0.06	0.03	0.21**	0.70**	0.21**	1
9. Self-efficacy	0.01	0.05	0.15*	0.11	0.14*	0.62**	0.41**	0.46**

• Note. DegreeInst = Degree of Instruction. Worktime = Working Time in the Organization. TimeFun = Time in the function. **p < 0.01 *p < 0.05

The sample’s demographic and functional data evidenced a low correlation with performance, given the values for age (r = 0.11, NS), level of education (r = 0.10, NS), working time (r = 0.29, p < 0.01) and time in the function (r = 0.18, p < 0.01). Correlations between the dependent variable (performance) and the independent variables were also low, ranging from 0.14 to 0.21, the highest being optimistic (r = 0.21, p < 0.01). With hope, the correlation was 0.18 (p < 0.01), and with self-efficacy, it was 0.14 (p < 0.05). Resilience showed no significant correlation with performance.

The highest correlations in the study were between optimism and hope (r = 0.70, p < 0.01) and between self-efficacy and hope (r = 0.62, p < 0.01). Self-efficacy has a moderate correlation with resilience (r = 0.41, p < 0.01) and with optimism (r = 0.46, p < 0.01).

The model's overall goodness of fit was based on the verification of the following indices: chi-square (X²), GFI (Goodness of Fit Index), CFI (Comparative Fit Index), SRMR (Standardized Root Mean Square Residual), and RMSEA (Root Mean Square Error of Approximation). Marôco (2014) stated the goodness of fit indices, used as an analysis parameter for the results.

Table 4 shows the adjustment indices for the proposed model. The chi-square (X²) has an adequate index of 19,482 (acceptable 2 < X ² / df < 5). According to Marôco (2014), the GFI = 0.96 and the CFI = 0.94 represent sufficient adjustment index, as they are consistent with the reference (< 1). The RMSEA = 0.19 shows that the model is not sustainable, as a value lower than 0.08 is expected to be considered adequate (Maroco, 2014). As for the SRMR, the index was 0.049, presenting an adequate adjustment since the reference value is close to zero.

Table 4
Model Fit Indices

Fit index	Values
X² - Chi-square	19,482
GFI	0.968
CFI	0.947
RMSEA	0.19
SRMR	0.049

• Note. p < 0.05

Figure 1 presents the diagram with the structural representation of the model. The optimism factor presented a factor loading (r² = 0.15), indicating that it is the only factor determining performance. The other factors, hope (r² = 0.06), resilience (r² = -0.08), and self-efficacy (r² = 0.07) presented a low factor variance explanation coefficient by the latent variable.

Figure 1.
Adjusted Model

Study results indicate that Psychological Capital is not a significant predictor of gross performance for salespeople in this particular organization. Therefore, the model tested in the study, in which work performance is explained by Psychological Capital (Psycap) in a sample of salespeople (telesales) of a wholesaler-distributor company, was not confirmed. Possible reasons for this result will be discussed in the next topic.

Discussion

The results found are not in line with most findings in the literature that indicate that the four psychological capabilities of Psychological Capital - self-efficacy, hope, optimism, and resilience - positively influence people's performance in organizations. When it comes to performance, the literature generally does not bring a consensus on the main predictors of performance or even any agreement when studying sales-oriented performance.

As illustrated by Verbeke et al. (2010), based on a meta-analysis, there is no agreement among researchers regarding salespeople's performance on defining, measuring, and, mainly, the main factors that influence this kind of performance. Fogaça et al. (2016) also state that it is impossible to identify a clear trend concerning identifying performance predictors.

However, studies indicate that the most researched variables that predict performance are organizational citizenship, personality traits, team-related themes, task elements, work commitment, Psychological Capital, mental power, teamwork, and autonomy (Fogaça et al., 2016). In the present study, the results do not corroborate the literature. Several investigations (e.g., Kappagoda et al., 2014; Luthans et al., 2007; Peterson et al., 2011) indicate a significant positive relationship between Psychological Capital, performance, and attitudes in the organizational context. However, most of these studies were not conducted in the sales context nor with the gross performance variable, which leads to the assumption that in sales, individual variables, such as Psychological Capital, may be less significant in influencing raw performance than contextual ones (leadership, for example).

Another relevant issue is that raw performance is practically unheard of in the WOP field, making this study innovative but with numerous questions to be investigated. The literature shows that most research on performance seeks to study it from perceived performance (through self-evaluations or evaluations from superiors) with psychological variables, such as turnover intention, citizenship, satisfaction, and personality. Nevertheless, the amount of empirical research is still incipient for raw performance, which generates gaps and weaknesses in the empirical domain.

In this sense, even though there is an agreement among authors in differentiating the conceptualizing performance in terms of processes (that is, actions and behavioral aspects) and outcome aspects (Bendassolli & Malvezzi, 2013; Sonnentag & Frese, 2002), there is growing interest in investigating the actions or behavioral elements that lead to performance, more than the outcomes themselves. Studies on performance influencers focus much more on individual characteristics than the situational and regulation aspects. The situational perspective refers to factors in the work environment, such as leadership style, quality of interpersonal relationships, clarity of roles, presence or absence of stressors, availability of resources, and organizational culture. In contrast, regulatory factors refer to cognitive processes (Bendasolli & Malvezzi, 2013).

