Next Article in Journal
Adaptation of the Management Model of Internationalization Processes in the Development of Railway Transport Activities
Next Article in Special Issue
Understanding the Effect of Dining and Motivational Factors on Out-Of-Home Consumer Food Waste
Previous Article in Journal
How to Improve the Functional Capacity of Frail and Pre-Frail Elderly People? Health, Nutritional Status and Exercise Intervention. The EXERNET-Elder 3.0 Project
Previous Article in Special Issue
Relational and Logistical Dimensions of Agricultural Food Recovery: Evidence from California Growers and Recovery Organizations
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 12
Issue 15
10.3390/su12156242
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Effect of the Perceived Utility of a Management Control System with a Broad Scope on the Use of Food Waste Information and on Financial and Non-Financial Performances in Restaurants

by
Rogério João Lunkes
1,*,
Fabricia Silva da Rosa
1 and
Pamela Lattanzi
2
1
Department of Accounting, Federal University of Santa Catarina, Florianópolis-SC 88040-900, Brazil
2
Department of Law, University of Macerata, 62100 Macerata (Marche), Italy
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(15), 6242; https://doi.org/10.3390/su12156242
Submission received: 29 June 2020 / Revised: 26 July 2020 / Accepted: 27 July 2020 / Published: 3 August 2020
(This article belongs to the Special Issue The Challenge of Food Waste Reduction to Achieve More Sustainable Food Systems)
Browse Figure
Versions Notes

Abstract

:
The purpose of this study is to analyse the effect of the perceived utility of a management control system with a broad scope on the use of food waste information and on financial and non-financial performances in restaurants. To collect data, a questionnaire was administered in Brazilian restaurants. Data from 206 restaurants were analysed with structural equation modelling, which was performed with SmartPLS software. The results reveal that a management control system of broad scope, which includes non-financial information, is oriented towards the future, and contains an external and long-term focus, assists in the use of information on food waste. In addition, the use of information about food waste by managers improves the financial and non-financial performance of restaurants. The study contributes to the literature by showing that broader information systems are effective in managing food waste, and they can also contribute to improving performance.

1. Introduction

Food waste is a significant global problem with consequences for food security and economic, social and environmental sustainability [1,2,3]. Food waste refers to “food which was originally produced for human consumption but then was discarded or was not consumed by humans. Includes food that spoiled prior to disposal and food that was still edible when thrown away” [4] (p. 112). According to the United Nations Food and Agriculture Organization (FAO), food waste is only measured for products intended for human consumption and excludes animal feed and product parts that are inedible. The FAO estimates that the loss and waste of food is one-third of the total amount of food produced: approximately 1.3 billion tons [5]. Reducing food waste levels is critical to mitigating hunger and reducing pressure on natural resources [1,6,7,8].
Reducing food waste is an interdisciplinary and multisectoral task that requires study from different perspectives [3,9,10]. In companies, this task is addressed by business management [4,11], which presupposes the incorporation of waste information in the planning and control system. Studies have recommended the implementation of information systems to manage food waste, e.g., [6,12,13,14,15]. However, it is challenging to design management systems that incorporate food waste measures in a manner that facilitates decision-making [16,17].
A management control system (MCS) can play this important role, as it can provide useful information for decision-making [18] and influence people to achieve organizational goals [19,20,21], because an MCS facilitates the appropriation of the benefits of sustainable innovations in organizations [22]. Such systems can be designed to provide more sophisticated information, which can facilitate the decision-making process and the coordination of various activities related to food waste [13,15]. These broad information systems are referred to in the MCS literature as “broad-scope” [23,24]. The scope dimension has three sub-dimensions: focus, quantification, and time horizon [25]. “Focus” refers to whether the information is collected from within the firm or outside it (e.g., economic, technological and market factors). The quantification feature pertains to whether the information is financial or non-financial. “Time horizon” refers to the extent to which the information relates to future rather than historical events. Thus, broad-scope information is externally focused, non-financial, and future-oriented [23]. To enhance the process of making decisions regarding food waste, it is essential to align scope with this use of an MCS. Thus, information of a broader scope can be used to support decisions regarding food waste. These performance measures for food waste can be formed by a combination of broad-scope measures [26]. For example, they can be formed from financial and non-financial information (e.g., costs, revenue from scrap, amount of waste). In this study, we propose to fill a gap in the management studies literature by aligning scope of MCS and use of food waste information for application in restaurants.
Studies reveal the importance of management mechanisms [13,15], such as big data [14] and performance indicators [27]. However, Warshawsky [28] recognized that the current business information system does not provide sufficient information for the management of food waste. In addition, the limited use of food waste information may be hampering related decision-making. There is relatively little empirical research examining how or under what circumstances MCS, more specifically the scope and use of food waste evaluation systems, can assist in the communication and management of food waste. Thus, investigating the importance of information of a broad scope for different levels or types of decision [23] can be fundamental. The purpose of this study is to analyse the effect of the perceived utility of a management control system of broad scope on the use of food waste information and on financial and non-financial performance in restaurants. To attain this objective, we used a questionnaire and used structural equation modelling to analyse data collected from 206 restaurants.
In this study, we argue that an MSC of broad scope is a means of assisting the decision-making process about food waste in restaurants for two reasons: (a) the decision-making environment about food waste in restaurants is dynamic; that is, food waste can occur at various stages (e.g., planning, purchasing, production, processing or cooking, cleaning, service, storage or stock management [29]); and (b) the information is broad in scope; that is, it is operational, financial, non-financial, external and forward-looking (e.g., product time to market, role of supply chain actors in food waste [13], operational aspects that lead to food waste [14,15], food management measures [16,17], and policies to avoid food waste [4]).
We contribute to the food waste literature by addressing this problem using a management control system (MCS). More specifically, we include in the MCS scope food waste information of a broad scope (i.e., non-financial, external, future-oriented and long term) and indicators that apply to food waste. We also expand empirical knowledge on the impact of food waste information on company results by analysing financial and non-financial performance in a segregated way. This analytical approach is adopted because studies have revealed that reducing food waste cannot be left to consumers alone; on the contrary, food waste reduction involves a double loop between consumers and companies [2]. The role of service companies, such as those in restaurants, can be important in reducing food waste [15,28]. We contribute to this field by empirically demonstrating that the scope and use of an MCS that includes food waste information improves the financial and non-financial performance of restaurants. This article contains sections on the background, materials and methods, results, discussion and finally a section on our conclusions.

