Culture and commitment as antecedents of performance in the food and beverage sector

A cultura e o comprometimento como antecedentes do desempenho no setor de alimentos e bebidas

Abstract

Purpose

The objective of the article is to verify the influence of antecedents such as culture and commitment on restaurant organisational performance and assess how they can impact managerial practices. This would explain the relationship between the exogenous variables of the model, thus providing managers with a parameter for managing the dimensions of culture, commitment, and performance.

Design/methodology/approach

Data were analysed using structural equation analysis (PLS-PM) and collected through a questionnaire with 45 assertions over a period of 2 months, obtaining 116 valid answers.

Findings

Four hypotheses were supported: H1—Innovative culture positively influences the affective commitment of restaurant employees (Γ=0.466); H8—Supportive culture influences normative commitment (Γ=0.270); H9—Supportive culture influences continuance commitment (Γ=0.293); and H10—Affective commitment positively influences bar and restaurant performance (Γ=0.293).

Managerial contributions

The model developed for this study can be used by restaurant managers to manage their employees and improve their organisational performance.

Practical implications

This model’s practical implications were that it could be used to guide restaurant decisions toward performance improvement.

Originality/value

The study fills a gap by proposing a managerial model that considers aspects of people management, such as culture and staff engagement, to improve the organisational performance of bars and restaurants.

Organisational Culture; Employee commitment; Management of bars and restaurants; People management; Food and Beverage Management

Resumo

Objetivo

O objetivo do artigo foi analisar o impacto da cultura organizacional e do comprometimento do antecedente no desempenho organizacional de restaurantes localizados na cidade de São Paulo para explicar a relação entre as variáveis exógenas do modelo, fornecendo assim aos gerentes um parâmetro de como as dimensões de cultura, comprometimento e desempenho devem ser gerenciadas.

Projeto/metodologia/abordagem

Os dados foram analisados por meio da análise de equações estruturais (PLS-PM) e foram coletados por meio de um questionário com 45 assertivas, durante o período de 2 meses, obtendo-se 116 respostas válidas.

Resultados

Quatro hipóteses foram suportadas - H1- A cultura inovadora influencia positivamente o comprometimento afetivo dos funcionários do restaurante (Γ=0,466), H8 - A cultura de apoio influencia o comprometimento normativo (Γ=0,270), H9 - A cultura de apoio influencia o comprometimento de continuidade (Γ=0,293), H10 - O comprometimento afetivo influencia positivamente o desempenho do bar e do restaurante (Γ=0,293).

Implicações gerenciais

O modelo obtido com o desenvolvimento deste estudo pode ser usado pelos gerentes de restaurantes para gerenciar seus funcionários com o objetivo de melhorar o desempenho organizacional.

Implicações práticas

As implicações práticas desse modelo foram o fato de que ele pode ser usado para orientar as decisões dos restaurantes em relação ao movimento de melhoria do desempenho.

Originalidade/valor

A lacuna que o estudo preenche é a proposição de um modelo gerencial que leva em consideração os aspectos da gestão de pessoas, como a cultura e o engajamento da equipe, para melhorar o desempenho organizacional de bares e restaurantes.

Cultura organizacional; Comprometimento dos colaboradores; Gestão de bares e restaurantes; Gestão de pessoas; Gestão em alimentos e bebidas

1 INTRODUCTION

The COVID-19 pandemic, which ravaged the world from late 2019 to early 2020, has caused several countries to take drastic measures to contain the health crisis, such as closing establishments, banning movement, wearing masks, and social distancing.

Thus, one of the sectors most affected by all the measures taken to contain the pandemic was the bars and restaurants sector, also known as Foodservice, making them bet on the junction of good products and good service mainly using new technologies (Market and Consumption in Foodservice, 2020).

In this scenario in Brazil, about three out of ten businesses were lost during the year 2020, mainly due to a lack of management in the sense of adopting restrictive measures and social isolation, both through creative solutions and to keep the business costs within the expected (Grandi, 2021).

The reflection of this is what was found in a survey developed by the National Association of Restaurants (ANR), which revealed that the Covid-19 pandemic caused the indebtedness of about 71% of establishments belonging to the sector of bars and restaurants, with accumulated debts with banks, overdue taxes and debts with suppliers (Minas & Minas, 2021).