Thus, we raise an explanatory hypothesis for the results found that contextual variables in a sales environment, such as leadership styles, market conditions, work tools, compensation and benefits, and career plan, among others, can be more significant in influencing the gross performance than individual character aspects - Psychological Capital, for example.

The activity nature in which the research was conducted - a Call Center - may also have contributed to the results. Donassolo and Matos (2012) state that several studies seek to identify which factors most influence salesperson performance. The importance of these factors may vary according to the type of product and the context in which sales are made. Therefore, the telesales segment in a wholesaler-distributor has some particular and complex characteristics that differ from any other Call Center due to the nature of the business and, consequently, its sales process, which is complex considering a large number of items, commercial conditions, and management style. The sales process is highly dependent on internal commercial conditions (e.g., price), as well as on the economic market. Thus, the salesperson may have the necessary individual skills to sell, but if internal or external business conditions are unfavorable, they may not achieve their goals. On the other hand, timing may favor business conditions. However, if the company does not provide adequate leadership, compensation, and benefits, besides growth prospects that retain employees, ..it may not meet sales goals.

Thus, it is assumed that the specific characteristics of telesales investigated in this study influence gross performance and should be considered in research. The literature indicates that performance results also depend on factors that extrapolate individual behaviors (Campbell, 1990; Campbell et al., 1993; Sonnentag & Frese, 2002), such as the type of product, the context where sales are made, the economic market, leadership style, compensation, among others (Bendassolli & Malvezzi, 2013).

The results found here also showed a high correlation between Psychological Capital constructs. Although the Positive Psychology literature has differentiated positive capacities (Vaz, 2013), those that make up the Psychological Capital (hope, resilience, optimism, and self-efficacy) seem to be very similar and interrelated. Thus, the high correlation between the Psychological Capital constructs may be due to concept similarities, which would make it difficult for the participants to respond.

The result of this study showed that Psychological Capital was not a significant predictor of raw performance. What is hypothesized is that situational variables, mainly due to the nature of the investigated activity, may have a more robust explanatory power than individual variables. This explanatory hypothesis composes a research agenda discussed in the next topic.

Final Considerations

This study aimed to investigate a model in which Psychological Capital was considered a predictor of the gross performance of telesales people of a wholesaler-distributor company. Gross performance as a dependent variable in performance studies is a gap in the WOP literature, in which perceived performance is more frequently used. The model test result showed that Psychological Capital has no significant predictive power over raw performance.

Although the tested model has not been confirmed, this study proves relevant to the organizational behavior field and human resources management practices. It brings insights into gross sales performance in a Call Center scenario, which lacks investigation. Furthermore, according to the literature, performance has multiple dimensions and meanings. This is even more intricate when it comes to raw sales performance. Therefore, continuing efforts to deepen the discussion on performance's conceptual issue is recommended, as it directly impacts its measurement.

Gross performance as a dependent variable is practically unstudied within WOP, which hardens the structuring of a theoretical basis. Consequently, the scarcity of studies on the subject, both in national and international literature, was also a limiting factor for this study. It made it impossible to compare studies in organizations with cultures and structures like this study. Another limitation refers to the homogeneity of the sample. Composed only of salespeople from a single company with their specific cultural standards, this may have caused a decrease in instruments' reliability when compared to validation studies' reliabilities, in addition to making it impossible to generalize the results.

The researched literature evidenced that most studies relate individual performance with psychological variables, and fewer studies include context variables in analysis models. A research agenda may consist of contextual variables related to gross performance, such as leadership styles, market conditions, work tools, compensation, benefits, and career plan that may be more significant in explaining raw performance than individual variables.

In this sense, the scientific production related to the gross performance theme evidence gaps is still unexplored in the academic landscape. Studies focused on contextual performance influencers could help organizational managers to meet their goals and retain employees.

The non-significant relationship between Psychological Capital and gross performance in telesales - which contradict the literature - is also an invitation to new confirmatory studies. Especially in other telesales of wholesalers-distributors in Brazil, to delve deeper into understanding the main predictors of gross performance of sellers in this segment.

This study also contributes to the sales context, specifically in the telesales segment. Call centers grow in volume and importance in the Brazilian economy, providing high job offers. However, this is accompanied by high turnover rates, which arouses interest in this reality. Given the absence of Brazilian studies that report interventions in this field, it is also relevant to investigate the primary influencers of performance in this environment. Knowing these influencers could contribute to achieving and surpassing goals, besides reducing turnover.

We believe that the findings and contributions of this study can add to practical applications in people management. Regarding the importance of performance in work organizations in achieving their goals, maybe it is possible to make them more competitive and raise employees' professional fulfillment.

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Publication Dates

• Publication in this collection
  09 Oct 2023
• Date of issue
  2023

History

• Received
  07 June 2021
• Accepted
  08 Feb 2022