The Operational Impact of the Restaurants on Its Management System and Business Characteristics

The study was conducted to determine the pros and cons of using Restaurant Management System (RMS) based on the views of restaurant staff in the context of Bangladesh. The effectiveness of RMS use in terms of business features was examined and differences were sought. Data were collected using a structured questionnaire. Participants working in restaurants where RMS was used were of the view that RMS simplified operations, increased sales, and improved product / service quality, while those working in restaurants where RMS was not used had higher scores in expressions of difficulty using the system. In addition, RMS has a more positive impact on sales growth and product / service quality delivery according to the chain restaurant staff (p <0.05). Again, restaurant employees with a minimum score of 10 or fewer employees are included in relation to the positive impact of using RMS in terms of operations management and sales growth. Therefore, there is a relationship be- tween business size and RMS usage requirements.


INTRODUCTION
In today's digital age, the use of information technology (IT) in the products, processes and service processes of enterprises is increasing, expanding and evolving day by day (Bharadwaj et al., 2013). Of course, for restaurants, one of the biggest components of the service sector, it is impossible to be indifferent to this transformation (Udoh & Inuwa, 2016;Cavusoglu, 2019). The use of technology to manage operations in the restaurant sector also shows a growing trend (Gendron et al., 2018;Blocher & Alt, 2020). RMSs are IT (such as automation systems), which provide important opportunities for fast, efficient, and mostly error-free execution of operations and management processes in restaurants (Udoh & Inuwa, 2016). Just as in various industries (Sambamurthy et al., 2003), the use of IT in restaurants will contribute to improving business performance, increasing competitiveness and adapting to changing conditions quickly (Gao & Su, 2018;Asrihapsari & Setiawan, 2020). For example, during the Covid-19 epidemic in 2020 we gave customers the opportunity to access menus with their mobile phones, place orders and even pay online or offline via restaurant data matrix with RMS, in an effort to reduce the spread of the disease . Again, during the epidemic period, there were times when country administrations only allowed package services and increased consumer preferences for package services as a personal precaution (Bracale & Vaccaro, 2020;Lim et al., 2020;Parks et al., 2020). During this sudden transition, thanks to package / delivery staff/ delivery application (Such as Food Panda) tracking systems of RMS software (Memis Kocaman & Kocaman, 2019) can provide an advantage for restaurants to successfully manage the growing package service demand. A large number of RMSs are commercially available in the market with their ever-evolving features. It is noteworthy that in recent years these have been used in restaurants with increasing frequency. The increased use of RMSs can be seen as an indication that the expected benefits have been achieved. However, there is limited research in the literature on the functional effects of RMSs (Moreno & Tejada, 2019). In a qualitative survey, the pros and cons of RMS were determined in accordance with the results of interviews conducted with a sample of 7 people, including restaurant managers, head waiters and marketing managers of service providers (Memis Kocaman & Kocaman, 2014). In his research, Cavusoglu (2019) evaluated the perspectives of business IT managers regarding the use of mobile POS (point of sale) in restaurants. The current study focuses on the perspectives of operation staff and operations managers working in restaurants on the practical implications of using RMS, as they form groups using it for direct purposes. In this sense, in order to contribute to the literature, this study was conducted to evaluate the potential disadvantages of RMS that provide the expected benefits in practice and use them based on the experience of restaurant staff. In addition, it was observed whether the use of RMS has different effects depending on the functional model, location, size and type of enterprise. Thus, it will be ensured that the restaurant operators in the sector have information on what results they will achieve if they choose RMS according to the characteristics of the business. In addition, the research will provide feedback opportunities for service providers operating in the RMS sector.