2. Background

2.1. Broad-Scope of Management Control System

Management control systems (MCS) are formal systems designed to provide information to managers, and their scope can be broad or restricted. The first scope comprises internal, financial and historical information, while the second is non-financial, external and future-oriented information [23,24,30,31,32,33,34]. Information with a broad scope can be financial and non-financial, and it can deal with future events based on plans for internal operating activities and external circumstances (changes). Such information includes target price, net present value and flexible budget [31].
There is empirical evidence on the importance of the perceived utility of broad-based information to manage uncertainties and complexities in organizations [32,34]. Therefore, the perceived usefulness of broad-scope systems affects the way information is used in organizations [24]. Our study deals specifically with information of broad scope, as it considers that for the use of information about food waste, several elements are needed to assist decision-making processes on operational activities [29] and organizational approaches [35,36].

2.2. Use of Food Waste Information

An emerging flow of literature explored corporate decisions as essential elements for reducing food waste and better performance [13,14,15]. What is known is that decisions about food waste can occur during planning, purchasing, production (processing or cooking), cleaning, service, storage or stock management operations [29]. Among the decisions made by managers of different organizations, the following have been observed: (a) demand and menu planning [7,12,37,38,39,40], (b) purchasing decisions [41,42], and (c) eco decisions—internal and external controls [35,36].
The literature reveals that management interventions [13], as well as operational aspects [14,15], food management measures [16,17], and effective policies must be considered to avoid food waste in organizations [4]. For example, Dora et al. [27] demonstrated that the inclusion of food waste in the main performance indicators (KPI), as well as the use of appropriate planning and scheduling tools, helps companies to reduce waste and improve performance.

3. Materials and Methods

3.1. Questionnaire Design and Data Collection

To select subject companies in the restaurants, we used the register of companies in the tourism sector known as Cadastur [43]. From this register, 1011 companies were selected that had complete contact details. The restaurants were selected for our research for several reasons. First, our theoretical model was proposed to analyse the perceived utility of MCS (broad-scope) and use of information in food waste, and a single sector was recommended to decrease noise in the data [44]. Second, Brazil’s restaurant network has expanded in response to a variety of factors, such as the organization of major events, including the soccer World Cup and the Olympics, which brought large numbers of tourists to the country [44]. However, another factor was the economic crisis faced by Brazil in recent years, one result of which was an employment shift of numerous individuals into the informal sector, including food selling as a means of earning a living. Third, waste in restaurants is significant [45], and the sector has been identified as the third-largest source of food waste [46].
A questionnaire based on the literature was designed to collect the data. The variables found in the questionnaire are described in detail in Section 2.2 (Table 2). Before administering the questionnaire, a pre-test was conducted using four academics and a restaurants manager to ensure question comprehensibility, verify appropriate extent and determine the response time.
A specialized company performed the data collection. To guarantee the reliability of the collected data, the company follows procedures previously used for academic research [47]. First, an introductory letter and the study objectives were presented. Interviewees who agreed to complete the questionnaire were included in the initial sample. The process of telephoning the restaurant members in the sample occurred from March to April 2020. The survey resulted in 206 responses, equivalent to 20.5% of the original sample population. This number of responses is representative and more than that of other studies in the area [7,36,38,45]. The characteristics of the respondents and restaurants are shown in Table 1.
We also tested possible biases in the sample. First, we evaluated common method bias, using the Harman single factor test [44]. The first factor showed a total explained variance of 31.25%, confirming the absence of the common method bias. Second, we performed the non-response bias test by comparing the first 10%of the answers to the last 10% [48]. There were no differences between the constructs, with the exception of financial performance (p < 0.05); similar results were reported in the study by De Harlez and Malagueno [49].

3.2. Variable Measurement

The study variables were constructed based on the literature and organized in a manner that facilitated analysing the research elements: scope and use of information, food waste information, financial and non-financial performance, and characteristics of managers and restaurant companies (Table 2).

3.2.1. Perceived Utility of MCS

Broad-scope MCS was measured with four scope dimensions: non-financial, future orientation, external orientation, and long-term orientation [24,30,34]. The restaurant managers indicated the usefulness of the following information characteristics: (i) “compared with information on past facts, I think that information about future facts is…”, (ii) “compared with internal information, I think that external information is…”, (iii) “compared to financial information, I think non-financial information is…”, and (iv) “compared to short-term information, I think long-term information is…” A five-point Likert scale was used to measure the questions (scale: 1 = not very useful, 5 = very useful). The respondents could also answer “C0” when they did not know the answer or felt the question did not apply to their circumstances.

3.2.2. Use of Food Waste Information

This construct was measured using food waste information. The information was adapted from the literature on eco-controls [35,36]. Recent research issues have been slightly adapted to the context of food waste [8,29]. We used four questions regarding the following topics to measure our construct: (i) monitoring of internal compliance with food waste policies and regulations, (ii) promotion of continuous improvement of internal processes to avoid food waste, (iii) provision of data for internal decision-making on food waste, and (iv) provision of data for external reports on food waste. A five-point scale was used (1 = not used to 5 = used). The respondents could also answer “0” if they did not know, or the question did not apply.

3.2.3. Financial Performance

Three questions have been widely used in the MCS literature to measure financial performance in restaurants. The measures assess operating profit [36,50], net revenue [51], and return on investment [44,52]. A Likert scale from 1 to 5 was used (1 = below average to 5 = significantly above average).