For these reasons, one of the first measures entrepreneurs take in these cases is to reduce the number of employees, which often leads to the closure of the company. If human talents are well managed, they will be essential to the company’s recovery. Thus, the study’s problem question is: How does organisational culture, mediated by commitment, influence employee performance?

Because of this, the study objective was to analyse whether organisational culture, mediated by commitment, influences employee performance in organisations localised in Sao Paulo city.

Thus, the search for the answer to this question will allow managers to identify which types of culture and commitment favour organisational performance, leading to the profitability of the organisation, mainly through improved service and also through investment in innovative solutions for the organisation (Machado et al., 2013; Oh & Sawang, 2021; Pereira, 2014).

To answer the research question, the study was divided as follows: Chapter 2 includes a literature review on organisational culture, organisational commitment, and organisational performance, then the hypotheses of the study are: What are the specific aspects of culture and commitment that are most important for improving the performance of bars and restaurants? How can managers effectively manage their employees to improve organisational performance in the food and beverage sector? What practical strategies can bars and restaurants implement to improve their performance during and after the COVID-19 pandemic? Chapter 3 presents the study’s methodology, and Chapter 4 presents the analysis and discussion of the study.

2 LITERATURE REVIEW

2.1 Organisational Culture - Innovative, Bureaucratic and Supportive

2.1.1 Innovative Culture

The Culture of Innovation can be considered part of the organisational culture since it favours and encourages innovation and creativity. Because it is a comprehensive theme, different authors have different conceptions of the subject (Bruno-Faria & Fonseca, 2014).

For Machado et al. (2013), innovation is a strategic theme in management, and in the work environment, innovation is related to something new.

According to Dobni (2008), Innovative Culture is related to the intention to be innovative; that is, it is linked to actions and tangible results and covers behavioural and structural aspects. Furthermore, Machado et al. (2013) describe organisational innovations as changes in processes, decision-making, team relationships, and other changes to adapt the company’s behaviour in the face of changes in its scope.

2.1.2 Bureaucratic culture

Bureaucracy is technical knowledge, rational and impersonal, which guarantees high administrative efficiency. It is guided by rules that standardise and guarantee equality in the treatment of each case and precisely define relationships such as command and subordination. In addition, bureaucracy needs to make room for more flexible notions, which can cause conflicts due to the rigidity of standards and rules (Saraiva, 2002).

According to Pires and Macêdo (Pires & Macêdo, 2006, p. 20), “There are some specificities in organisational culture as an attachment to rules and routines, overvaluation of hierarchy, paternalism in relations, attachment to power, among others”. For the authors, these specificities of the public organisational culture provided by the bureaucracy influence the employees, generating a centralisation of ideas.

According to Veiga (2013), through bureaucratic processes, it is possible to ensure quality in administrative work, whether public or private, since the bureaucracy promotes organised administrative actions and appropriate conditions for the control of these actions.

2.1.3 Supportive culture

The human relations model, which corresponds to the support culture, is characterised by flexible and internal dimensioning and prevailing social relationships such as openness, trust, and human relationships (Santos & Gonçalves, 2010). This model believes in strengthening people’s motivation and sharing of decisions, giving due importance to cooperative methods, the spirit of group work, and social and human well-being (O. P. Ribeiro, 2006).

In summary, the goal of the supportive culture is for everyone to be committed and create and maintain cohesive behaviours by supporting the expression of ideas from all employees about their surroundings and their feelings related to their colleagues. Thus, the expression of ideas is promoted in an informal way, and decisions are made in a decentralised way and formed by informal contact (Pereira, 2014).

2.2 Employee commitment - affective, normative, and ongoing

2.2.1 Affective commitment

It consists of the experience of an emotional attachment on the part of the employee, identifying and getting involved with the organisation. They remain in the company by their will and strive to stay in it. Usually, this employee is unquestioningly motivated (Soares & Oliveira, 2003).

On affective commitment:

Employees’ cognitions about the social exchange relations they maintain with the organisation are considered to be the psychological antecedents of affective variables. It is assumed that when the employee believes that the interruption of this relationship would bring costs to himself, that the organisation cares about his well-being and behaves reciprocally to his spontaneous work acts, then he would be cognitively able to develop affective bonds with the work performed and with the employing organisation (Soares & Oliveira, 2003, p. 5).

According to the author, the relationship between employee satisfaction and their involvement with their work is related to the organisation’s affective commitment to them.