LITERATURE REVIEW
Thanks to RMS, restaurants will have the opportunity to host more customers due to shorter service times, increase https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 1(4) 35-43, 2022 customer satisfaction, increase customer loyalty, reduce labor costs (thus competitive advantage), and increase productivity. And service efficiency (thus reducing labor costs due to increased staff efficiency). Sales and profits can be increased in the direct and indirect ways mentioned above (Kimes, 2008). RMSs, on the other hand, come with costs for business (Udoh and Inuwa, 2016). Hardware, software license fees, internal staff and external service provider fees, system maintenance, network and telecom fees may be listed as basic costs for using RMS (Cavusoglu, 2019). Although the cost of using technology reached 10.5% of their income for some businesses (Thompson et al., 2014), 69.1% of restaurants said they allocated 1% or less of their income to IT use, and 11.5% reported that they spent it. 2% of income from IT use (Kobanoglu, 2007). In another study, more than half (57.4%) of IT managers for restaurants reported that 1% or less of their total sales revenue was allocated to IT use. Also, the inadequacy of the budget allocated by the IT managers of the restaurant (Cavusoglu, 2019) was emphasized. Therefore, budget allocation should be planned to effectively assess the cost of RMS in business (Kimes, 2008) including the expected revenue growth and other potential benefits from using RMS. RMS consists of two parts as software and hardware. Tools such as computers, kiosks, hand terminals, monitors, printers that integrate with software designed specifically for restaurant work and operations to create hardware form RMS (Memis Kocaman & Kocaman, 2019). Many patented RMSs have been created since their first examples were created as a console that tracks restaurant table occupancy (Auger, 1967), and where customer orders were entered and bills were calculated (Wolf, 1967) years ago, and many more. More advanced and more efficient RMSs are widely used in industry. The RMSs currently used by customers for accessing menus, ordering, making payments, finding the right place in the parking lot and accessing menus with a monitor on their smart phones or customer tables have become more efficient. And restaurants, and for accounting staff and managers to access various statistical data such as accounting, inventory, employee performance, etc., and to obtain instant and periodic reports and orders from suppliers (Coleman et al., 1997;Coleman, 1998;Leifer, 2003;Geet al., 2003;Tripp and Vaszary, 2006;Doran, 2010;Burns et al., 2013). There are even smart RMS studies created using Internet of Things (IoT), cloud technology, near field communication (NFC) sensors and proximity sensors (Saeed et al., 2016;Saraubon et al., 2018).

MATERIAL AND METHODS Participants
The study was conducted to reveal the effective effects of RMS use in restaurants. To date, there is a global classification for the restaurant industry, both nationally and internationally (Parsa et al., 2020). The study, based on a restaurant classification conducted by (Davis et al 2018), included fine dining, casual, coffee shop, fast food, take-away and pop-ups. The business, which is defined as a patisserie by the Bangladeshi public and business people, but whose concept is partly similar to a casual restaurant / fast-food restaurant and partly similar to a coffee shop, was included in the study. Again, businesses that define themselves as "bistro, brasserie, catering" were also included in the study, as their ideas partly coincide with casual restaurants, fast food restaurants and / or take-away. Participants were selected through sampling among those who are actively working in restaurants and those who have voluntarily agreed to participate in the study. Restaurant managers in different cities of Bangladesh (metropolis and district) were approached face to face and through social media with different ideas (casual restaurant, fast-food, cafe, patisserie etc.) and our aim was to inform them and get their feedback. The questionnaires were distributed to business managers who agreed to take part in the study and the questions were given to the restaurant staff. Questionnaires were given to 500 people working in different positions in the restaurant. Incomplete questionnaires were excluded from the assessment, and the study was completed with data from 385 individuals. Among the participants, 64.7% were males, 46.2% were graduates of higher education, and the average age was 31.99 ± 8.97 years (min. = 20, maximum = 66 years). Considering their distribution according to their position in the restaurant, 22.6% of the participants were business managers, 2.6% were business owners, 27.6% were culinary staff (culinary chef / chef cook / assistant chef), 24.4% were waiters, 10.4% were restaurant chefs, 5.2 % Bellboy, 1.6% of them were bartenders, 1.3% were cashiers and 4.4% had multiple jobs.

