An analysis of technology applications in the restaurant industry

Muhittin Cavusoglu (College of Education, University of South Florida, Tampa, Florida, USA)

Journal of Hospitality and Tourism Technology

ISSN: 1757-9880

Article publication date: 25 January 2019

Issue publication date: 8 March 2019

Abstract

Purpose

This study aims to examine the utilization of Front of House (FOH) and Back of House (BOH) technology applications in different types of US restaurants along with their level of IT management and explore the importance of these technology applications to restaurant operations.

Design/methodology/approach

Survey data were collected from 500 randomly selected restaurant technology managers who subscribe to Hospitality Technology Magazine. The sample group represented 67,299 restaurant units. Data analysis was organized into three parts (descriptive, exploratory factor analysis, and independent samples t-test).

Findings

For FOH, the top-five point of sale (POS) technologies used are POS hardware, touchscreen, POS software, gift card integration and integrated credit card swipe into POS. At the BOH, the top-five POS technologies used are accounting/financial software, enterprise reporting, inventory management software, kitchen printers and company intranet.

Originality/value

This is one of the first studies to include a variety of technologies used in restaurants. Most existing studies focus on a single technology or a small number of them. However, this study provides an overall perspective on a variety of restaurant technologies from FOH to BOH. It also includes mobile POS technologies.

研究目的

本论文旨在研究美国各种类型饭店的前厅(FOH)和后厨(BOH)的各种科技应用系统以及其信息科技管理水平, 此外, 本论文还分析了这些科技应用对于饭店运营的重要性。

研究设计/方法/途径

本论文采用问卷采样形式, 从订阅了酒店科技杂志(Hospitality Technology Magazine)的饭店科技经理中随机抽取500名经理为问卷样本, 此样本代表了67,299家饭店。数据分析方法共分为三个部分(描述型、因子分析、和独立样本t检定)。

研究结果

对于FOH而言, 排名前五的POS科技包括POS硬件、触摸屏、POS软件、礼品卡管理、和信用卡与POS系统链接。对于BOH而言, 排名前五的POS科技包括会计/财务软件、企业报表、库存管理软件、厨房打印机、和公司内网。

研究原创性/价值

本论文是仅存的几篇研究多样饭店科技的文章之一。大多数文章只是关注一种或者少数几种科技。然而, 本论文提供从FOH到BOH多种饭店科技的分析研究, 包括移动POS科技等。

关键词 饭店科技、前厅科技、后厨科技、移动POS

Keywords

Citation

Cavusoglu, M. (2019), "An analysis of technology applications in the restaurant industry", Journal of Hospitality and Tourism Technology, Vol. 10 No. 1, pp. 45-72. https://doi.org/10.1108/JHTT-12-2017-0141

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited


1. Introduction

The service industry is one of the most important industries in the USA (US Bureau of Economic Analysis, 2017), and the restaurant industry is one of the biggest components of the service industry. Restaurant industry sales were projected at over $798bn at the end of 2017 (National Restaurant Association, 2017a) and are expected to a growth in 2018 as well (Carbonara, 2017). Moreover, expenditures in food services increased steadily from 2010 to 2016, rising from $532.2bn to $727.0bn, respectively (US Bureau of Economic Analysis, 2017). The proportion of food services in total personal consumption expenditures was 5.67 per cent (US Bureau of Economic Analysis, 2017), whereas it was almost four percent of the total US Gross Domestic Product in 2016 (National Restaurant Association, 2017b; US Bureau of Economic Analysis, 2018). As a result, the restaurant industry is one of the fastest-growing industries in the US.

Technological advancements have influenced the restaurant business. Technology has become a major factor in business operations, with the blossoming of the telecommunications industry, advancements in computer capabilities and the development of software to support delivery of services (Buhalis, 1998; Olsen and Connolly, 2000; Parsa and van der Rest, 2017). The restaurant industry is no exception (Collins et al., 2017; DeMicco et al., 2015). Previous studies have shown that companies that invested in information technology (IT) were able to achieve revenue growth as well as cost savings (Kauffman and Walden, 2001; Kulatilaka and Venkatraman, 2001; Patil and Wongsurawat, 2015; Ruth et al., 2015; Sambamurthy et al., 2003). As a result, “with the advent of new technology and its impact on restaurant operations, one would believe that most firms in the restaurant industry would be IT-oriented in the production and delivery of goods and services” (Oronsky and Chathoth, 2007, p. 942). Still, IT may not be seen as a major function in restaurants; in fact, less than half of IT managers in restaurants are solely responsible for IT, while the rest have a main function (e.g. accounting manager, restaurant manager) within the restaurant and have IT management responsibilities as a second role (Cobanoglu, 2007). Cobanoglu (2007) stated:

[…] this simple fact indicates a potential problem within the restaurant industry wherein managers who have a core responsibility in an area other than technology would be unable to spend sufficient time on technology management […] (p. 40).

Restaurant technology may be divided into two categories: Front of House (FOH) and Back of House (BOH) technologies that support FOH and BOH operations (American Hotel and Lodging Association [AH&LA, 2006; Walker, 2014). The primary technology system used in these operations in a restaurant is the point of sale system (POS), which is “a network of cashiers and server terminals that typically handles food and beverage orders, transmission of orders to the kitchen and bar, guest-check settlement, timekeeping, and interactive charge posting to guest folios” (Collins and Cobanoglu, 2008, p. 245). Another version of POS used in the restaurant industry is the handheld POS terminal, also known as a mobile POS device or tableside ordering device (Raina, 2017). A handheld POS terminal is a portable device with all the capability and main functions of a pre-check POS system, as well as integrated tableside ordering and payment devices (Kasavana, 2011). These restaurant technologies that accept, process, store or transmit credit card payments must comply with a set of security standards known as the Payment Card Industry Data Security Standards (PCI Security Standards Council, n.d.). The existence of technology-based systems in restaurants and how they are used in restaurants is the focus of this study. Restaurants may have different approaches to managing technology based on their level of receptivity to innovation (i.e. whether they are innovators or followers) from the business and technology perspective (Collins et al., 2017).

Restaurant managers continuously face the problem of selecting technologies for their businesses, and fast growth and development of restaurant technology makes it difficult to keep up with solutions that are important for the industry. This study will close that gap. Therefore, the purpose of this study is to examine utilization of FOH and BOH technology applications in different types of US restaurants along with their level of IT management and explore importance of these technology applications to restaurant operations.

1.1 Research questions

The following research questions are proposed in this study:

  • What are the main drivers for IT investments in restaurants?

  • What are the main drivers for restaurants’ IT efforts?

  • What is the top challenge facing restaurants’ technology departments?

  • What POS FOH technology features are used in restaurants?

  • What is the importance of POS FOH technology features to restaurants’ operations IT managers?

  • What POS BOH technology features are used in restaurants?

  • What is the importance of POS BOH technology features to restaurants’ operations IT managers?

  • What are restaurant IT managers’ perceptions of mobile POS systems?

  • What are restaurant IT managers’ perceptions of Payment Card Industry Data Security Standards (PCI DSS) practices in restaurants?

  • To what extent are US restaurants compliant with PCI DSS requirements?

  • What is the level of PCI DSS compliance in restaurants?

2. Literature review

2.1 Hospitality industry and information technology

According to Ukpabi and Karjaluoto (2017), IT is one of the most essential tools in the hospitality industry. Collins et al. (2017) claim that IT is an inseparable part of the hospitality industry.

A vast amount of capital has been invested on IT in the hospitality industry to increase revenues, decrease costs and improve service quality for customers (Huo, 1998; Siguaw et al., 2000; Tan and Netessine, 2017). Furthermore, by implementing IT, hospitality businesses have realized a positive and significant correlation between the use of IT and competitive advantage (Barney, 2015; Madhavaram and Appan, 2015; Sun et al., 2017). Researchers from academia as well as industry professionals have highlighted the valuable impact of IT and have paid attention to IT’s contributions to the hospitality industry (Collins et al., 2017; Ham et al., 2005; Hua et al., 2015; Law et al., 2014; Siguaw et al., 2000).