3.2.4. Non-Financial Performance

Non-financial performance was measured using a balanced scorecard (BSC), adapted from Elbanna et al. [50] and Abdel-Maksoud et al. [36]. This variable was measured by four items: (i) customer satisfaction (customer perspective), (ii) quality of products and services provided (internal business perspective), (iii) development or product and service innovation (innovation perspective), and (iv) employee resources (learning perspective). A Likert scale from 1 to 5 was used (1 = below average to 5 = significantly above average).

3.2.5. Control Variables

It was expected that characteristics of the manager and the restaurants are related to food waste. In this regard, we performed complementary analyses using the following manager characteristics: age, sex, and education. Age was measured in years of life. The gender variable was divided into male and female. Finally, education was separated into managers with a background in business and other backgrounds not related to business areas. We understand that these characteristics may influence the model because they are often noted in studies as variables that can cause food waste (for example, [6,26,53,54,55,56]). The restaurant characteristics included size (number of employees and seats) and business maturity. Employees were measured by the number of permanent contracts, and seats by the number of seats available to customers. The maturity of the business was measured by the years since the restaurant was founded. Studies indicate that the size of a food service company can play an important role in food waste generation [7,8,42,57].

3.3. Data Analysis

For the data analysis, we used structural equation modelling (SEM). SEM can be classified into two types: (i) SEM based on covariance (MEEBC) or (ii) simple LISREL models and the partial quadratic minimum, which amounts to performing SEM by parts [58]. This technique is appropriate because in this study we seek to analyse the relationship between broad-scope MCS, the use of food waste information and financial and non-financial performance in the restaurants.
For the analysis, we used Smart/PLS 3 software. The PLS-SEM analysis is performed in two stages: the first addresses the validity and adequacy of the constructs based on the relationship between the information and the latent variables (or constructs) and thus enables the researcher to evaluate construct reliability and validity; the second addresses the relationship between latent variables and enables one to verify whether an exogenous latent variable is related to an endogenous latent variable [50]. PLS does not require data to be normally distributed and can handle high complexity [58].

4. Results

4.1. Measurement Model

Initially, we verified the factorial loads of the model variables. According to Table 3, the loading factor values were very close to or above 0.7 and are thus in line with what is recommended by the literature [59]. To verify model reliability, reliability and validity checks were performed. The composite reliability (CR) values were all above 0.80, and the average variance extracted (AVE) values were larger than 0.5. Cronbach’s alpha was also determined. Its values were very close to or above 0.7. Thus, all results are above the recommended limits, which indicates the internal reliability of the measurement model.
In addition, we tested the discriminant validity by the criteria of Fornell and Larcker (Table 4). The results reveal that none of the correlations between the constructs was greater than the square root of the stroke, indicating that there is discriminant validity in the research model. This outcome demonstrates that the measurement model has satisfactory convergent and discriminant validity.

4.2. Structural Model

The broad-scope MCS, which includes food waste information and non-financial performance and is future- and external- and long term-oriented, positively and significantly impacts the use of food waste information. In addition, the use of food waste information, including the monitoring of internal compliance with food waste policies and regulations, the encouraging of continuous improvement of internal processes to prevent food waste, and the provision of data for internal decision-making on food waste and for external reports on food waste, positively and significantly affects non-financial and financial performance. Table 5 shows the structural model with the significant path coefficients.
Non-financial performance consists of customer satisfaction, the quality of products and services provided, product development or service innovation, and employee resources, and has an impact on operating profit, net revenue, and return on investment (financial performance).