2.2.2 Normative commitment

It consists of the employee presenting a feeling of obligation to work. For example, he grew up with the company. Because of this fact, he feels obliged to stay for the whole history of this organisation since he has received several benefits in a reciprocal relationship in his corporate history. They remain because of obligation (Soares & Oliveira, 2003).

According to Rego and Souto (2004), normative commitment arises when the employee absorbs the organisation’s guidelines through an exchange, give and take. When the employee receives benefits from the organisation, he/she feels obliged to reciprocate this good deed.

For Farias Traldi and Demo (2012, p. 6), “In other words, normatively committed employees continue working for the organisation out of a sense of debt or even obligation”.

2.2.3 Continuity commitment

It is also called permanence or instrumental commitment. It consists of being aware of the relationship between the individual and the organisation and of the conflicts that may lead to the individual’s decision to remain or leave the organisation. However, the individual is fully aware that leaving will result in undesirable costs. They remain in the organisation because they need to (Soares & Oliveira, 2003).

According to Rodrigues, Queirós, and Pires (2016), the commitment to continuity is related to the employee’s awareness of the costs he will incur when leaving the organisation and his alternatives if this occurs. Before this awareness is achieved, the employee generates a sense of loyalty and obligation to stay in the organisation.

2.3 Organisational Performance

Organisational performance refers to the performance executed by a group, team, or individual in corporate environments. Some indicators were defined to evaluate organisational performance and assist in the creation of strategies to achieve the company’s objective. The indicators in question are profitability, productivity, quality, strategy, efficiency, fixed costs, billing, receipts, and sales growth (Dias, 2021).

According to Ribeiro, Passos, and Pereira (2018), an organisation’s performance and productivity are directly linked to two concepts: motivation and employee satisfaction.

2.4 Hypotheses

Based on the literature review, the general hypothesis of the study was developed: it is expected to find what contributes to the performance of an organisation in the perception of employees of food and beverage establishments in the city of São Paulo and 21 hypothesised to assess the correlation between the types of culture and commitment cited in this study with organisational performance.

Based on the literature review, 12 hypotheses were developed:

Hypothesis 1: Innovative Culture is positively related to affective commitment.

Hypothesis 2: Innovative Culture is positively related to normative commitment.

Hypothesis 3: Innovative Culture is positively related to continuity commitment.

Hypothesis 4: Bureaucratic Culture is positively related to affective commitment.

Hypothesis 5: Bureaucratic Culture is positively related to normative commitment.

Hypothesis 6: Bureaucratic Culture is positively related to continuity commitment.

Hypothesis 7: Affective support culture is positively related to affective commitment.

Hypothesis 8: Supportive Culture is positively related to normative commitment.

Hypothesis 9: Supportive Culture is positively related to continuity commitment.

Hypothesis 10: Affective commitment is positively related to Organizational Performance.

Hypothesis 11: Normative commitment is positively related to Organizational Performance.

Hypothesis 12: Continuance commitment is positively related to Organizational Performance.

Figure 1 demonstrates the theoretical structural model concerning the antecedents of the organisational performance of bars and restaurants, which was elaborated based on the hypotheses previously presented.

Figure 1
– Theoretical Structural Model

3 METHODOLOGICAL PROCEDURES

The objective of the article is to verify the influence of antecedents such as culture and commitment in the organisational performance of restaurants in order to assess how they can impact managerial practices. Thus, the research seeks to be explanatory because, according to Gil (2008), this type of research explains the relationship between latent variables.

This type of research was chosen since it seeks to generalise data and thus explain existing correlations between variables, leading to the study of small and large populations (Kerlinger, 1988; Selltiz et al., 2005).

To develop the research, data were collected through a cross-sectional survey using a questionnaire of 2 scales (Culture and performance) and commitment, containing 45 assertions based on a 5-point Likert scale (Babbie, 1999; Malhotra & Menezes, 2019). To perform data collection, Google Forms was sent to professionals who work in restaurants in the city of São Paulo, but the questionnaire was pre-tested with a sample of professionals to gauge the variability of the data, in addition to the semantic evaluation (Selltiz et al., 2007; Vergara, 2016).