2.2 The relationship between information technology and the restaurant industry

In the tourism and hospitality industry, total trip spending is categorized into four main groups: food and beverages, lodging, transportation, and fees and admissions. Shares for these categories were 23.98 per cent, 22.96 per cent, 43.95 per cent and 9.11 per cent, respectively (Pauiln, 2012). Also, restaurant sales were projected at over $798bn in the USA in 2017 (National Restaurant Association, 2017a). The above figures show the importance and economic impact of the food and beverage industry on the hospitality industry.

In addition to its impact on the US economy and the food industry, the restaurant industry is one of the main branches of the hospitality industry. As IT is commonly used in the hospitality industry, convenient technology can bring many benefits to the restaurant industry (Gao and Su, 2017; Kimes, 2008). Therefore, IT makes a significant difference (Hayes, 2002) and strongly affects restaurants’ performance (Devaraj and Kohli, 2003; Gao and Su, 2017).

Kimes (2008) points out that the benefits of technology include shorter time spent in the ordering process (e.g. handheld terminals), enhanced processing in food production (e.g. kitchen technology), quicker service time (e.g. table management systems), faster payment (e.g. handheld terminals), improved seat turnover or turnaround time (e.g. near-field communications and/or table management systems) and decreased labor cost (e.g. labor management systems, online reservation systems and POS integration into online ordering). Other improvements technology may provide include competitive advantage, enhanced productivity, higher profitability (Kasavana, 2011), cost minimization (Thompson et al., 2014), increased demand (Gao and Su, 2017), better employee management and customization of customer preferences (Ansel and Dyer, 1999). Moreover, “restaurant technology provides management with the right information at the right time, resulting in fewer costly mistakes, better forecasting, higher productivity, and improved marketing know-how” (Collins and Cobanoglu, 2008, p. 245).

Oronsky and Chathoth (2007) state:

[…] information technology has played some role in changing a customer’s dining experience over the years – the way in which the meal is prepared, the speed at which it is delivered, the way an order is received […] (p. 941).

In addition, Buhalis (2003) stressed that IT has been changing the main dynamics of the industry and reshaping it. Liddle (2002) claimed that “nearly half of the ‘sure-thing’ changes that will reshape foodservice by 2010 will involve technology” (p. 4).

According to Oronsky and Chathoth (2007), recent IT trends in the restaurant industry include customer feedback systems (customer relationship management [CRM], social media activity integrated into CRM platform, and real-time, Web-based reporting), repeat business management applications (e-reservation systems, POS integration into online ordering), marketing management systems (POS software and handheld terminals), restaurant operation systems (wireless credit card authorization or mobile POS, revenue management system, accounting/financial software, and integrated cost control software or inventory management tools), human resources management systems (labor management systems, labor screening and recruitment systems and company intranet) and BOH management systems (kitchen technologies, kitchen management systems, kitchen displays and kitchen printers). Clearly, the role of technology in the restaurant industry is important.

2.3 Technology systems and applications in the restaurant industry

While many technology systems are used in the restaurant industry, a review of the literature demonstrated that there have not been as many studies on restaurant technology as other fields in the hospitality industry (Huo, 1998; Lam et al., 2007; Kim et al., 2008). According to AH&LA’s Food and Beverage Systems Report (2006), technologies used in restaurants are divided into two main groups: systems and applications used in FOH operations, and those used in BOH operations.

2.3.1 Front of house operations systems.

FOH operations can be defined as the process that begins with taking orders and delivering food to guests and ends with payment (AH&LA, 2006). Many technology applications are used in this process, including POS systems, POS integrated modules, POS integrated payment applications and some emerging technologies for FOH operations.

2.3.1.1 Point of sale integrated payment applications.

Customer-facing payment technologies (e.g. tableside payment devices, mobile wallets and mobile remote payment/wireless credit card authorization) have been increasing in popularity among both restaurants and consumers (Kimes and Collier, 2014). According to Horovitz (2012), 44 per cent of casual restaurant consumers now prefer to use tableside payment.

Although Cobanoglu et al. (2012) state that “mobile POS technology reduces credit card skimming” (p. 15), they also stress that network protection is itself a serious concern. According to the 2014 Trustwave Global Security Report, 59 per cent of credit card fraud in 2013 came from the USA. In total, 18 per cent of this fraud came from the food and beverage industry, and 11 per cent came from the hospitality industry (Trustwave Global Security Report, 2014). Furthermore, in 2013, 1.165 million complaints were recorded regarding fraud; 17 per cent of fraud complaints came from credit card holders (Federal Trade Commission, 2014). In 2016, fraud complaints from credit card holders almost doubled and reached 32 per cent (Federal Trade Commission, 2017). Because of the increase in network security and fraud issues in the last decade, the Payment Card Industry Data Security Standards (PCI DSS) were established in 2006 by global payment brands including American Express, Discover Financial Services, JCB International, MasterCard, and Visa Inc. (Berezina et al., 2012). As a result, all companies that accept credit cards must abide by the standards or requirements initiated by PCI DSS (Connolly and Haley, 2008). The PCI data security standards are provided below (PCI Security Standards Council, 2013, p. 11):

  • install and maintain a firewall configuration to protect cardholder data;

  • do not use vendor-supplied defaults for system passwords and other security parameters;

  • protect stored cardholder data;

  • encrypt transmission of cardholder data across open, public networks;

  • use and regularly update anti-virus software or programs;

  • develop and maintain secure systems and applications;

  • restrict access to cardholder data by business need-to-know;

  • assign a unique ID to each person with computer access;

  • restrict physical access to cardholder data;

  • track and monitor all access to network resources and cardholder data;

  • regularly test security systems and processes; and

  • maintain a policy that addresses information security for all personnel.

In addition to the requirements above, other practices may improve credit card transaction security. These practices can include using point-to-point encryption (P2PE), outsourcing PCI compliance efforts, and tokenization at the card swipe (PCI Security Standards Council, 2011, 2012, 2013). Moreover, EMV (Europay, MasterCard, Visa) is another practice that can be deployed. EMV is a type of Chip-and-PIN card introduced by some major card issuers, Europay, MasterCard, and Visa. Since an advance technology is used, unique transaction identification is created in each transaction. Therefore, the same identification number cannot be reused (Rodríguez, 2017). As a result, EMV provides a secure transaction and help issuers reduces chances of fraud. Although EMV increases transaction security, it does not override PCI but works well together (Figliola, 2016; Thomas, 2014).

Additionally, researchers identified several issues and barriers to PCI DSS compliance in restaurants (Collins et al., 2017): brand responsibility, lack of budget, the high burden of PCI DSS compliance on merchants, lack of knowledgeable staff, complexity of PCI DSS, and limited support from vendors and top management.

2.3.1.2 Emerging front of house operation technologies.

Advancement in technology is bringing useful tools and applications to daily life as well as the restaurant industry. According to Lorden’s (2012) interview, David Lehn, vice president of IT for Noodles and Company, stated that a modern restaurant should have digital menu boards and signage. R.P. Rama, vice president and chief technology officer for JHM Hotels, predicts that biometric tools will bring many benefits to the hospitality industry, stating they “would resolve any issues with stolen or lost credit cards or even getting duplicate cards” (Lorden, 2012, para. 5). Digital signage devices, meantime, can be used as interactive digital menus, for promoting special meals and discounts, sharing photos and videos about the restaurant and informing guests about forthcoming events (Sonnenshein, 2014). According to the Digital Signage Federation (N.A.), a digital sign can easily be a standard fixture of a restaurant and offers many benefits, such as cost savings, promoting special and new items, and providing upselling opportunities. A biometric reader, another innovative technology for the restaurant industry, identifies people by their unique physical traits (such as fingerprints, retina prints, and voices) and stores these identifiers to use for later to verify the same customers based on their profile in the database, which includes their identities linked to their unique makers (Collins et al., 2017). As a result of implementing biometric-enabled POS, Hooters, one of the biggest chains, with 452 world restaurants, accomplished a reduction in transaction fraud, payroll fraud, and food costs. In addition, Hooters got rid of swipe card replacement cost and unauthorized overrides (Hospitality Technology Magazine, 2015).