5. Discussion

Our results reveal that a broad-scope MCS that includes non-financial information is future- and external- and long term-oriented, and affects the use of food waste indicators. The findings indicate that broad-scope information is important in managing food waste. This is because waste management involves the combination of various types of information. For example, there is a need for information on, e.g., waste costs, cost and revenue per dish, wasted food quantities and customer satisfaction. That is, managers require a mix of information that combines financial and non-financial data. Our results agree with previous studies that demonstrate that broad-scope MCS improves information relevance and that helps manage flexibility, decentralization and innovation [24,60,61]. It also reveals that the use of managerial information [13,15], and performance indicators [27] on food waste improves financial and non-financial results. Figure 1 shows the structural model with the significant path coefficients.
Management in restaurants also demands information oriented towards the future, which is consistent with [15,23,24,30,31,32,34]. For example, planning and forecasting is noted in the literature as the best way to reduce food waste. Strotmann et al. [62] and Derqui and Fernandez [12] argue that it is essential to define the objectives to be achieved. In addition, these objectives can be subdivided into quantitative and qualitative objectives. Generally, food waste occurs because of planning and forecasting errors, such as mistaken customer-number forecasts. Overestimating customer demand generates food overproduction, which has been identified as a primary cause of waste [2,7,16,17,37,45,62,63]. This overestimation of demand is in many cases generated by a lack of information; were it available, this information would make it possible to better plan food service activities, such as the accurate estimation of customer numbers. The flow of customers for the day, week or month is also important information, e.g., planning purchases, estimating the amount of food to be prepared, and calculating employee time off. Goh and Jie [64] note that to reduce waste it is necessary to implement measures based on accurate information that facilitate preparing the correct number of meals.
Previous studies have observed that the use of performance assessment information for food waste is not yet fully consolidated in the sector. For example, Otten et al. [17] found that only three food-generating businesses reported the presence of official targets for preventing food waste and that only three companies reported being in the process of creating such goals. In addition, these goals vary in terms of results (e.g., food volume or the amount of an ingredient used) and the level of responsibility (e.g., department or food preparation station). Most companies mentioned that these goals have been defined or are being defined at the corporate level. Two companies without goals reported that they thought they did not require such goals or that there was no corporate support for establishing them. Strotmann et al. [16] analysed food waste reduction before and after the process of developing and implementing measures to reduce waste. Their results reveal that there was a reduction in food waste after the implementation of the measures.
Lack of planning can also affect other activities and processes. For example, Bilska et al. [45] found that a substantial quantity of stock was lost due to unplanned purchases. Planning information can play an important role in reducing unplanned purchases at the consumer level [2,65] but also in the restaurants. Cicatiello and Franco [66] observed a lack of adequate records on food waste in supermarkets in Italy. Such information can also be essential to establishing marketing plans, such as promotions and daily specials as well as discounts [10], among other creative activities, as well as the increase in other strategies such as increasing the temperature of food storage [67].
Our results also indicate that external information (e.g., regarding the market) and use of management information [15] and big data [13] are important in making decisions concerning food waste. The restaurants must increasingly anticipate market changes, and for this they require information on the external environment, such as customers and suppliers. The food sector is highly sensitive to changes in the business environment and must be aware of any such changes. Thus, having information regarding the external environment can mean faster adaptation. Therefore, the use of technology to support decision-making is increasingly identified as an essential tool to help reduce food waste [13,26]. It seems that managers recognize the importance of receiving more sophisticated information to managing food waste.
The results also reveal that the use of food waste information generates better financial and non-financial performance. This outcome shows that food waste management improves performance in restaurants. In addition, the improvement in non-financial performance has a positive impact on financial performance, which indicates that non-financial measures affect financial performance. Irani et al. [13] demonstrated that the use of managerial information on food distribution and consumption can improve the non-financial result (food security and reduced food waste). Wen et al. [15] showed that the use of information technology allowed waste management in restaurants. Dora et al. [27] demonstrated that the inclusion of performance indicators (KPI) and appropriate planning and scheduling tools helps companies to reduce food waste and improve performance.
To expand the results of the model, we performed complementary analyses with variables related to the characteristics of the respondents and the restaurants. The objective was to investigate possible characteristics that could help explain the results of the research model. We selected the variables used in these analyses from the literature. For example, researchers note that age, gender and education can be variables that affect food waste (e.g., [3,6,12,68]). Initially, we analysed variables related to restaurants managers. The results are shown in Table 6 (Panel A).
The results reveal that the relationship between perceived utility of MCS (broad-scope) and the use of food waste information is not significant for managers of up to 30 years of age. This outcome indicates that younger managers may have less experience and skill with management systems, and a lesser ability to plan [2]. In contrast, the use of food waste information had a positive and significant impact on financial performance for male managers with business experience. Studies have found that a business background can be important in management, as managers with training in this area tend to be more conservative and accurate when making forecasts [69,70]. This background also makes them more adept at making strategic decisions.
The relationship between the use of food waste information and non-financial performance was not significant for managers trained in areas other than business. One of the explanations is the cognitive capacity related to the implementation of management systems and the use of information from managers trained in the business area [71,72,73]. Although demographic characteristics are expected to offer predictive power in relation to food waste management [26], the empirical evidence from this study reveals that age (i.e., younger managers), gender and training in other areas (than business) do not significantly affect the results.
The restaurants control variables used in this study to reflect size are presented in other studies as characteristics related to food waste [7,8,57]. The results for the restaurant characteristics reveal that those enterprises that are larger (more than 10 employees and more than 100 seats) and with more years of operation (over 10 years) do not perceive that the use of food waste information improves financial performance. In larger restaurants with more than 100 seats, the use of information on food waste is also not significantly related to non-financial performance. This is a curious result, since it was expected that larger restaurants would make greater use of information about food waste; consequently, this greater use of information would be reflected in financial and non-financial performance. New studies can better explore these relationships and expand knowledge about possible factors that may explain these results.
This study contributes to the literature by demonstrating that a management control system that includes food waste information can help improve performance in the restaurants. The results also reveal that using broader information systems can help with waste management. Thus, the study not only contributes to the literature but can also assist managers, accountants, and controllers in the restaurants.

6. Conclusions

The aim of this study was to analyse the effect of the perceived utility of a management control system of broad scope on the use of food waste information and on financial and non-financial performance in restaurants. To conduct the study, a questionnaire was used in Brazilian restaurants. To analyse the data provided by 206 responses, structural equation modelling was performed.
The study concludes that a comprehensive MCS, i.e., one that includes non-financial information, is future- and externally oriented and long-term, affects the use of food waste indicators. This outcome indicates that more comprehensive information helps restaurant managers make decisions regarding food waste.
Our study provides empirical evidence that the use of food waste information in restaurants (a—internal compliance with waste policies and regulations; b—continuous process improvement; c—waste data provision; d—external waste data) improves non-financial performance (a—customer satisfaction, b—quality of products and services, c—development and innovation of products and services, d—human resources) and financial performance (a—operating profit; b—net revenue; c—return on investment). The study finds that the use of broad-scope information on food waste improves organizational performance. These findings provide evidence that can assist restaurant managers in the design of management systems.
Although the results are robust, our study has several limitations. The findings represent manager perceptions regarding the scope and use of food waste information and business results. For example, managers can overestimate the performance of their businesses compared to their competitors. Several questions in the study regarding the use of information were adapted to the food waste context. Despite these limitations, the study results are an important addition to knowledge regarding food waste management.
Future research may investigate other aspects of the management system, such as diagnosis and interactive use. In addition, more research is needed on the role of planning and control systems in food waste, so that it is possible to analyse the reflection of management issues (e.g., purchasing planning, training of kitchen staff, and employee and customer awareness) in the level of food waste and the companies’ operating and financial results. It is also important to study the incentive systems related to food waste and their impact on employee motivation. Studies that assess manager motivation and economic gains in connection with MCS design and use are encouraged.

Author Contributions

All authors were involved in the conception, design, and writing of the manuscript. R.J.L. supervised the data collection and carried out the data analysis. F.S.d.R. and P.L. interpreted the data and critically reviewed the manuscript. All authors have approved the final version of the manuscript.

Funding

This publication has been developed with support from Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq (Processo No. 305812/2018-1) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES (Processo No. 88887.468904/2019-00).