Data were collected over a two-month period in the first half of 2021, obtaining 116 valid responses. Sample significance was tested using G-Power 3.1.9.4 software, using the F family test, linear regression, and a priori sampling. The test had a power of 0.95, F² of 0.15, F= 3.0845768 error probability of 0.05, and three predictors, which indicated a sample size of 107 responses (Ringle et al., 2014).

The data were then subjected to structural equation analysis using SmartPLS 3 software by using the PLS-PM model because it can achieve considerable levels of explanation with small samples (Dijkstra & Henseler, 2015; Henseler et al., 2015; Tenenhaus, 2008).

Mediating effects were also tested using the SmarPLS 3 software, using indirect effects analysis to determine whether there is total or partial mediation between the constructs (Wood et al., 2008).

In the next chapter, the results and discussion are demonstrated to better understand the sample from which the data was collected and the study hypotheses using the methods discussed in the current chapter.

4 RESULTS AND DISCUSSION

Firstly, the demographic data of the research were analysed using Figure 2, which presents the frequency in relation to the respondents’ age range.

Figure 2
–Age Group of the Survey Respondents

Figure 5 shows that the highest concentration of respondents is in the age bracket “Between 31 and 40 years”, comprising about 40 respondents, followed by the bracket “Between 26 and 30 years” which concentrates about 22 respondents, then comes the bracket “Between 41 and 50 years” with about 15 respondents, followed by the bracket “Between 18 and 25 years” with 14 respondents, also followed by the bracket “Between 51 and 60 years” with about 10 respondents and finally comes the bracket “over 60 years” with 1 respondent.

Figure 3
– Position/Function of the Research Respondents

Based on figure 6 it can be observed that the position or function with the highest number of respondents is that of “manager” with 39 respondents, followed by the cook with 10 respondents, then attendant with eight respondents, Kitchen assistant with seven respondents, Chef with six respondents, Nutritionist with three respondents, Consultant with two respondents, Sous Chef with two respondents, Stocker with two respondents, Waiter with two respondents, Chef 1 respondent and purchasing manager with one respondent.

Once the demographic analysis of the data had been completed, to describe the sample of research participants, the data analysis of the structural equation model was conducted by applying the PLS-PM (Partial Least Squares) model using the SmarPLS 3.3.3 software (SmartPLS, 2021).

The confirmatory factor analysis uses the theoretical model to perform the data analyses; for this, SmartPLS 3.2.7 was used (Ringle et al., 2014).

After applying the confirmatory factor analysis, the observable variables with a loading value below 0.7 were first verified, as suggested by Hair et al. (2016). In this way, the variables are parsimonious, that is, one by one since it is a model of multivariate analysis, which implies the change of values in a systemic way. The process of eliminating variables is demonstrated by means of appendix “a.”

Having carried out the elimination of the variables with values below the recommendation of Hair et al. (J. Hair Jr et al., 2016)it was passed to the analysis of paths through the analysis of significance between the dependent variable and the independent variable, whose values are suggested by Pestana and Gageiro (2014) and Garson (2013) are: Γ >= 1.96 and sig >= 0.05 which led to the parsimonious elimination of the paths, for the same reasons presented previously: 11th Round -Innovative Culture -> Continuity Commitment => Γ=0.459;sig=0.646; 12th Round - Supportive Culture -> Affective Commitment => Γ=0.549;sig=0.583; Innovative Culture -> Normative Commitment => Γ=0.715;sig=0.475; 14th Round - Bureaucratic Culture -> Normative Commitment => Γ=0.706;sig=0.480; 15th Round - Continuity Commitment => performance => Γ=0.763;sig=0.446; 16th Round - Bureaucratic Culture -> Affective Commitment => Γ=0.906;sig=0,365; Round 17 - Support Culture -> Continuity Commitment => Γ=0,905; sig=0,366, however due to the fact that there was the need to eliminate the bureaucratic culture construct, since there would be no more paths neither leaving nor arriving to the mentioned construct, it was decided to only eliminate the construct and maintain the path, to check if there would be the need to eliminate it in the next round, which did not occur because the path presented significance. In the 18th round, it was decided to keep the path Normative Commitment -> Performance => Γ=1.372; sig=0.171 because it would make it impossible to continue the analysis since it would make it impossible to make a new round since there would be a discontinuity between the factors. However, the hypothesis linked to this path was not supported.