2.3.2 Back of house operations.

According to AH&LA (2006), it is difficult to manage a restaurant without measuring and controlling main performance indicators. Technology plays a key role in BOH operations (Susskind, 2017). BOH operation systems make it possible to measure and monitor indicators such as inventory, financial status, labor scheduling and productivity, and cost of food (AH&LA, 2006). Important BOH operations systems include:

  • accounting/financial software;

  • labor management systems/labor screening and recruitment tools;

  • CRM systems;

  • business intelligence systems;

  • inventory management applications;

  • company intranets;

  • kitchen management systems (kitchen displays and kitchen printers);

  • integrated cost control systems;

  • cloud-based applications;

  • enterprise management systems (EMS);

  • enterprise reporting systems (ERS) and real-time based reporting;

  • disaster recovery systems; and

  • personal digital assistants (PDAs) and intra-day reporting.

2.3.2.1 Accounting/financial software.

Accounting/financial software is connected with POS systems and creates sales transaction reports and economic events (Tesone, 2005). Many BOH software programs include a variety of accounting modules, but most of them generally include at least accounts receivable, accounts payable, payroll accounting, inventory accounting, purchasing and financial reporting modules (Kasavana, 2011). The main reason for using accounting software is to keep track of all financial transactions among company stakeholders (Collins et al., 2017). Moreover, the main benefits of accounting applications are “managing cash flow, collecting monies owed by customers, controlling and tracking expenditures, evaluating financial status, and tracking monies owed to creditors” (Collins and Cobanoglu, 2008, p. 225).

2.3.2.2 Labor management systems and labor screening and recruitment tools.

Labor management systems help the restaurant create work schedules for better and more efficient workforce forecasting and give managers better opportunities to analyze employee performance and to control clock-in/clock-out hours (AH&LA, 2006). According to the National Restaurant Association (2017c), employee turnover rate in the accommodations and restaurant industry was 72.9 per cent in 2016, whereas it was 46.1 per cent in the overall private industry. Therefore, some POS systems provide labor screening, which offers restaurateurs opportunities to keep track of employee records, tax information, employee benefits and recruitment tools (AH&LA, 2006). This offers restaurateurs online control over new position applicants to better hire the right employees and to train them effectively (AH&LA, 2006). As a result, labor-management/screening tools can help decrease the turnover rate.

2.3.2.3 Customer relationship management systems.

These IT management systems allow restaurants to focus on customer activities to maintain customer loyalty in the long run (Lo et al., 2010; Rahimi and Kozak, 2017). With the help of CRM systems, customers’ contact information (e.g. email, mailing address, cell phone number) can be kept and used to share promotional campaigns with customers (Collins et al., 2017). Another important feature of CRM systems is that they provide a record of useful information such as name and meal preferences, so servers can greet customers by name and help them with their meal choices (DeMicco et al., 2015).

2.3.2.4 Business intelligence systems.

These modules integrate with BOH operation systems and provide detailed reports such as budgets, profit/loss statements, balance sheets and daily reports (e.g. guest counts, food cost, and labour cost) that keep managers focused on daily operations (AH&LA, 2006). The main advantage of business intelligence systems is that they provide reports in real time, so restaurant managers can make quicker and more informed decisions (AH&LA, 2006). They also help restaurant managers manage resources well, add understand customers’ desires and find hidden opportunities by mining the information in these systems (Ofori-Boateng, 2017).

2.3.2.5 Inventory management applications.

These applications integrate with accounting systems to provide details on the quantity and price of products and monitor inventory replacement (Kasavana, 2011). Additionally, inventory management systems make ordering and receiving inventory easier (Collins et al., 2017). With the help of a handheld device connected to inventory management software, taking inventory and organizing storage is easy and quick (Collins et al., 2017).

2.3.2.6 Company intranet.

These internal company networks require the Internet to communicate with company employees (Vallaster, 2017). An intranet is an important and flexible system for easy “access to operations manuals, discussion forums, company news items, a corporate documents library, e-mail between sites and many other purposes” (AH&LA, 2006, p. 17). Moreover, intranets provide benefits such as “rapid transmission of up-to-date information, improved communication flows, knowledge enhancement, sharing of best practice in context and encouragement of innovation” (Curry and Stancich, 2000, p. 249).

2.3.2.7 Kitchen management systems.

Kitchen management systems are crucial to a restaurant’s service success (Ansel and Dyer, 1999; Mastroberte, 2018). The main components of kitchen management systems are kitchen display systems and kitchen printers. Kitchen display systems are digital media screens which “can be used to help the kitchen better manage orders and to ensure that orders are prepared in a timely fashion” (Kimes, 2008, p. 304). Kitchen display systems provide additional advantages such as keeping track of preparation times for menu items (Mastroberte, 2018), allowing managers to measure and compare kitchen staff performance (Collins et al., 2017) and improve employee efficiency (Noone and Maier, 2015). In addition, kitchen display systems serve as a communication tool between kitchen staff and servers. Kitchen printers serve the same purpose as kitchen display systems but are used if there is a need for paper copies for an internal control system or if printed copies are critical for operations (Kasavana, 2011). With kitchen technologies, restaurants can enjoy a successful service without service interruption (Oronsky and Chathoth, 2007).

2.3.2.8 Integrated cost control systems.

These are integrated applications of the POS which use current product prices to calculate the cost of menu items (O’Connor, 1996). Based on each recipe’s portion size, cooking or preparation method and ingredients, which are identified in the system beforehand, selling price can be calculated with a viable profit percentage (O’Connor, 1996). Cobanoglu (2012) stated the benefits of integrated cost control systems are keeping track of perpetual inventory, accessing cost information, keeping track of costs instantly, and having the ability to make correct and timely decisions about cost.

2.3.2.9 Cloud-based applications.

According to Mell and Grange (2011), cloud computing, also known as virtual computing, is “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” (p. 2). The cloud represents software delivery via the Internet (Kasavana, 2011); software is distributed to clients with the help of the Internet. The restaurant industry is switching to cloud-based applications (Mastroberte, 2018). Some examples include cloud-based POS (Mastroberte, 2014), kitchen inventory management systems (Mastroberte, 2018) integrated video/IP video for security, and back office systems (Roy et al., 2017). Integrated video/IP video is used as part of POS in which the transactions are recorded at a unit level. If an exception to a rule is achieved (e.g. a server voids orders three or more times per shift), this video is sent to management over the cloud system for additional screening to determine if the employee is stealing money (Collins et al., 2017).

2.3.2.10 Enterprise management systems.

Enterprise management system (EMS) provides the technology infrastructure for multi-unit hospitality organizations including restaurant chains (Kasavana, 2011). According to Norton (1999), EMS offers “an integrated solution for planning, executing, and controlling business processes horizontally across the value chain” (p. 38). Most EMS is based on cloud technology; all information is retained and distributed from a central point with the help of the Internet and intranets. EMS accesses data in real-time from different systems that feed the central database (Terry, 2006). Property-level data is fed into the central EMS on a real-time basis or at predetermined intervals for analysis and reporting. For EMS to work, property-based systems must integrate with the central database, allowing data to be consolidated and stored for additional analysis (Mastroberte, 2007).

2.3.2.11 Enterprise reporting systems and real-time based reporting.

ERS distribute data collected by EMS to authorized users after turning it into useable information (Kasavana, 2011). This information helps executives make timely, well-informed business decisions (Collins et al., 2017). EMS are structured to make different analyses on the data collected from property-level systems by sorting data by regions, price points and other variables, and implementing some business and financial rules (e.g. occupancy rates, revenue per available seat). Once these analyses are finalized, ERS report this information on a dashboard designed to present complex data in a simple manner. All information contains drill-down features that allow executives to go deeper into the data. For example, an executive can sort all restaurants based on annual sales amounts companywide, then drill down to a regional basis to a city, and then to a store.