Acknowledgments

We would like to acknowledge the support of Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES, and the English revision service provided by AJE.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Eriksson, M.; Strid, I.; Hansson, P.A. Carbon footprint of food waste management options in the waste hierarchy–a Swedish case study. J. Clean. Prod. 2015, 93, 115–125. [Google Scholar] [CrossRef]
  2. Bravi, L.; Murmura, F.; Savelli, E.; Viganò, E. Motivations and Actions to Prevent Food Waste among Young Italian Consumers. Sustainability 2019, 11, 1110. [Google Scholar] [CrossRef] [Green Version]
  3. Fanelli, R.M. Using Causal Maps to Analyse the Major Root Causes of Household Food Waste: Results of a Survey among People from Central and Southern Italy. Sustainability 2019, 11, 1183. [Google Scholar] [CrossRef] [Green Version]
  4. Thyberg, K.L.; Tonjes, D.J. Drivers of food waste and their implications for sustainable policy development. Resour. Conserv. Recycl. 2016, 106, 110–123. [Google Scholar] [CrossRef]
  5. Food and Agriculture Organization of the United Nations-FAO. Food Loss and Food Waste. Available online: http://www.fao.org/food-loss-and-foodwaste/en/ (accessed on 1 February 2019).
  6. Betz, A.; Buchli, J.; Göbel, C.; Müller, C. Food waste in the Swiss restaurants-magnitude and potential for reduction. Waste Manag. 2015, 35, 218–226. [Google Scholar] [CrossRef]
  7. Aamir, M.; Ahmad, H.; Javaid, Q.; Hasan, S.M. Waste Not, Want Not: A Case Study on Food Waste in Restaurants of Lahore, Pakistan. J. Food Prod. Market. 2018, 24, 591–610. [Google Scholar] [CrossRef]
  8. Sakaguchi, L.; Pak, N.; Potts, M.D. Tackling the issue of food waste in restaurants: Options for measurement method, reduction and behavioral change. J. Clean. Prod. 2018, 180, 430–436. [Google Scholar] [CrossRef] [Green Version]
  9. Langley, J.; Yoxall, A.; Heppell, G.; Rodriguez, E.M.; Bradbury, S.; Lewis, R.; Luxmoore, J.; Hodzic, A.; Rowson, J. Food for thought? A UK pilot study testing a methodology for compositional domestic food waste analysis. Waste Manag. Res. 2010, 28, 220–227. [Google Scholar] [CrossRef]
  10. Cicatiello, C.; Secondi, L.; Principato, L. Investigating Consumers’ Perception of Discounted Suboptimal Products at Retail Stores. Resources 2019, 8, 129. [Google Scholar] [CrossRef] [Green Version]
  11. Canali, M.; Amani, P.; Aramyan, L.; Gheoldus, M.; Moates, G.; Östergren, K.; Silvennoinen, K.; Waldron, K.; Vittuari, M. Food waste drivers in Europe, from identification to possible interventions. Sustainability 2017, 9, 37. [Google Scholar] [CrossRef] [Green Version]
  12. Derqui, B.; Fernandez, V. The opportunity of tracking food waste in school canteens: Guidelines for self-assessment. Waste Manag. 2017, 69, 431–444. [Google Scholar] [CrossRef] [PubMed]
  13. Irani, Z.; Sharif, A.M.; Lee, H.; Aktas, E.; Topalo, Z.; Wout, T.V.; Huda, S. Managing food security through food waste and loss: Small data to big data. Comput. Oper. Res. 2018, 98, 367–383. [Google Scholar] [CrossRef]
  14. Raimondo, M.; Caracciolo, F.; Cembalo, L.; Chinnici, G.; Pecorino, B.; D’Amico, M. Making Virtue Out of Necessity: Managing the Citrus Waste Supply Chain for Bioeconomy Applications. Sustainability 2018, 10, 4821. [Google Scholar] [CrossRef] [Green Version]
  15. Wen, Z.; Hu, S.; Clercq, D.; Beck, M.B.; Zhang, H.; Zhang, H. Design, implementation, and evaluation of an Internet of Things (IoT) network system for restaurant food waste management. Waste Manag. 2018, 73, 26–38. [Google Scholar] [CrossRef] [PubMed]
  16. Strotmann, C.; Friedrich, S.; Kreyenschmidt, J.; Teitscheid, P.; Ritter, G. Comparing food provided and wasted before and after implementing measures against food waste in three healthcare food service facilities. Sustainability 2017, 9, 1409. [Google Scholar] [CrossRef] [Green Version]
  17. Otten, J.; Getts, K.; Diedrich, S.; Benson, C. Commercial and anti-hunger sector views on local government strategies for helping to manage food waste. J. Agric. Food Syst. Community Dev. 2018, 8, 55–72. [Google Scholar] [CrossRef]
  18. Chenhall, R.H. Management control systems design within its organizational context: Findings from contingency-based research and directions for the future. Account. Organ. Soc. 2003, 28, 127–168. [Google Scholar] [CrossRef]
  19. Otley, D. Performance management: A framework for management control systems research. Manag. Account. Res. 1999, 10, 363–382. [Google Scholar] [CrossRef] [Green Version]
  20. Cugueró-Escofet, N.; Rosanas, J.M. The just design and use of management control systems as requirements for goal congruence. Manag. Account. Res. 2013, 24, 23–40. [Google Scholar] [CrossRef] [Green Version]
  21. Langevin, P.; Mendoza, C. How can management control system fairness reduce managers’ unethical behaviours? Eur. Manag. J. 2013, 31, 209–222. [Google Scholar] [CrossRef]
  22. Lopez-Valeiras, E.; Gomez-Conde, J.; Naranjo-Gil, D. Sustainable innovation, management accounting and control systems, and international performance. Sustainability 2015, 7, 3479–3492. [Google Scholar] [CrossRef] [Green Version]
  23. Bouwens, J.; Abernethy, M.A. The consequences of customization on management accounting systems design. Account. Organ. Soc. 2000, 25, 221–259. [Google Scholar] [CrossRef]