Measurement and Scaling
Research data were collected using a questionnaire form. The questionnaire consists of three parts: the first part of the questionnaire consists of close-end questions to determine the demographic characteristics of the participants (4 questions) and the second part consists of questions to determine the information about the business they are currently operating in (6 questions). The third part of the questionnaire has a scale with 22 statements based on the employees' perspectives on the advantages and disadvantages of using RMS in restaurants. The scale statement was prepared by the researcher based on the relevant literature (Memis Kokaman & Kokaman, 2014. The RMS usage view scale was scored with a 5-point Lycart type scale (5 = completely agree, 1 = completely disagree). The questionnaire was applied to 60 people with work experience in the sector for piloting and corrected questions and statements that were not understood according to the pilot test results. An exploratory factor was analyzed to determine the construction validity of the scale used in the study. The KMO value of the scale was found to be 0.882, and the https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 1(4) [35][36][37][38][39][40][41][42][43]2022 results of the Bartlett test were found to be statistically significant (p <0.001). Accordingly, the need for normal distribution was achieved. According to KMO and Bartlett test results, it was concluded that the data was suitable for factor analysis (Jeong, 2004: 70). Based on the results of the factor analysis, it was determined that the scale consists of 5 factors. The following labels based on the properties of the rotating component matrix for the RMS approach were assigned to the following factors, operations management, system usage difficulty, sales growth, product / service quality, and technical costs, respectively (Table 1). Operations Management consists of 6 items, including first factor, factor load from 0.369 to 0.895. The total variability explained by the first factor is 30.017%, and the reliability coefficient is 0.825. The difficulty of using the system consists of 4 items, including factor II, factor load from 0.696 to 0.867. The total variability explained by the second factor is 12.464%, and the reliability coefficient is 0.821.
Sales growth consists of 4 items with factor III, factor load between 0.545 and 0.900. The total variability defined by the third factor is 6.803%, and the reliability coefficient is 0.792. Production / service standard consists of 5 items with 4th factor, factor load from 0.325 to 0.804. The total variability defined by the fourth factor is 4.989%, and the reliability coefficient is 0.723. The technical cost consists of 3 items with a factor of between factor 5, 0.558 and 0.849. The total variability defined by the fifth factor is 4.782%, and the reliability coefficient is 0.635 (Table  1). According to these results, the difficulty in using operation management components and systems has a very high reliability level and there is a high reliability of components of sales growth, production / service quality and technical costs (Nunnally, 1967: 248). As seen in Table 1, the operations management, sales growth, and scale production / service standard components consist of statements indicating the advantages of using RMS. The difficulty in using the system components of the scale contains statements that   (Table 1). The path made in the Confirmation Factor Analysis (CFA) analysis for the scale is given in Figure 1. Appropriate indicators of the model show that χ2 / df = 2.083, GFI = 0.916, AGFI = 0.890, CFI = 0.930, RMSEA = 0.053, RMR = 0.047. It can be seen that the FA2 / df, GFI, CFI, RMSEA and RMR fit indicators calculated in the CFA analysis provide acceptable fit indicators, only the AGFI index (AGFI> 0.90) seems to be close to the fit index and is provided.

Data Analysis
Data were analyzed through the SPSS 21 program and the confidence level was 95%. Kurtosis and skewness values obtained for items on scales between +3 and −3 are considered sufficient for a general distribution. Since the diagonal and cartilage values obtained from the scores were between +3 and −3 (Table 2), normalization was provided and parametric test techniques were used in the analysis. When evaluating the advantages and disadvantages of using RMS, it was predicted that business features could distinguish the perceived effects of using RMS, and business features were tested as independent variables. Business features tested as independent variables are RMS usage status, operational model (independent or chain), city size, number of employees, working hours and type of restaurant (casual restaurant, fast food, café, patisserie etc.). In terms of management, restaurants with a single branch were classified as "independent restaurants" (Dorf, 1992), casual or family style chains with multiple branches, franchising restaurants (Young et al., 2007), restaurants belonging to a group of restaurants. Chain Restaurant ". The business location was classified as a metropolis, province and district according to its size. In Bangladesh, districts with a population of over 750,000 are defined as metropolises. Employees of restaurants in 15 metropolitan areas and 10 districts took part in the study. The study also included restaurants in 19 districts, which are affiliated with both the metropolis, and established outside the metropolitan / provincial center, in terms of provisional administration, and the location of these businesses as "districts". Participants from one or more restaurants in each city were involved. Differences in scores according to business characteristics were analyzed using parametric T-tests and one-way Anova tests.