With the finalisation of the elimination of variables from the model and the analysis of the paths, it began by verifying the quality of the model, first by Pearson’s coefficient of determination the R² with the following values obtained: Affective Commitment => β=0.201; Continuance Commitment => 0.051 and Performance = β=0.069, which represents a medium effect for the first two constructs and a small value for the last construct (Garson, 2013; J. Hair Jr et al., 2016; Ringle et al., 2014).

Having done Pearson’s R² analysis, Cohen’s effect analysis was carried out F² whose values found were: Affective Commitment -> Performance 0.084 (small); Innovative Culture -> Affective Commitment => 0.264 (medium) and Innovative Culture -> Continuance Commitment => 0.042 (Medium) (Cohen, 1988; Ringle et al., 2014).

Once the construct quality analysis was completed, the construct reliability analysis was performed using the following analyses: Cronbach’s Alpha > 0.6, Composite Reliability > 0.6 and Average Extracted Variance (AVE) > 0.5 (J. F. Hair Jr et al., 2010; Ringle et al., 2014). Table 1 will be used to make these evaluations so that it is possible to verify the measures:

Table 1
– Reliability and Validity of the Constructs

	Cronbach’s alpha (CA)	rho_A	Composite Reliability (CR)	Average Variance Extracted (AVE)
Affective Commitment	0,884	0,897	0,945	0,896
Normative Commitment	0,762	0,834	0,840	0,642
Continuity Commitment	0,721	0,721	0,877	0,782
Innovative Culture	0,865	0,898	0,897	0,563
Supportive Culture	0,950	0,955	0,959	0,770
Performance	0,942	0,946	0,954	0,777

Source: Prepared with Research Data (2021)

Table 1 shows the continuity of the model quality analysis carried out, using firstly the Average Variance Extracted, whose factors presented higher values than those recommended by Hair et al. (2016), which is 0.5. The values presented are: Affective Commitment => 0.896; Normative Commitment => 0.642; Continuity Commitment => 0.782; Innovative Culture => 0.563; Supportive culture => 0.770 and performance => 0.777.

Table 1 evaluates Cronbach’s Alpha, which measures the internal reliability of the constructs. According to Pestana and Gageiro (2014), these constructs should present values above 0.6. The following values are presented: Affective Commitment => 0.884; Normative Commitment => 0.762; Continuity Commitment => 0.721; Innovative Culture => 0.865; Supportive Culture => 0.950; and Performance => 0.942.

It was also decided to analyse the rho_A, whose values should be above 0.6, according to Dijkstra and Henseler (2015). The model values obtained were: Affective Commitment => 0.897; Normative Commitment => 0.834; Continuity Commitment => 0.721; Innovative Culture => 0.898; Supportive Culture => 0.955 and Performance => 0.946.

In order to finalise the quality analysis, still based on Table 1, the composite reliability (CR) analysis was carried out, whose recommended values should be above 0.6, according to Hair et al. (2016) and Dijkstra and Henseler (2015)The values found were: Affective Commitment => 0.945; Normative Commitment => 0.840; Continuity Commitment => 0.877; Innovative Culture => 0.897; Supportive Culture => 0.959 and Performance => 0.954.

The next step was to perform a discriminant analysis of the model, which aims to validate whether one construct is distinct from another by empirical standards, thus implying that it does not capture the phenomenon represented by another construct (Hair et al., 2016). For Hair et al. (2016), the first discriminant analysis that is usually done is the analysis of cross-loadings and the VIF, whose values are presented in Table 2.