Real-time-based reporting is used in ERS to distribute information to authorized users on a real-time basis (Kasavana, 2011). Real-time based reporting provides immediate feedback to the management team, enabling them to make faster decisions to maximize revenue or minimize costs (Hospitality Technology, 2016).

2.3.2.12 Disaster recovery systems.

Restaurants depend on technology systems for operational and managerial functions (Melián-González and Bulchand-Gidumal, 2017). These systems are called mission-critical systems, which indicate that restaurants may have a hardship if these systems do not work or function properly. For this reason, restaurant companies employ disaster recovery systems that will enable restaurant operators to restore their systems in the shortest time possible (Cobanoglu, 2007; Kasavana, 2011). Disaster recovery systems use tools and techniques such as daily back-up and redundant servers (Pradhan, 2017).

2.3.2.13 Personal digital assistants and intra-day reporting.

PDAs provide many useful functions for management (Agarwal et al., 2017). According to Writing (2018), PDAs can work as electronic secretaries, electronic notebooks, and voice recorders. Moreover, using a PDA in the restaurant industry offers many advantages, such as “increased efficiency, speedier service, better usability and ease of use, enhanced reputation/image and increased accuracy” (Prasad et al., 2005, p. 69).

Additionally, PDAs can access the POS system and some of POS functionalities such as intra-day reporting. This function offers hour-by-hour reports about restaurant sales, guest checks and guest counts. As a result, restaurant managers can have better control over their daily tasks and decisions (Agarwal et al., 2017).

3. Methodology

3.1 Research design and instrument

The survey had four sections. The first section listed Company Characteristics, where respondents were asked to provide information about their IT budgets and business strategies. The second section listed Point of Sale Front of House (POS FOH) operation technology features, where respondents were asked to indicate if they utilize these technologies. If they answered yes, then they were asked the level of the importance of each item for their restaurants. All items concerning the importance of POS FOH operation technology features were rated on a five-point Likert-scale ranging from 1 = Not important at all to 5 = Extremely important. In addition to POS FOH operation technology features in the first section, levels of agreement with perspectives on mobile POS were surveyed (1 = Strongly disagree, 5 = Strongly agree, and 6 = Not sure*[1]). The third section of the survey included Point of Sale Systems Back of House (POS BOH) features and asked respondents to indicate if they use these technologies. If they answered yes, then the survey asked the level of importance of each item for the restaurants.

All items concerning the importance of POS BOH operation tools features were rated on a five-point Likert-scale ranging from 1 = Not important at all to 5 = Extremely important. Finally, the fourth section asked for levels of agreement on Payment Card Industry Data Security Standard (PCI DSS) compliance in restaurants (1 = Strongly disagree, 5 = Strongly agree, and 6 = Don’t know*). In addition to these four sections, there were questions about respondent and company information. All the questions about restaurant technology were obtained through an extensive review of the literature.

Construct validity is a critical task and one of the most difficult types of validity to establish (Churchill, 1979). Therefore, the instrument in this study was sent to a panel of restaurant technology experts to make sure the questions in the instruments were valid. Based on their suggestions, some minor adjustments were made to the instrument to increase construct validity.

Other than descriptive statistics, exploratory factor analyses (EFA) were used to identify the proposed factors of POS FOH operation technology features, POS BOH tools features and mobile POS perspectives. Moreover, independent t-test was used to test the hypotheses mentioned above.

3.2 Sample

The sample consisted of 500 randomly selected restaurant technology managers who subscribe to Hospitality Technology Magazine. The sample was provided by Hospitality Technology Magazine. After eliminating some suspect and incomplete surveys, 136 surveys, which yielded a 27 per cent response rate, were processed. All respondents were responsible for IT in their companies. These sample groups represented 67,299 restaurant units. Of this number, 57,208 were quick-service restaurants, 8,457 were casual/family restaurants, 109 were fine-dining restaurants and 1,525 were other types of restaurants. Each sample member had an email address; therefore, only an online version of the survey was conducted.

3.3 Non-response bias

Whenever there is less than 100 per cent participation in a survey, there is a question of non-response bias, that is, the likelihood of data being changed if all members of the population had responded to the survey. We conducted a non-response analysis using wave analysis (early vs later respondents) to answer whether non-respondents and respondents differed significantly and whether equivalent data from those who did not respond would have significantly altered findings. Rylander et al. (1995) suggested that late respondents and non-respondents were alike, and wave analysis and respondent/non-respondent comparisons yielded the same results. Therefore, we conducted an independent samples t-test on the means of POS BOH and POS FOH technology features’ importance to see if early respondents’ answers were different from those of late respondents. Analysis indicated that there was no significant difference in any of the POS BOH and POS FOH technology features’ importance, which meant that this survey did not suffer non-response bias and therefore, was representative of the population (Hospitality Technology Magazine subscribers who are in charge of IT in restaurants).

4. Findings

4.1 Company characteristics

The highest proportion (39 per cent) of the respondents holds information systems/technology management positions in their companies. More than 60 per cent of the respondents work for chain restaurants, a category further subdivided into national restaurant chain (30.9 per cent), regional restaurant chain (20.6 per cent) and global restaurant chain (16.2 per cent). Almost 30 per cent of the respondents work for independent restaurants, a category further subdivided into independent restaurant management company without franchised products (14.7 per cent), independent restaurant management company with franchised products (7.4 per cent) and other independent types of restaurants (7.4 per cent). Moreover, 14.0 per cent of the respondents work for franchisors.

As indicated in Table I, in this study, respondents represented 67,299 individual restaurants, including quick-service restaurants (57,208), casual/family restaurants (8,457), fine dining restaurants (209) and other types of restaurants (1,525).

Of the respondents, 53.7 per cent reported that they preferred to be An innovator in business leadership; only 31.5 per cent of them preferred to be An innovator in technology leadership, while 25.9 per cent of the companies preferred to be A close follower in business leadership, and 38.9 per cent of the companies preferred to be to be A close follower in technology leadership. Moreover, 11.1 per cent of the companies preferred to be A reactor to industry conditions and competitor moves in business leadership compared to 7.4 per cent in technology leadership.

More than 55 per cent of respondents reported that their IT budget consisted of one per cent (31.5 per cent) or less than one per cent (25.9 per cent) of total sales. More than 75.5 per cent of the represented restaurant companies had an IT budget of more than $1bn. Additionally, 17 per cent of the represented companies had an IT budget of $500m to $1bn.

4.2 Research questions

In this section, research questions proposed for this study are shown and further explained.

4.2.1 What are the main drivers for information technology investments in restaurants?

To be able to answer the first research question, respondents were asked to indicate the approximate distribution of IT budget or spending at your company and indicate the approximate distribution of technology area budget allocation (not including personnel), in percentages, at your company.

On average, companies allocated 20.6 per cent for hardware, 19.2 per cent for software, 17.5 per cent for internal personnel, 13.7 per cent for external service providers, 9.4 per cent for networks and telecom, 4.1 per cent for facilities and 3.2 per cent for other spending of their IT budget.

For the technology area IT budget distribution, POS solutions (30.8 per cent) and back-office solutions (22.0 per cent) received the bulk of funds, which together accounted for more than 52 per cent of the overall technology area IT budgets. Other than POS solutions and back-office solutions, companies spent on average 11.5 per cent for networking, 9.3 per cent for security solutions, 9.0 per cent for mobile/Web technologies and 6.3 per cent for kitchen technology of their technology area IT budgets.

4.2.2 What are the main drivers for restaurants’ information technology efforts?

Business efficiency (30.9 per cent), enhanced guest service (27.2 per cent), employee productivity (25.7 per cent), security/compliance (25.0 per cent) and cost savings measures (22.8 per cent) are the top five main drivers for companies’ IT efforts, followed by revenue-generating opportunities (14.7), increasing guest loyalty (14.7 per cent), preserve existing technology investment (12.5 per cent), competitive pressure (6.6 per cent) social responsibility (4.4 per cent) and other primary drivers including business intelligence and future focus (1.5 per cent).