RESULT AND DISCUSSION
The characteristics of the restaurants in which the study participants worked are given in Table 3. The results of the T-test conducted to examine the scores obtained from the perspective of RMS usage in terms of RMS usage in  Tables 4 and 5. The average scores of those who worked in restaurants that used RMS were higher in operations management (p <0.001), increased sales (p <0.05), and product / service quality (p <0.001) ( Table 4). That is, employees at organizations that used RMS have expressed more positive feedback about the benefits of using the system. In other words, restaurants that are actively staffed by relevant literature (Prasad et al., 2005;Kims 2008;Doran 2010;Huber et al., 2010;Gao & Su 2018; Mastrobarte 2018; Cavusoglu 2019). By using RMS they have ensured the convenience of managing the activities provided by RMS, their contribution to achieving product / service quality, and their positive impact on sales growth (and consequently revenue growth for the business) (see Table  1 for relevant statements). It can be concluded that employees working in restaurants who do not use RMS have a biased approach with high average scores (p <0.05) for difficulty using the system (Table 4).
Both restaurants where RMS is used (= 4.19 ± 0.66) and restaurants that do not use RMS (Х = 4.02 ± 0.71) have higher average scores for staff technical costs ( Table  4). No statistically significant differences were found between the groups in terms of technical costs (p> 0.05).     Kims, 2008). The benefits to the business outweigh the costs (Green and Weaver, 2008). Another problem is that software and hardware need to be constantly updated due to rapidly evolving technologies (Thompson et al., 2014). Furthermore, due to the growing interest of consumers in new technologies, it is also challenging for businesses as consumers expect restaurants to use more technology than their current capacity (Cavusoglu, 2019). Also, the low durability of the devices used is another problem (Prasad et al., 2005;Cavusoglu, 2019). Due to these reasons, the cost of information technology for restaurants increases even more. Cavusoglu (2019) concluded in his research that at present there is not enough budget allocated for the use of technology in this sector. At this point, business managers / investors need to make an accurate profit / loss analysis. Technical costs are not limited to financial resources (see Table 1 for related statements). It also needs staff and managers who can use their RMS effectively. This requires training existing staff and hiring trained system administrators in their IT systems (Cobanoglu, 2007;Blocher ¨ & Alt, 2020). In this way, it will be possible to select the appropriate hardware and software for the needs of the enterprise (Green & Weaver, 2008) and use the capabilities provided by RMS installed in the enterprise with maximum performance (Huber et al., 2010). Unfortunately, it should be noted that in restaurants, IT managers are generally responsible for the system in addition to their various responsibilities (such as accounting managers, restaurant managers) and their work pressures create a barrier to their effective use of the system. In addition, the lack of information about RMS, which prevents both employees and IT managers from fully mastering the system, should be cited as another obstacle to using the system effectively (Cavusoglu, 2019). A statistically significant difference was found between the groups currently operating in the business with different operational models in terms of sales growth and product / service quality (p <0.05). In both cases, the average score of employees working for the chain business was higher (Table 5). According to these results, the experience of staff working for chain enterprises confirms that RMS provides a standard in terms of production and service quality across different branches of enterprise and facilitates sales growth in chain enterprises (Memis Coca-Cola & Coca-Cola, 2019). In a study conducted by Cavusoglu (2019), it was determined that some of the software contained in RMS seemed to be more important in chain restaurants than in independent restaurants. In another study, it was found that most applications were used more in chain enterprises than in independent enterprises (Huber et al., 2010). Based on these results, it can be explained that RMSs are more required in chain restaurants.
The results of the ANOVA test conducted to test the scores obtained by the participants from the perspective of RMS usage in restaurants according to the size of the city are given in Table 6. Statistically significant differences were found among participants by city size, with restaurant staff participating in the study currently operating in terms of operations management, sales growth, production / service quality (p <0.05). The average score of operations management and sales growth components increases in proportion to the size of the city where the business is located (p <0.01), and product / service quality, metropolitan business employees average scores, and provincial centers close together, while their average scores in districts are lowest (p <0.001). ) (Table 6). On the other hand, there was no significant difference between the participants in terms of the difficulty of using the system and the technical cost in terms of the size of the business city (p˃0.05).
The results of the ANOVA test conducted to test the scores obtained from the perspective of RMS usage in terms of the capacity of the restaurant where the participants were working are given in Table 7. According to the total number of employees in the restaurant where the participants were working, in operations management and sales growth, the average score of the group employed between 21 and 30 people was the highest, with an average score of 10 people. Or less employees was the lowest (Table 7). There is a statistically significant difference between the groups (p <0.05). In terms of technical costs, those who worked with employees between 21 and 30 had the highest average scores (Х = 4.42 ± 0.48), while those with 41 or more employees working in the business had average scores (Х = 4.02 ± 0.84) and 10 or its lower staff (Х = 4.03 ± 0.64) was lower than other groups (p <0.01) ( Table 7). Similar to the variables in the  (Table 5) and the size of the city where the business is located (Table 6), there was no significant difference between the participants in the difficulty of using the system (p >0.05).
The results of the ANOVA test conducted to test the scores obtained from the point of view of RMS usage in terms of operational duration and type of restaurant in which the participants were working are given in Tables  8 and 9. When participants' perspectives were assessed in terms of the business operational period of the business where they were currently operating, the lowest score in the technical cost of RMS only was for business staff managed for 3 years or less (7 = 4.01 ±) 0.79, p <0.05), and A significant difference was found. The operational period of the enterprise (p 0.05) (Table 8) found no differences between the other elements related to the perspective of the impact of RMS usage.
The study participants found no significant differences in any of the factors related to the impact of RMS use by the type of business (restaurant, fast-food, cafe, patisserie, etc.) where they were working (p˃0.05) ( Table  9). Therefore, it can be said that the positive and negative effects of using RMS are the same due to the functional similarity of all the restaurants with different concepts. Huber et al. (2010) in their study stated that some of the software used in different restaurants were different (p <0.05). In other words, although the features of RMS used differ in terms of business features, it can be said that all restaurants have made positive contributions to RMS.