  24. Naranjo-Gil, D.; Hartmann, F. Management accounting systems, top management team heterogeneity and strategic change. Account. Organ. Soc. 2007, 32, 735–756. [Google Scholar] [CrossRef]
  25. Chenhall, R.H.; Morris, D. The impact of structure, environment, and interdependence on the perceived usefulness of management accounting systems. Account. Rev. 1986, 61, 16–35. [Google Scholar]
  26. Schanes, K.; Dobernig, K.; Gözet, B. Food waste matters-A systematic review of household food waste practices and their policy implications. J. Clean. Prod. 2018, 182, 978–991. [Google Scholar] [CrossRef]
  27. Dora, M.; Wesana, J.; Gellynck, X.; Seth, N.; Dey, B.; De Steur, H. Importance of sustainable operations in food loss: Evidence from the Belgian food processing industry. Ann. Oper. Res. 2020, 290, 47–72. [Google Scholar] [CrossRef] [Green Version]
  28. Warshawsky, D.N. The Challenge of Food Waste Governance in Cities: Case Study of Consumer Perspectives in Los Angeles. Sustainability 2019, 11, 847. [Google Scholar] [CrossRef] [Green Version]
  29. Ozbük, R.M.Y.; Coskun, A. Factors affecting food waste at the downstream entities of the supply chain: A critical review. J. Clean. Prod. 2020, 244, 118–628. [Google Scholar] [CrossRef]
  30. Mia, L.; Chenhall, R.H. The usefulness of management accounting systems, functional differentiation and managerial effectiveness. Account. Organ. Soc. 1994, 19, 1–13. [Google Scholar] [CrossRef]
  31. Tillema, S. Towards an integrated contingency framework for MAS sophistication Case studies on the scope of accounting instruments in Dutch power and gas companies. Manag. Account. Res. 2005, 16, 101–129. [Google Scholar] [CrossRef]
  32. Mia, L.; Winata, L. Manufacturing strategy, broad scope MAS information and information and communication technology. Br. Account. Rev. 2008, 40, 182–192. [Google Scholar] [CrossRef] [Green Version]
  33. Choe, J.M. The strategic alignment of management accounting information systems, and organizational performance. Global Bus. Financ. Rev. Open Access 2017, 22, 51–65. [Google Scholar] [CrossRef]
  34. Lunkes, R.J.; Naranjo-Gil, D.; Lopez-Valeiras, E. Management Control Systems and Clinical Experience of Managers in Public Hospitals. Int. J. Environ. Res. Public Health 2018, 15, 776. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Henri, J.F.; Journeault, M. Eco-control: The influence of management control systems on environmental and economic performance. Account. Organ. Soc. 2010, 35, 63–80. [Google Scholar] [CrossRef]
  36. Abdel-Maksoud, A.; Kamel, H.; Elbanna, S. Investigating relationships between stakeholders’ pressure, eco-control systems and hotel performance. Int. J. Hosp. Manag. 2016, 59, 95–104. [Google Scholar] [CrossRef]
  37. Goonan, S.; Mirosa, M.; Spence, H. Getting a taste for food waste: A mixed methods ethnographic study into hospital food waste before patient consumption conducted at three New Zealand foodservice facilities. J. Acad. Nutr. Diet. 2014, 114, 63–71. [Google Scholar] [CrossRef]
  38. Pirani, S.I.; Arafat, H.A. Reduction of food waste generation in the hospitality industry. J. Clean. Prod. 2016, 132, 129–145. [Google Scholar] [CrossRef]
  39. Derqui, B.; Fernandez, V.; Fayos, T. Towards more sustainable food systems. Addressing food waste at school canteens. Appetite 2018, 129, 1–11. [Google Scholar] [CrossRef]
  40. Pinto, R.S.; Pinto, R.M.S.; Melo, F.F.S.; Campos, S.S.; Cordovil, C.M.S. A simple awareness campaign to promote food waste reduction in a University canteen. Waste Manag. 2018, 76, 28–38. [Google Scholar] [CrossRef]
  41. Falasconi, L.; Vittuari, M.; Politano, A.; Segrè, A. Food waste in school catering: An Italian case study. Sustainability 2015, 7, 14745–14760. [Google Scholar] [CrossRef] [Green Version]
  42. Filimonau, V.; Gherbin, A. An exploratory study of food waste management practices in the UK grocery retail sector. J. Clean. Prod. 2017, 167, 1184–1194. [Google Scholar] [CrossRef] [Green Version]
  43. Ministry of Tourism (Brazil). Cadastur. Available online: http://www.classificacao.turismo.gov.br/MTUR-classificacao/mtur-site/cadastur.jsp. (accessed on 24 January 2020).
  44. Gomez-Conde, J.; Lunkes, R.J.; Rosa, F.S. Environmental innovation practices and operational performance. The joint effects of management accounting and control systems and environmental training. Account. Audit Account. J. 2019, 32, 1325–1357. [Google Scholar] [CrossRef]
  45. Bilska, B.; Tomaszewska, M.; Kołożyn-Krajewska, D. Managing the Risk of Food Waste in Foodservice Establishments. Sustainability 2020, 12, 2050. [Google Scholar] [CrossRef] [Green Version]
  46. Beretta, C.; Stoessel, F.; Baier, U.; Hellweg, S. Quantifying food losses and the potential for reduction in Switzerland. Waste Manag. 2013, 33, 764–773. [Google Scholar] [CrossRef] [Green Version]
  47. Dillman, D.A.; Smyth, J.D.; Cristian, L.M. Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method, 4th ed.; Wiley: New York, NY, USA, 2014. [Google Scholar]
  48. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879. [Google Scholar] [CrossRef] [PubMed]
  49. De Harlez, Y.; Malagueño, R. Examining the joint effects of strategic priorities, use of management control systems, and personal background on hospital performance. Manag. Account. Res. 2016, 30, 2–17. [Google Scholar] [CrossRef] [Green Version]