4.2.3 What is the top challenge facing restaurants’ technology departments?

Insufficient IT budget to keep pace with needed investment was pointed out by 33.3 per cent of the respondents as the top challenge of the technology departments. The other main challenges for the restaurant technology departments are The technology itself is insufficient to meet our needs (19.6 per cent), Guests expect greater technology than we can keep pace with (15.7 per cent), and We lack IT talent in our internal team (13.7 per cent).

4.2.4 What point of sale front of house technology features are used in restaurants? And

4.2.5 What is the importance of point of sale front of house technology features to restaurants’ operations information technology managers?

Addressing the questions above, respondents were asked, For each of the POS FOH features/devices, indicate if the organization is currently using the technology/solution, or has plans to add the technology in the coming 12 months. In addition, they were asked to rate the importance of each technology to (their) organization and the overall restaurant industry.

As indicated in Table II, POS hardware (95.9 per cent), touchscreen (93.9 per cent), POS software (93.8 per cent), gift card integration (87.7 per cent), and integrated credit card swipe into POS (81.6 per cent) are heavily used in the industry. Bill pay via customers’ mobile phone (37.5 per cent), social media activity integrated into POS and/or CRM platform (29.8 per cent), POS integration into online ordering (28.6 per cent), barcode scanners (24.5 per cent), digital signage (20.4 per cent), take-out/delivery system (20.0 per cent) are some of the POS FOH features/devices companies plan to add in the future. In the restaurant industry, the five most important POS FOH features/devices are POS hardware, touchscreen, POS software, gift card integration, and integrated credit card swipe into POS (Table II).

4.2.6 What point of sale back of house technology features are used in restaurants? And

4.2.7 What is the importance of point of sale back of house technology features to restaurants’ operations information technology managers?

To answer questions above, respondents were asked: for each of the POS BOH features/devices, indicate if (their) organization is currently using the technology/solution, or if it plans to add the technology in the coming 12 months. In addition, they were asked to rate the importance of each technology to (their) organization and the overall restaurant industry.

As seen in Table III, accounting/financial software (93.3 per cent), enterprise reporting (86.7 per cent) and inventory management software (84.1 per cent) are the most-used POS BOH technology software/hardware in the restaurant industry. Other preferred BOH applications are kitchen printers (75.6 per cent), company intranet (71.1 per cent), labor management (68.9 per cent), intra-day reporting (66.7 per cent), disaster recovery plan for technology systems (57.8 per cent) and integrated video/IP video for security (55.6 per cent). Real-time Web-based reporting (35.6 per cent), business intelligence system (26.7 per cent) and mobile device for managers’ use (20.0 per cent) are some of the POS BOH features/devices companies plan to add in the future. The five most important POS BOH features/devices are accounting/financial software, enterprise reporting, inventory management software, kitchen printers and company intranet (Table III).

4.2.8 What are restaurant information technology managers’ perceptions of mobile point of sale systems?

Survey respondents mainly agreed on the following statements about mobile POS: Mobile POS helps serve guests more quickly, Mobile POS terminals increase guest satisfaction, Mobile credit card terminals reduce credit card skimming, Mobile POS devices are easy to break and Mobile POS devices are too expensive (Table IV).

4.2.9 What are restaurant information technology managers’ perceptions of payment card industry data security standard practices in restaurants?

The survey respondents mainly agreed on the following regarding PCI compliance and challenges: Card brands should take greater responsibility for ensuring payment technology is secure, Merchants have an unreasonable burden associated with protecting cardholders, Our organization plans to upgrade devices and procedures by the EMV deadline for merchant compliance, PCI Standards are too complex and Our franchisees lack commitment to compliance efforts (Table V).

4.2.10 To what extent are US restaurants compliant with payment card industry data security standard requirements?

PCI data security standards are used by the restaurant companies in the range of 81.4 per cent to 95.3 per cent, except for the standard, Use original passwords (non-vendor-supplied defaults) for system passwords and other security parameters, to which 76.7 per cent of respondents answered yes, and 20.9 per cent answered no. Additionally, practices such as Use of point-to-point encryption (P2PE), Outsource PCI compliance efforts, Our organization has invested in PCI compliance insurance and Use of tokenization at the card swipe were not preferred by most respondents (Table VI).

4.2.11 What is the level of payment card industry data security standard compliance in restaurants?

On average, the level of payment card industry compliance is 87 per cent in the represented restaurant companies.

4.3 Exploratory factor analysis (EFA)

In this study, EFA was used to identify the proposed factors of POS FOH operation technology, POS BOH tools, and mobile POS perspectives.

From the literature reviewed and using the advice of restaurant technology experts, 20 significant software/hardware items were captured for POS FOH operations, and 19 significant software/hardware items were captured for POS BOH operations. Respondents were asked to indicate whether they utilized POS FOH and POS BOH technology. Besides the yes and no option to this question, the option have plans to add was given to respondents. If they answered affirmatively, then they were asked the level of importance for each item. All items concerning the importance of POS FOH and POS BOH operation technology features were rated on a five-point Likert-scale ranging from 1 = Not important at all to 5 = Extremely important.

For the mobile POS perspectives, 13 items were captured, and respondents were asked the level of each item’s importance on a five-point Likert scale ranging from 1 = Strongly disagree to 5 = Strongly agree.

Before starting the EFA, based on Walker and Maddan’s (2008) suggestion, two measures of univariate analysis (skewness and kurtosis) of each item in each section were checked as a first step. Although the majority of items in each section had a skewness and kurtosis value between +1.5 and -1.5, some items had a skewness and kurtosis value greater than 3. According to Kline (2005), values exceeding 3 were considered to be a problem for a normal univariate distribution. Therefore, items with a skewness and/or kurtosis value greater than 3 were removed from the model.

As a second step, the reliability scores of each section were calculated and are demonstrated in Table VII. According to DeVellis (2012) and Nunnally (1978), Cronbach’s alpha scores of the POS FOH and mobile POS perspective sections are within parameters considered to be acceptable for this study. Moreover, a Cronbach’s alpha score of POS BOS is considered very good (DeVellis, 2012).

After removing items that did not distribute normally or did not have acceptable reliability scores, EFA with principal components analysis and Varimax rotation was conducted on each section’s remaining items. After EFA was utilized, according to Yong and Pearce’s (2013) suggestion table of correlation matrix, Bartlett’s test of sphericity, the Kaiser–Meyer−Olkin (KMO) measure of sampling adequacy, anti-image correlation matrix, eigenvalues, commonalities and percentage of the non-redundant residuals, total variance explained and rotated factor matrix were checked for each section.

To interpret EFA, there should be some correlations of 0.30 or greater (Pallant, 2013). Yong and Pearce (2013) stressed that correlations among variables greater than ±0.90 should be removed from the model. Therefore, for each item and section, it was ensured that the majority of the item correlations were 0.30 or greater and there was not a correlation among the variables greater than ± 0.90.

After the correlation matrix test, it is necessary to check the significance level of Bartlett’s test of sphericity and the KMO measure of sampling adequacy, to be able to determine the factorability of the data (Pallant, 2013). Bartlett’s test of sphericity should also demonstrate a significance level of p < 0.05 to continue the EFA (Yong and Pearce, 2013). Although Yong and Pearce (2013) state that the cut-off for the KMO measure of sampling adequacy should be greater than 0.50, Pallant (2013) states that the value for KMO should be 0.60 or above. As a result, because all four sections’ values of the KMO measure of sampling adequacy was higher than the recommend value of 0.60, and Bartlett’s test of sphericity was significant at a level of p < 0.05, an anti-image correlation matrix was checked to support the KMO measure of sampling adequacy and Bartlett’s test of sphericity.

Anti-image correlation “matrices contain measures of sampling adequacy for each variable along the diagonal and the negatives of the partial correlation/covariance on the off-diagonals” (Field, 2013, p. 287). According to Yong and Pearce (2013), the value on the diagonal element of the anti-image correlation matrix should be above 0.50, and if the value is below the cut-off point (0.50), more data should be collected, or the item that has a value below the cut-off should be removed from the model. As a result, it was realized that some items in each section had an anti-image correlation value of less than 0.50. Therefore, items with a value of less than 0.50 anti-image correlations were removed from the model. As there were few items with a value of less than 0.50 anti-image correlations in some of the sections, as a first step, the item with the lowest value was removed from the model, and the model tested again. After trying variations to select the item to be removed, it was decided that items less than 0.50 should be removed one by one, from lowest to highest value.