Table 2
– Model Cross Loadings and VIF

	CA	CDA	CN	CI	CA	DE	VIF
COMPAFET03	0,954	0,147	0,356	0,473	0,383	0,280	2,690
COMPAFET04	0,939	0,098	0,252	0,405	0,284	0,255	2,690
COMPCONT01	0,142	0,883	0,442	0,243	0,258	0,002	1,465
COMPCONT03	0,090	0,885	0,585	0,197	0,261	0,030	1,465
COMPNORM03	0,402	0,496	0,898	0,225	0,279	-0,005	1,494
COMPNORM04	0,269	0,372	0,619	0,145	0,043	0,002	1,504
COMPNORM05	0,128	0,529	0,858	0,141	0,210	-0,091	1,747
CULTAPO01	0,380	0,200	0,213	0,658	0,811	0,213	2,750
CULTAPO02	0,323	0,233	0,293	0,635	0,848	0,223	2,908
CULTAPO03	0,379	0,322	0,233	0,731	0,918	0,184	4,780
CULTAPO04	0,233	0,271	0,210	0,623	0,889	0,178	5,129
CULTAPO05	0,252	0,273	0,171	0,717	0,899	0,245	5,848
CULTAPO06	0,321	0,216	0,242	0,640	0,876	0,240	3,735
CULTAPO07	0,300	0,270	0,281	0,623	0,897	0,198	3,766
CULTINOV01	0,412	0,304	0,310	0,775	0,521	0,266	2,067
CULTINOV02	0,402	0,156	0,244	0,879	0,696	0,212	3,491
CULTINOV03	0,422	0,096	0,147	0,809	0,612	0,375	2,236
CULTINOV04	0,389	0,330	0,198	0,855	0,687	0,136	2,982
CULTINOV05	0,308	0,137	0,001	0,746	0,601	0,363	2,129
CULTINOV06	0,169	0,222	0,163	0,440	0,293	-0,066	1,329
CULTINOV07	0,245	0,062	-0,017	0,658	0,438	0,341	1,708
DESEMP01	0,216	-0,025	-0,119	0,261	0,142	0,839	2,811
DESEMP02	0,239	0,053	0,028	0,320	0,251	0,881	3,786
DESEMP03	0,219	0,050	-0,086	0,246	0,155	0,854	2,811
DESEMP04	0,308	0,011	0,022	0,298	0,247	0,902	4,733
DESEMP05	0,245	-0,024	-0,100	0,291	0,198	0,905	4,448
DESEMP06	0,265	0,045	0,021	0,311	0,279	0,904	4,352

• Legend CA = Affective Commitment CDA = Continuity Commitment CN = Normative Commitment CI = Innovative Culture DE = Performance
Source: Prepared with Research Data (2021)

Based on the values presented in Table 2, it can be observed that all values are higher in their respective constructs, according to the recommendation of Chin et al. (2016) and Hair et al. (2016). This demonstrates that, according to this criterion, the model has discriminant validity since it has higher values in its respective constructs than in others.

Also, employing Table 2, one can observe the VIF (Variance Inflation Factor), which measures the multicollinearity of the sample, which exists when two or more variables are highly correlated, being a rule the existence of inflation of variance with values above 5, although there is no cutoff number (Garson, 2013; J. et al., 2016). Through the observation of Table xx, it is verified that only two values were above the tolerance limit.

The next step was to use Fornell and Larcker (1981), which compares the square roots of the AVEs with the values of the existing correlations between the latent variables, whose values are shown in Table 3.

Table 3
– Fornell and Larcker and HTMT (Heterotrait-Monotrait) criteria

	CA	CN	CC	CI	CDA	DE	CA	CN	CC	CI	CDA
CA	0,947
CN	0,325	0,801					0,389
CC	0,131	0,581	0,884				0,162	0,763
CI	0,466	0,214	0,249	0,750			0,513	0,257	0,318
CA	0,356	0,270	0,293	0,752	0,878		0,385	0,259	0,351	0,819
DE	0,284	-0,046	0,018	0,326	0,239	0,881	0,308	0,113	0,056	0,377	0,256

• Legend CA = Affective Commitment CN= Normative Commitment CC= Continuity Commitment CI = Innovative Culture CDA= Supportive Culture
Source: Prepared with Research Data (2021)

Based on the values shown in Table 3, it is noted that the highlighted values, representing the squares of the AVEs, are all greater than the subsequent values representing the correlations of the latent variables, thus demonstrating discriminant validity.

The following analysis regarding discriminant validity was the analysis of Heterotrait-Monotrait correlations (HTMT), shown in Table 3. According to Henseler, Ringle, and Sartedt (2015), these correlations demonstrate discriminant validity. According to Garson (2013), the values should be below 0.8, which, based on the values available in Table 3, demonstrates that the model has discriminant validity.

Having finalised the discriminant analysis using the three criteria suggested by Hair et al. (2016), the analysis of model fit using the SRMR criterion, as suggested by Garson (2013), can be seen in Table 4.