LIMITATIONS
The study is limited to a few Bangladeshi restaurants. Another limitation of this study is that the RMSs used in the restaurants where the participants worked were

CONCLUSION
In this study, the aim was to reveal the effects of RMS use in restaurants based on employee perspectives. While the positive advantages of using RMS in terms of operations management, sales growth, and product / service quality come to the fore, the fact that it brings technical costs in terms of staff training and installation costs is a disadvantage that a large number of research participants agree on. The disadvantage of using the system was considered as another disadvantage of using RMS, but it was a disadvantage that the participants agreed with the lowest score level. Also, restaurant employees who do not use RMS had a higher (biased) opinion about the difficulty of using the system compared to businesses where RMS is used. According to participants working in Chain Enterprise, the advantages of using RMS outweigh the disadvantages in terms of sales growth and product / service quality. The positive effects of using RMS in terms of operations management and sales growth were reflected by the lowest scores by restaurant staff, including the lowest number of employees. Accordingly, it can be assessed that in the case of business growth, the positive effects of RMS can be better noticed by employees. The type of restaurant does not affect the attitude towards RMS usage. In various studies, the impact of using RMS for consumers and restaurant investors can be evaluated. In addition to conducting new studies, the impact of RMS use on restaurant workers and / or managers in different countries could be examined. Based on this research, it can be claimed that the use of RMS contributes highly to the management of restaurants and facilitates business operations and management. Difficulties in using the system, such as staff training and installation costs, can be overlooked considering its positive effects. RMS will contribute to the success of the business in providing more professional services and management processes in restaurants. In other words, the use of RMS is a preferred technology in meeting expectations.