A rotated factor matrix is used to name factors and interpret them (Yong and Pearce, 2013). On the rotation matrix, the minimum loading of an item should be at least 0.32 to be considered statistically meaningful (Tabachnick and Fidell, 2013).

EFA resulted in four factors with eigenvalues greater than 1.0 that cumulatively explain 71.28 per cent of the entire variance for POS FOH. All the commonalities were within acceptable limits. Except for the barcode scanners (0.51), commonalities of all other items were between 0.63 and 0.90. As can be seen in Table VIII, factor loading of items ranged 0.47 to 0.93. Based on the characteristics of the rotated component matrix for POS FOH operation features, the following labels were assigned to factors, respectively: table management features, online/ordering features, mobile payment features and emerging applications. As a first factor, Table management features captured 31.07 per cent of the variance consisting of the four items. Online/ordering features captured 21.74 per cent of the variance consisting of the four items. Mobile payment features, the third factor, captured 10.29 per cent of the variance of three items. As the fourth factor, emerging applications captured 8.18 per cent of variance consisting of two items.

EFA for POS BOH resulted in four factors with eigenvalues greater than 1.0 that cumulatively explain 71.27 per cent of the entire variance. All the commonalities were within acceptable limits. Other than the Disaster recovery plan for technology systems (0.50), communalities ranged from 0.65 to 0.87. As shown in Table IX, factor loading of items ranged 0.59 to 0.87, suggesting a high correlation of the items. The following labels, essential functions, cloud applications, enterprise systems, and integrated systems, were assigned to factors respectively, based on the characteristics of the rotated component matrix for POS BOH operation features. Essential functions, the first factor, captured 39.66 per cent of the variance consisting of six items. Cloud applications, the second factor, captured 15.75 per cent of the variance consisting of four items. Enterprise systems, the third factor, captured 8.28 per cent of the variance consisting of three items. As the last factor, integrated systems captured 7.58 per cent of the variance consisting of two items.

EFA for mobile POS revealed the presence of three components with eigenvalues higher than 1, explaining 45.06, 17.81 and 12.94 per cent of the variance, respectively, and explaining the total 75.81 per cent of the entire variance. All the commonalities were acceptable with a range from 0.67 to 0.83. As is clearly shown Table X, factor loading of items ranged from 0.78 to 0.87, suggesting a high correlation of the items. Based on the characteristics of the rotated component matrix for mobile POS perspectives, following labels were assigned to factors respectively: advantages of mobile POS, disadvantages of mobile POS and Web-enabled mobile POS features. As the first factor, advantages of mobile POS captured 45.06 per cent of the variance consisting of four items. Disadvantages of mobile POS, the second factor, captured 17.81 per cent of the variance consisting of three items. As the last factor, Web-enabled mobile POS Features captured 12.94 per cent of the variance consisting of two items.

4.4 Hypotheses testing

Based on their job function, respondents were divided into two groups: IT and Non-IT position holder, respectively. Additionally, based on company descriptions, companies were also divided into two groups: chain and independent restaurants. For purposes of this study, a restaurant with more than five units was considered a chain restaurant. Finally, according to companies’ reported preferences from business and technology perspectives, companies were grouped under two categories (business leadership and technology leadership): Innovator or Follower. This categorization was based on self-reported responses.

Based on the literature about FOH, BOH and mobile POS perspectives, the following hypotheses are developed:

H1a.

There is a significant difference in POS FOH technology features between chain restaurants and independent restaurants.

H1b.

There is a significant difference in POS FOH technology features between restaurants that identify themselves as innovators and those that identify themselves as followers, from a business perspective.

H1c.

There is a significant difference in POS FOH technology features between restaurants that identify themselves as innovators and those that identify themselves as followers, from a technology perspective.

H1d.

There is a significant difference in POS FOH technology features between restaurants with IT-educated IT managers and those with non-IT-educated IT managers.

H2a.

There is a significant difference in POS BOH technology features between chain restaurants and independent restaurants.

H2b.

There is a significant difference in POS BOH technology features between restaurants that identify themselves as innovators and those that identify themselves as followers, from a business perspective.

H2c.

There is a significant difference in POS BOH technology features between restaurants that identify themselves as innovators and those that identify themselves as followers, from a technology perspective.

H2d.

There is a significant difference in POS BOH technology features between restaurants with IT-educated IT managers and those with non-IT-educated IT managers.

H3a.

There is a significant difference in mobile POS perspectives between chain restaurants and independent restaurants.

H3b.

There is a significant difference in mobile POS perspectives between restaurants that identify themselves as innovators and those that identify themselves as followers, from a business perspective.

H3c.

There is a significant difference in mobile POS perspectives between restaurants that identify themselves as innovators and those that identify themselves as followers, from a technology perspective.

H3d.

There is a significant difference in mobile POS perspectives between restaurants with IT-educated IT managers versus restaurants with non-IT-educated IT managers.

Independent sample t-tests were conducted to compare factors of POS FOH and POS FOH technology and factors of mobile POS perspectives for restaurant types (chain or independent), business leadership perspective (innovator or follower), technology leadership perspective (innovator or follower), and job function (IT or non-IT), and to test the hypotheses mentioned above.

As seen in Table XI, hypotheses H1a, H1d, H2d, H3a, and H3b were not statistically significant; therefore, they were rejected. Since there were statistically significant differences between some factors for POS FOH technology features, POS BOH technology features, and mobile POS perspectives, chain restaurants and independent restaurants, innovators and followers from a business perspective, and IT-educated IT managers and Non-IT-educated IT managers, H1b, H1c, H2a, H2b, H2c, H3c, and H3d were partially supported, at α ≤ 0.05.

5. Conclusions and recommendations

Based on the findings of this study, the following conclusions may be made. Among all respondents responsible for IT management in their companies, only about 40 per cent have IT as their sole job function. Most respondents have IT as a secondary job function. This supports previous studies’ findings that IT is still not a major job function in restaurant companies (Cobanoglu, 2007). With increased dependence on IT and other compliance requirements such as PCI DSS, restaurateurs may view their IT management policies and create a formal IT structure that supports the entire operation. This finding is in line with another finding in the study: business leadership versus technology leadership. A majority of the respondents identified their companies as innovators from a business perspective, while only one-third of the companies identified themselves as leaders from a technology perspective.

On average, 37 per cent of companies’ IT budgets were allocated to capital expenditures, which include investing in new technology, innovation, and so on. On the other hand, on average, 61.8 per cent of the IT budget was allocated to operating expenditures, which include maintaining systems, licensing, and fees. This finding is somewhat in line with results found by the Info Tech Research Group (Johnston, 2015 para. 3) that “a company’s total percentage of all expenditures should be 33 per cent for capital assets and 67 per cent for operating expenses”. Additionally, it can be observed that IT budgets in restaurants are increasing. Findings of the study indicated that POS (FOH and BOH features) continue to dominate IT spending in respondents’ restaurants. Spending for mobile technologies continues to increase, even though these technologies constitute only nine per cent of total spending.

The top-five main drivers for companies’ IT efforts are business efficiency, enhanced guest service, employee productivity, security/compliance (PCI and payments) and cost-saving measures. These drivers are in line with results from Kasavana (2011) and Ansel and Dyer (1999).

The three main challenges for restaurant technology companies are as follows:

  1. insufficient IT budgets to keep pace with needed investments;

  2. the technology itself is insufficient to meet our needs; and

  3. guests expect greater technology than we can keep pace with.

This finding is also supported by Fellah (2015) and may be explained by the fact that restaurateurs do not see IT as a major job function, but rather as a cost-center. The problem of insufficient investments in IT may disappear as technology becomes an integral part of business operations and its positive impact on the bottom line becomes clearer. Additionally, according to the National Restaurant Association (2015a, para 2). “Technology rapidly is becoming an expectation rather than a novelty when dining out”. As a result, 25 per cent of consumers state that technology is an important decision when choosing a restaurant. Therefore, restaurants may allocate more budget to meet customer expectations in this area and consider IT as a major job function.