Table 4
– Model Adjustment

	Saturated Model	Estimated Model
SRMR	0,076	0,111
d_ULS	2,164	4,694
d_G	1,310	1,456
Chi-Square	755,897	819,452
NFI	0,718	0,694

Source: Prepared with Research Data (2021)

The SRMR is an approximate measure of model fit. It measures the difference between the observed and implied correlation matrices. Hu and Bentler (1999) suggest that by convention, a good model has fit below 0.08, while some researchers suggest a lenient value of 0.10, as noted by Garson (2016)(2016), observing the values presented in Table 4 it is verified that the saturated model, presents the value of 0.076, staying below the value suggested by Garson (2013) which indicates that the model has a good fit. The value of NFI that shows the model fit indicates that the better of 1, the better the model fit, for our case, 0.718, indicating a good fit (Arbuckle, 2012; Byrne, 2001, 2016).

Having concluded the verification of the model fit, it proceeded to the analysis of the validity of the paths through the bootstrapping analysis of the valid paths of the model, whose values should be above Γ=1.96 and significance below sig=0.05 (Hair et al., 2016). Table 5 demonstrates the values found for the proposed model.

Table 5
– Path analysis

	Original Sample	Sample Average	Standard Deviation	Γ	Sig	Hypothesis	Status
CI -> CA	0,466	0,475	0,086	5,447	0,000	H1	Supported
CI -> CN	0,167	0,174	0,234	0,715	0,475	H2	Not Supported
CI -> CC	0,079	0,080	0,171	0,459	0,646	H3	Not Supported
CB-> CA	-0,133	-0,122	0,147	0,906	0,365	H4	Not Supported
CB-> CN	-0,129	-0,119	0,182	0,706	0,480	H5	Not Supported
CB-> CC	0,264	0,304	0,104	2,526	0,012	H6	Not Supported
CDA->CA	0,079	0,056	0,143	0,549	0,583	H7	Not Supported
CDA->CN	0,270	0,288	0,097	2,787	0,006	H8	Supported
AC -> DC	0,293	0,306	0,086	3,399	0,001	H9	Supported
CA -> DE	0,334	0,340	0,090	3,723	0,000	H10	Supported
CN -> DE	-0,155	-0,161	0,113	1,372	0,171	H11	Not Supported
CC -> DE	0,098	0,107	0,128	0,763	0,446	H12	Not Supported

• Legend CA = Affective Commitment CN= Normative Commitment CB = Bureaucratic Culture CC= Continuity Commitment CI = Innovative Culture CDA= Supportive Culture DE = Performance
Source: Prepared with Research Data (2021)

Based on the values found in Table 5, one can observe that 4 paths were supported while others were not, which implies accepting four proposed hypotheses and rejecting eight other hypotheses.

Hypothesis H1, which presupposes a positive influence of innovative culture on affective commitment in the organisation, may be explained by the fact that this type of culture influences innovation and creativity, according to Bruno-Faria and Fonseca (2014). According to Soares and Oliveira (2003), it leads to affective commitment because the employee creates an emotional attachment to the organisation, being unquestioningly motivated. The importance of creating a culture of innovation in companies such as restaurants is the fact that it can create a sustainable competitive advantage for the organisation through differentiated service, new menus or even through partnership with suppliers of the organisation, based on investment in knowledge management and people development (Heriyanto et al., 2021; Oladimeji et al., 2020; Sinha, 2021).

This relationship is antecedent to organisational performance, which corresponds to H10, which was supported, refers to the performance achieved by a group, team or individual in companies, leading to an increase in productivity, profitability, quality, strategy, efficiency, billing, receiving and sales growth, being very important for all types of companies, through management practices, having a significant influence of organisational culture for this to be achieved (Dias, 2021; Oladimeji et al., 2020; White et al., 2021).

The following hypothesis supported - H8 - assumed a positive relationship between the supportive culture and the normative commitment since the supportive culture is characterised by the flexible dimensioning, in which social relationships prevail, favouring the sharing of decisions and social well-being (O. de P. Ribeiro, 2006; Santos & Gonçalves, 2010). In this way, the support culture seeks to engage everyone through support for expressing ideas, promoting the informal manifestation through decentralised decisions with frontline managers’ primary influence (Cafferkey et al., 2019; Pereira, 2014). Already, the normative commitment arises from the commitment that the employee has by the obligation of the work, absorbing the organisational guidelines, and feeling the obligation to do well, influenced mainly by the supportive culture, which probably made this hypothesis supported (Oh & Sawang, 2021; Rego & Souto, 2004; Soares & Oliveira, 2003).