  50. Elbanna, S.; Eid, R.; Kamel, H. Measuring hotel performance using the balanced scorecard: A theoretical construct development and its empirical validation. Int. J. Hosp. Manag. 2015, 51, 105–114. [Google Scholar] [CrossRef]
  51. Dennehy, C.; Lawlor, P.G.; Gardiner, G.E.; Jiang, Y.; Shalloo, L.; Zhan, X. Stochastic modelling of the economic viability of on-farm co-digestion of pig manure and food waste in Ireland. Appl. Energ. 2017, 205, 1528–1537. [Google Scholar] [CrossRef]
  52. Rosa, F.S.; Lunkes, R.J.; Brizzola, M.M.B. Exploring the relationship between internal pressures, greenhouse gas management and performance of Brazilian companies. J. Clean. Prod. 2019, 212, 567–575. [Google Scholar] [CrossRef]
  53. Koivupuro, H.K.; Hartikainen, H.; Silvennoinen, K.; Katajajuuri, J.M.; Heikintalo, M.; Reinikainen, A.; Jalkanen, L. Influence of socio-demographical, behavioural and attitudinal factors on the amount of avoidable food waste generated in Finnish households. Int. J. Consum. Stud. 2012, 36, 183–191. [Google Scholar] [CrossRef]
  54. Visschers, V.H.M.; Wickli, N.; Siegrist, M. Sorting out food waste behaviour: A survey on the motivators and barriers of self-reported amounts of food waste in households. J. Environ. Psychol. 2016, 45, 66–78. [Google Scholar] [CrossRef]
  55. Marais, M.L.; Smit, Y.; Koen, N.; Lötze, E. Are the attitudes and practices of food service managers, catering personnel and students contributing to excessive food wastage at Stellenbosch University? S. Afr. J. Clin. Nutr. 2017, 30, 15–22. [Google Scholar] [CrossRef] [Green Version]
  56. Hebrok, M.; Boks, C. Household food waste: Drivers and potential intervention points for design-An extensive review. J. Clean. Prod. 2017, 151, 380–392. [Google Scholar] [CrossRef]
  57. Wang, L.; Liu, G.; Liu, X.; Liu, Y.; Gao, J.; Zhou, B.; Gao, S.; Cheng, S. The weight of unfinished plate: A survey based characterization of restaurant food waste in Chinese cities. Waste Manag. 2017, 66, 3–12. [Google Scholar] [CrossRef]
  58. Bido, D.S.; Silva, D.; Souza, C.A.; Godoy, A.S. Mensuração com indicadores formativos nas pesquisas em administração de empresas: Como lidar com multicolinearidade entre eles? Administração 2010, 11, 245–269. [Google Scholar]
  59. Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: Los Angeles, CA, USA, 2016. [Google Scholar]
  60. Bisbe, J.; Otley, D. The effects of the interactive use of management control systems on product innovation. Account. Organ. Soc. 2004, 29, 709–737. [Google Scholar] [CrossRef]
  61. Gerdin, J. The impact of departmental interdependencies and management accounting system use on subunit performance. Eur. Account. Rev. 2005, 14, 297–328. [Google Scholar] [CrossRef]
  62. Strotmann, C.; Göbel, C.; Friedrich, S.; Kreyenschmidt, J.; Ritter, G.; Teitscheid, P. A participatory approach to minimizing food waste in the food industry-A manual for managers. Sustainability 2017, 9, 66. [Google Scholar] [CrossRef] [Green Version]
  63. Silvennoinen, K.; Heikkilä, L.; Katajajuuri, J.M.; Reinikainen, A. Food waste volume and origin: Case studies in the Finnish food service sector. Waste Manag. 2015, 46, 140–145. [Google Scholar] [CrossRef]
  64. Goh, E.; Jie, F. To waste or not to waste: Exploring motivational factors of Generation Z hospitality employees towards food wastage in the hospitality industry. Int. J. Hosp. Manag. 2019, 80, 126–135. [Google Scholar] [CrossRef]
  65. Mondéjar-Jiménez, J.A.; Ferrari, G.; Secondi, L.; Principato, L. From the table to waste: An exploratory study on behaviour towards food waste of Spanish and Italian youths. J. Clean. Prod. 2016, 138, 8–18. [Google Scholar] [CrossRef]
  66. Cicatiello, C.; Franco, S. Disclosure and assessment of unrecorded food waste at retail stores. J. Retail. Consum. Serv. 2020, 52, 101932. [Google Scholar] [CrossRef]
  67. Eriksson, M.; Strid, I.; Hansson, P.A. Food waste reduction in supermarkets–Net costs and benefits of reduced storage temperature. Resour. Conserv. Recycl. 2016, 107, 73–81. [Google Scholar] [CrossRef]
  68. Stancu, V.; Haugaard, P.; Lähteenmäki, L. Determinants of consumer food waste behaviour: Two routes to food waste. Appetite 2016, 96, 7–17. [Google Scholar] [CrossRef] [PubMed]
  69. Bamber, L.S.; Jiang, J.; Wang, I.Y. What’s my style? The influence of top managers on voluntary corporate financial disclosure. Account. Rev. 2010, 85, 1131–1162. [Google Scholar] [CrossRef]
  70. Ran, G.; Fang, Q.; Luo, S.; Chan, K.C. Supervisory board characteristics and accounting information quality: Evidence from China. Int. Rev. Econ. Financ. 2015, 37, 18–32. [Google Scholar] [CrossRef]
  71. Amran, N.A.; Yusof, M.A.M.; Ishak, R.; Aripin, N. Do characteristics of CEO and Chairman influence Government-Linked Companies performance? Procedia Soc. Behav. Sci. 2014, 109, 799–803. [Google Scholar] [CrossRef] [Green Version]
  72. Menegazzo, G.D.; Lunkes, R.J.; Mendes, A.; Schnorrenberger, D. Relação entre características demográficas dos gestores e uso de informações para tomada de decisões: Um estudo em micro e pequenas empresas. J. Glob. Compet. Gov. 2017, 11, 90–110. [Google Scholar] [CrossRef]
  73. Lunkes, R.J.; Pereira, B.D.S.; Santos, E.A.; Rosa, F.S. Analysis of the relationship between the observable characteristics of CEOs and organizational performance. Contaduría Adm. 2019, 64, 1–22. [Google Scholar] [CrossRef]