The top-five used POS FOH technology features are POS hardware, touchscreen, POS software, gift card integration, and integrated credit card swipe into POS. The five least-used technology features are social media activity integrated into POS and/or CRM platform, tableside payment device, tableside ordering device (tablet, other hardware), near-field communications capability, and bill pay via customers’ mobile phone. This finding makes sense since the least-utilized technologies are still emerging. As these technologies develop further, one can predict that they will be used by more restaurants. This is supported by another finding in this study. Restaurant managers have plans to implement bill pay via customers’ mobile phone, and social media activity integrated into POS and/or CRM platform features into their FOH operation technology systems in the future. Additionally, POS integration into online ordering, barcode scanners, digital signage and take-out/delivery system are other technology features managers plan to add to FOH operations technology systems in their restaurants.

The top-five used POS BOH technology features are accounting/financial software, enterprise reporting, inventory management software, kitchen printer, and company intranet. The five least-used technology features are labor screening and recruitment tools, real-time, Web-based reporting, kitchen management, CRM system and mobile device for manager use. This underutilized hardware and software could bring potential benefits of cost savings and enhanced productivity. Some of the restaurant managers have realized their importance and have plan to implement real-time, Web-based reporting and mobile device for manager use technology features in their restaurants in the future. Additionally, business intelligence system is another POS BOH features restaurant managers wish to have in the future.

There is a positive point of view about mobile POS devices among the restaurant managers. Respondents agreed with the statements: mobile POS helps serve guests more quickly, Mobile POS terminals increase guest satisfaction and mobile credit card terminals reduce credit card skimming.

Restaurateurs may do well by developing mobile apps for their restaurants, or, at the very least, optimizing their websites for mobile phones. Alternatively, restaurants may use existing apps, such as Yelp, to make sure the restaurant is reaching target customers and use feedback or reviews. Finally, reviews on these apps need to be monitored on a continuing basis by management. In the past, mobile features have been separately added to FOH and BOH technologies. However, in the current age of technology, most restaurant technology applications have integrated mobile feature as part of the main hardware/software.

The findings indicated that restaurants comply with 87 per cent of PCI DSS on average. Even though this appears a high percentage, normally PCI DSS compliance should be 100 per cent among restaurants that accept, process, and transmit credit card information. PCI DSS requirements do not accommodate partial compliance. A restaurant may be deemed not compliant even if one of the PCI DSS standards is not met. Restaurants may do well to allocate resources to PCI DSS compliance; a data breach can cost restaurants significant resources as fines. The average cost of a data breach in 2017 was $3.6 million (IBM Security, 2017). To provide a secure transaction, PCI DSS is evolving and publishing new requirements every year. For example, PCI DSS 3.2 requirements must be used by 1 February 2018 (PCI Security Standards Council, 2016). Restaurateurs would do well to review PCI DSS requirements on a regular basis and update their technology to be in compliance. About 21 per cent of respondents indicated that they use vendor-supplied default passwords. This represents a significant potential risk for restaurants, as these passwords are highly known publicly, and hackers use them widely in data breaches.

There is no statistically significant difference between the chain and independent restaurants’ mean for factors of POS FOH technology features. In addition, there is no statistically significant difference between the IT-educated IT managers and non-IT-educated IT managers’ opinions regarding POS FOH technology features. These findings are expected since these features are mission-critical at any restaurant. Mission-critical systems are those that are integral to the operation of a restaurant (Collins et al., 2017). The means of mobile payment features and emerging applications for POS FOH technology features differ significantly between innovators and followers. This finding is expected, as innovator restaurant companies are expected to invest in emerging and new technologies earlier than are follower restaurant companies.

The means of essential functions and cloud applications differs significantly for POS BOH technology features for chain and independent restaurants. Essential functions factors consisted of the following variables: enterprise management software, labor management, accounting/financial software, business intelligence system, inventory management software and real-time and Web-based reporting. This finding may be attributed to the necessity of the essential functions in chain restaurants. Because chain restaurants are composed of multiple units, they may need these functions more than independent restaurants, which usually have one or few units.

In terms of mobile POS perspectives, innovator restaurants, from the technology leadership perspective, agree significantly more in favor of mobile POS (i.e. mobile POS helps serve guests more quickly, mobile POS terminals increase guest satisfaction, mobile credit card terminals reduce credit card skimming and mobile POS terminals wow guests) and Web-enabled mobile POS features (i.e. my company has a mobile app and my company has a website that is optimized for mobile devices). Innovator companies are expected to experiment with new technologies before follower companies do. This is another expected finding. Additionally, IT-educated managers realize the disadvantages of mobile POS features significantly more than do non-IT-educated managers.

6. Limitations and future research

The limitations of this study are the sampling procedure and sample size. The sample was provided by Hospitality Technology Magazine. The sample consisted of IT managers of restaurants that subscribe to Hospitality Technology Magazine; therefore, the results may not be able to be generalized beyond this population. Additionally, it was assumed that the respondents had the responsibility for their restaurant companies, including multi-units, and their answers represented the technology outlook of the entire enterprise.

Future research may include replication of this study every other year to keep track of the trends in restaurant technology. In addition, the impact of technology on restaurant customers’ satisfaction is a future research topic. Finally, further research is needed in emerging technology from both operators’ and customers’ perspectives.

Type of the restaurant

Type of restaurant No. Valid percent
Quick service restaurant (including fast casual) 57208 85.0
Casual/Family restaurant (full service) 8457 12.6
Fine dining restaurant 109 0.2
Other 1525 2.3
Total 67299 100.0

POS FOH Technology software/Hardware utilization and their importance

Current usage Importance
POS FOH technology software/hardware utilized Yes No Plan to add Total (%) Ma SDb
POS hardware 95.9 4.1   100.0 4.4 0.9
Touch screen 93.9 2.0 4.1 100.0 4.4 0.8
POS software 93.8 6.3   100.0 4.6 0.8
Gift card integration 87.8 6.1 6.1 100.0 4.4 0.8
Integrated credit card swipe into POS 81.6 16.3 2.0 100.0 4.3 1.1
Take-out/Delivery system 44.4 35.6 20.0 100.0 3.1 1.5
Energy efficient POS 32.7 59.2 8.2 100.0 2.8 1.0
POS Integration into online ordering 32.7 38.8 28.6 100.0 3.6 1.3
Menu labeling/Nutritional information 32.7 49.0 18.4 100.0 3.2 1.1
Wireless credit card authorization 30.6 53.1 16.3 100.0 3.0 1.4
Digital signage 30.6 49.0 20.4 100.0 3.1 1.3
Table management 26.5 67.3 6.1 100.0 2.3 1.4
Reservations 20.4 73.5 6.1 100.0 2.1 1.5
Biometrics fingerprint reader 16.7 68.8 14.6 100.0 2.4 1.4
Barcode scanners 16.3 59.2 24.5 100.0 2.7 1.2
Social media activity integrated into POS and/or CRM platform 12.8 57.4 29.8 100.0 3.2 1.1
Tableside payment device 10.6 74.5 14.9 100.0 2.3 1.5
Tableside ordering device (tablet, other hardware) 8.5 80.9 10.6 100.0 2.2 1.4
Near field communications (NFC) capability 6.1 81.6 12.2 100.0 2.5 1.3
Bill pay via customers’ mobile phone 2.1 60.4 37.5 100.0 2.7 1.3
Notes:

aMean (1 = not important at all, 5 = extremely important); bstandard deviation

POS BOH technology software/hardware utilization and their importance

Current usage Importance
POS BOH technology software/hardware utilized Yes No Plan to add Total (%) Ma SDb
Accounting/Financial software 93.3 6.7 0.0 100.0 4.4 1.0
Enterprise reporting 86.7 11.1 2.2 100.0 4.3 1.0
Inventory management software 84.1 13.6 2.3 100.0 4.3 1.1
Kitchen printers 75.6 24.4 0.0 100.0 3.9 1.4
Company intranet 71.1 20.0 8.9 100.0 3.7 1.1
Labor management 68.9 20.0 11.1 100.0 4.2 1.0
Intra-day reporting 66.7 28.9 4.4 100.0 3.8 1.2
Disaster recovery plan for technology systems 57.8 26.7 15.6 100.0 4.1 1.1
Integrated video/IP video for security 55.6 31.1 13.3 100.0 3.5 1.1
Cloud-based applications 47.7 36.4 15.9 100.0 3.4 1.3
Kitchen displays 46.7 37.8 15.6 100.0 3.6 1.2
Integrated cost control software 46.7 46.7 6.7 100.0 3.6 1.2
Business intelligence system 44.4 28.9 26.7 100.0 3.9 1.1
Labor screening and recruitment tools 42.2 40.0 17.8 100.0 3.6 1.3
Real-time, Web-based reporting 42.2 22.2 35.6 100.0 3.8 1.2
Kitchen management 40.0 51.1 8.9 100.0 3.5 1.1
CRM system 33.3 48.9 17.8 100.0 3.4 1.1
Mobile device for manager use 28.9 51.1 20.0 100.0 3.2 1.0
Notes:

aMean (1 = not important at all, 5 = extremely important); bstandard deviation

Mobile POS perspectives

Statements Ma SDb
Mobile POS helps serve guests more quickly 4.0 1.0
Mobile POS terminals increase guest satisfaction 3.9 1.1
Mobile credit card terminals reduce credit card skimming 3.9 1.3
Mobile POS devices are easy to break 3.7 1.1
Mobile POS devices are too expensive 3.7 1.1
Mobile POS terminals “wow” guests 3.5 1.1
Mobile POS devices are easy to lose 3.3 1.0
My company has a website that is optimized for mobile devices (i.e. iPhone, Android) 3.2 1.4
My company does not see the value in investing in wireless handheld POS terminals 2.8 1.3
Mobile POS is not a secure method of payment 2.8 1.2
My company has a mobile app 2.6 1.4
Notes:

aMean (1 = strongly disagree, 5 = strongly agree); bstandard deviation;

M > 3.5 is considered as “Agree”

PCI Compliance trends and challenges

Trends and challenges Ma SDb
Card brands should take greater responsibility in ensuring payment technology is secure 4.6 0.9
Merchants have an unreasonable burden associated with protecting cardholders 4.4 1.2
Our organization plans to upgrade devices and procedures by the EMV deadline for merchant compliance 4.2 1.1
PCI Standards are too complex 3.9 1.1
Our franchisees lack commitment to compliance efforts 3.6 1.6
We are fully aware of changes necessary to implement EMV technology 3.5 1.3
We have deployed the PCI council’s best practices for mobile payments 3.5 1.4
We lack vendor support for PCI compliance efforts 3.0 1.4
We lack the budget necessary to implement payment security technologies 2.9 1.3
We lack knowledgeable staff at a senior-level to oversee payment security measures 2.8 1.3
We have limited commitment from top management for payment security 2.4 1.3
Notes:

aMean (1 = strongly disagree, 5 = strongly agree); bstandard deviation;

M > 3.5 is considered as “Agree”

Payment card industry data security standards

Payment card industry data security
standard (PCI DSS) requirements
Yes
(%)
No
(%)
Do not know
(%)
Total
(%)
Install and maintain a firewall configuration to protect cardholder data 95.3 2.3 2.3 100.0
Use and regularly update anti-virus software or programs 95.3 2.3 2.3 100.0
Restrict access to cardholder data by business need-to-know 93.0 2.3 4.7 100.0
Assign a unique ID to each person with computer access 93.0 4.7 2.3 100.0
Protect stored cardholder data 90.7 4.7 4.7 100.0
Encrypt transmission of cardholder data across open, public networks 90.7 2.3 7.0 100.0
Track and monitor all access to network resources and cardholder data 86.0 11.6 2.3 100.0
Regularly test security systems and processes 85.7 9.5 4.8 100.0
Develop and maintain secure systems and applications 83.7 9.3 7.0 100.0
Maintain a policy that addresses information security for all personnel 81.4 11.6 7.0 100.0
Do not use vendor-supplied defaults for system passwords and other
security parameters
76.7 20.9 2.3 100.0
Use of point-to-point encryption (P2PE) 51.2 25.6 23.3 100.0
Outsource PCI compliance efforts 46.5 44.2 9.3 100.0
Our organization has invested in PCI compliance insurance 38.1 45.2 16.7 100.0
Use of tokenization at the card swipe 32.6 46.5 20.9 100.0

Reliability scores

Survey sections with models Cronbach’s alpha
POS FOH 0.76
POS BOH 0.88
Mobile POS perspective 0.71

Rotated component matrix for POS FOH operation features

Items for POS BOH Component
1 2 3 4
Table management 0.93      
Reservations 0.88      
Tableside payment device 0.87      
Tableside ordering device (tablet, other hardware) 0.72      
POS integration into online ordering   0.85    
Take-out/Delivery system   0.80    
Barcode scanners   0.70    
Wireless credit card authorization   0.47    
Biometrics fingerprint reader     0.80  
Bill pay via customers’ mobile phone     0.74  
Near field communications (NFC) capability     0.64  
Social media activity integrated into POS and/or CRM platform       0.73
Energy efficient POS       −0.61
Percentage of total variance 31.07 21.74 10.29 8.18

Rotated component matrix for POS BOH operation features

Items for POS BOH Component
1 2 3 4
Enterprise management software 0.85      
Labor management 0.83      
Accounting/Financial software 0.74      
Business intelligence system 0.74      
Inventory management software 0.64      
Real-time, Web-based reporting 0.63      
Kitchen management   0.87    
Cloud-based applications   0.75    
Kitchen displays   0.73    
Labor screening and recruitment tools   0.65    
Intra-day reporting     0.80  
Company intranet     0.78  
Disaster recovery plan for technology systems     0.59  
Integrated cost control software       0.83
Integrated video/IP video for security       0.81
Percentage of total variance 39.66 15.75 8.28 7.58

Rotated component matrix for mobile POS perspectives

Items for Mobile POS Component
1 2 3
Mobile POS helps serve guests more quickly 0.87    
Mobile POS terminals increase guest satisfaction 0.84    
Mobile credit card terminals reduce credit card skimming 0.81    
Mobile POS terminals “wow” guests 0.78    
Mobile POS devices are easy to break   0.86  
Mobile POS devices are easy to lose   0.84  
Mobile POS devices are too expensive   0.78  
My company has a mobile app     0.84
My company has a website that is optimized for mobile devices (i.e. iPhone, Android)     0.84
Percentage of total variance 45.06 17.81 12.94

Hypotheses testing on extracted factors

Categories POS FOH technology features (H1) POS BOH technology features (H2) Mobile POS perspectives (H3)
Chain restaurants versus Independent restaurants (a) x Essential functions: t = 2.05, p = 0.05
Cloud applications:
t = 2.00, p = 0.05
x
Innovators versus followers from a business perspective (b) Mobile payment features:
t = 2.68, p = 0.01
Emerging applications:
t = 3.11, p = 0.00
Cloud applications:
t = 2.25, p = 0.03
x
Innovators versus followers from a technology perspective (c) Mobile payment features:
t = 2.27, p = 0.03
Emerging applications:
t = 1.95, p = 0.05
Essential functions:
t = 2.30, p = 0.04
Advantages of mobile POS:
t = 2.33, p = 0.03
Web enabled mobile POS features,
t = 1.95, p = 0.05
IT educated IT managers versusNon-IT educated IT managers (d) x x Disadvantages of mobile POS:
t = 1.98, p = 0.05

Note

1.

*This option was given to respondents when they could not evaluate the item with certainty; this number was not used in the mean calculation.

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Acknowledgements

This article is based on a thesis written by the author.

Corresponding author

Muhittin Cavusoglu can be contacted at: muhittincavusoglu@gmail.com