As for hypothesis H9, it presupposes that there is a positive relationship between a supportive culture and the commitment to continuity, which leads us to infer that since the supportive culture is characterised by a flexible dimensioning, favouring social relations, according to Ribeiro (2006) and Santos & Gonçalves (2010) It impacts positively on the culture of support since it is based on science about the relations of the individual with the organisation, through the full knowledge that their departure will bring undesirable costs, minimising the idea that they stay in the company because they need, through the culture of support making them feel welcomed by a family (Erden Bayazit & Bayazit, 2019; Soares & Oliveira, 2003).

Figure 4 demonstrates the final model, which shows the antecedents of the Influence of Culture and Commitment on Performance and demonstrates the hypotheses that were supported and not supported in the structural analysis.

Figure 4
– Final Model Antecedents of Performance Influence

5 CONCLUSION

The research objective was achieved by testing the existing hypotheses in the study. However, it is noteworthy that only four of them were supported. These were H1, through which it was assumed that there was a positive influence between innovative culture and affective commitment, indicating to managers that it is essential to invest in this type of relationship, seeking first to create innovations in restaurants, which makes them different from others, creating a competitive advantage this way, mainly through a differentiated service, an innovative menu, and a different ambience. The second supported hypothesis was H8, which assumed a positive relationship between the supportive culture and normative commitment, indicating that if there is freedom in relationships between employees, there is an exchange of information, creating a better orientation of the organisational objectives, thus creating a normative commitment, causing employees to deliver what the organisation formally expects of them. Hypothesis H9 indicates a positive relationship between a supportive culture and continuance commitment, assuming that if there is investment in a supportive culture, employees will not leave the organisation because they believe that the costs will be more significant for them. Finally, hypothesis H10 is an antecedent relationship to organisational performance, referring to the performance achieved by a group, team or individual in companies, leading to increased productivity, profitability, quality, strategy, efficiency, billing, receipts, and sales growth. It is worth mentioning that hypothesis H11 was not supported, which presupposed a negative relationship between normative commitment and performance. The result was positive for this study, and because of this, it is crucial to develop more qualitative studies to understand this relationship better or even search for alternatives that replace this path. The other hypotheses that were not supported should be investigated better, perhaps through studies with more participants or even in environments other than the bars and restaurants area, including in other states. It is also suggested that qualitative studies be done to understand these relationships better.

These are some of the study’s limitations, which future researchers may continue to test the model in other realities, in new configurations to test new relations or even the inclusion of new dimensions. The limitations also include the fact that at this very moment, we can explain more precisely, and because of this, we understand that there exists a gap that can be delved through the development of Qualitative research to deeply understand the underlying reason why these hypotheses are not supported.

The study’s managerial contributions are mainly linked to understanding the antecedents of organisational performance, enabling Food and Beverage managers to improve restaurant performance, staff engagement, and the creation of competitive advantage.

6 APPENDICES

6.1 Analysis of Observable Variables

The variables were thus eliminated from the model: COMPNORM02 -> -0,024- Round 1, COMPAFET05 -> = -0,076- Round 2, COMPAFET02 -> -0,287- Round 3, COMPAFET01 -> 0,477 - Round 4, CULTINOV06 -> 0,509 - Round 5, COMPNORM01 -> 0,538- Round 6.

In the sixth round - the option was made to eliminate the variable COMPCONT02 = 0.596 instead of the variable COMPAFET06 = 0.587, because the construct Continuity Commitment, has an AVE = 0.466, below the recommended by Hair et al. (2016).

In the seventh round, COMPCONT05 = 0.639 was eliminated instead of COMPAFET06 = 0.587 because the construct Continuity Commitment has an AVE = 0.488.

In the eighth round, it was opted to eliminate the COMPAFET05=> Γ=0.801; sig=0.423. Also, in the 9thRound, the option was to eliminate the COMPNORM06=> Γ=0.777; sig=0.438.

In the 10th round, the option was to eliminate the variable COMPCONT04 = 0.692 due to Cronbach’s Alpha of the Continuity Commitment construct = 0.542, below the recommended one.

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Edited by

Edited by: Jordana Marques Kneipp

Publication Dates

• Publication in this collection
  23 Aug 2024
• Date of issue
  June 2024

History

• Received
  30 Jan 2023
• Accepted
  24 May 2023
• Published
  13 June 2024