Sustainability 12 06242 g001 550
Figure 1. PLS structural model with significant path coefficients.
Figure 1. PLS structural model with significant path coefficients.
Sustainability 12 06242 g001
Table
Table 1. Descriptive Statistics for Demographic Variables (N = 206).
Table 1. Descriptive Statistics for Demographic Variables (N = 206).
Panel A—Respondent Characteristics
Academic educationHigh school82GenderFemale79
Graduate119Male127
MBA5Age (years)21–3029
Tenure (years)Up to 59531–4092
6–106641–5040
+106351–6031
Over 618
Panel B—Restaurants Characteristics
Number employeesUp to 10034Food service (style)À la carte65
+100183Buffet (kg)11
Number seatsTo 20143Buffet free25
21–4057À la carte and buffet71
41–604Buffet (kg) and buffet free29
61–801Prato done3
+811Other2
Table
Table 2. Variables Measurement.
Table 2. Variables Measurement.
VariablesDescription
Perceived Utility of MCS (Broad-Scope)Information characteristics: (i) past and future facts, (ii) internal and external information, (iii) financial and non-financial information, and (iv) short-term and long-term thinking.
Use of FW InformationFood waste information use: (i) monitor internal compliance, (ii) internal processes, (iii) internal decision-making, (iv) external reports.
Financial PerformancePerception of the financial performance of the restaurants: involves operating, net revenue, and return on investment.
Non-Financial PerformancePerception of (i) customer satisfaction, (ii) quality of products and services provided, (iii) development or product and service innovation, and (iv) employee resources.
Control variablesManager characteristics (age, gender, and education) and the restaurants (number of employees and seats, and business maturity).
Table
Table 3. Factor Loading, Reliability and Variable Validity Results.
Table 3. Factor Loading, Reliability and Variable Validity Results.
VariableItemLoadingCronbach’s AlphaCRAVE
Perceived Utility of MCS (Broad-Scope)Deb10.8360.8600.8370.565
Deb20.743
Deb30.657
Deb40.758
Use of Food Waste InformationUse10.7170.8150.8530.592
Use20.804
Use30.719
Use40.832
Non-Financial PerformanceNof10.8400.8800.9080.713
Nof20.878
Nof30.836
Nof40.821
Financial PerformanceFin10.9100.9040.9380.835
Fin20.939
Fin30.890
Table
Table 4. Discriminant Validity—Fornell and Larcker.
Table 4. Discriminant Validity—Fornell and Larcker.
Broad-Scope MCSUse of Food Waste InformationFinancial PerformanceNon-Financial Performance
Perceived Utility of MCS (Broad-Scope)0.751
Use of Food Waste Information0.3560.770
Financial Performance0.0880.2610.914
Non-Financial Performance0.1370.3250.4980.844
Table
Table 5. PLS Structural Model with Significant Path Coefficients.
Table 5. PLS Structural Model with Significant Path Coefficients.
Original Sample (O)T Statistics (|O/STDEV|)P Values
Perceived Utility of MCS (Broad-scope) -> Use of Food Waste Inf.0.3564.9210.000 ***
Use of Food Waste Inf._ -> Financial Performance0.1112.0390.041 **
Use of Food Waste _Inf. -> Non-Financial Performance0.3254.2650.000 ***
Non-Financial Performance -> Financial Performance0.4626.0190.000 ***
* p > 0.10; ** p > 0.05; *** p > 0.01.
Table
Table 6. Manager and restaurants control variables.
Table 6. Manager and restaurants control variables.
Panel A—Manager Control Variables
RelationsAgeGenderEducation
Less Than or Equal to 30 yearsMore Than 30 yearsMaleFemaleBackground in BusinessOther Education
MSC-Sco→UseFW0.4040.395 ***0.318 ***0.377 ***0.355 ***0.595 ***
UseFW→FinPerf0.1920.0960.147 *0.1000.153 ***0.080
UseFW→Non-FinPerf0.396 **0.319 ***0.265 *0.364 ***0.406 ***0.020
Non-FinPerf→FinPerf0.596 ***0.441 ***0.488 ***0.452 ***0.412 ***0.545 **
Panel B—Restaurants Control Variables
RelationsEmployersSeatsBusiness Maturity
Up to 10 employers+10 employersUp to 100 seats+100 seatsUp to 10 years+10 years
MSC-Sco→UseFW0.458 ***0.345 ***0.364 ***0.473 ***0.426 ***0.389 ***
UseFW→FinPerf0.174 ***0.0790.184 ***−0.0030.216 ***0.127
UseFW→Non-FinPerf0.228 *0.400 ***0.359 ***0.2100.329 ***0.258 ***
Non-FinPerf→FinPerf0.643 ***0.312 ***0.471 ***0.475 ***0.681 ***0.272 **
Note: MSC-Sco = Perceived Utility of MCS (Broad-Scope); UseFW = Use of Food Waste Information; Non-FinPerf = Non-Financial Performance; FinPerf = Financial Performance; * p > 0.10; ** p > 0.05; *** p > 0.01.

Share and Cite

MDPI and ACS Style

Lunkes, R.J.; Rosa, F.S.d.; Lattanzi, P. The Effect of the Perceived Utility of a Management Control System with a Broad Scope on the Use of Food Waste Information and on Financial and Non-Financial Performances in Restaurants. Sustainability 2020, 12, 6242. https://doi.org/10.3390/su12156242

AMA Style

Lunkes RJ, Rosa FSd, Lattanzi P. The Effect of the Perceived Utility of a Management Control System with a Broad Scope on the Use of Food Waste Information and on Financial and Non-Financial Performances in Restaurants. Sustainability. 2020; 12(15):6242. https://doi.org/10.3390/su12156242

Chicago/Turabian Style

Lunkes, Rogério João, Fabricia Silva da Rosa, and Pamela Lattanzi. 2020. "The Effect of the Perceived Utility of a Management Control System with a Broad Scope on the Use of Food Waste Information and on Financial and Non-Financial Performances in Restaurants" Sustainability 12, no. 15: 6242. https://doi.org/10.3390/su12156242

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop