Dissertation Topic: "The Effects of Cybersecurity Measures on the Productivity and Well-being of Teleworkers in the Healthcare Industry". 

Introduction

Draft no more than TWO paragraphs here - remember the reader has seen Chapter 1. Include citations

Research Project Design

Outline your methodology section and reasoning followed by the specific design. Include citations include a Table of recent studies for your topic that used that Design List Research questions (numbered - at least 2)

Sampling Procedures and or/ Data Collection Sources (reference Informed Consent and IRB approval placed in Appendices)

Desired sample size - Reasoning for sample size - include a Table of recent studies with those sample sizes. Outline your Site and Recruiting plan in detail Outline your Data source(s) - i.e. interviews - and include a Table with your protocol

Data Analysis

How will you conduct your data analysis? Will you use software. Outline your data analysis process in detail and include a Table of steps. What are the quality measures for your study? Describe each one with citations. =| What are the ethical concerns and processes - i.e. data storage; protection of human subjects

Summary

ONE-TWO paragraph summary

Use the outline below as a guide to my dissertation topic use in-text and end-of-article summary

Chapter Three

Procedures and Methodology

Introduction

The problem this a comparative research study aimed to address is the potential discrepancy in food service performance and safety when comparing human and robotic service providers, as well as the implications of integrating robotics into the food service industry. This study employed a survey to compare a rollup of several scales from Song & Kim (2022), Bartneck et al. (2009), and Qin & Prybutok (2009). Researchers have called for further exploration of this topic (Wang et al., 2022; Grau et al., 2020). There has been a lack of quantitative research on the opinions on the potential impacts of robotics on the industry (Zemke, 2020). This study aimed to fill this gap by gathering empirical data from experts, including software engineers, data scientists, machine learning experts, those in the field of robotics, and those who have knowledge of IT in food service to find potential impacts of robotics on performance within the food service industry. This chapter described the method and design for this study, which used quantitative data to study the impact of robotics on food service performance. It outlined research questions, study population, sample size, source of data used in the study, data collection methods, and data analysis techniques. Furthermore, it discussed ethical considerations, limitations, and delimitations associated with the study before concluding with a summary.

Research Paradigm

This study adopted a quantitative approach to explore the impact of robotics on food service. Quantitative research was employed to gather and analyze numerical data, measure trends, evaluate opinions, and draw conclusions about a population (Creswell, 2013). The goal was to derive generalizable insights from the data collected, which could inform practices in the food service industry (Farrelly, 2013).

The survey method allowed researchers to gather large amounts of data quickly and enabled them to draw generalizations from their findings. Furthermore, this methodology enabled the accurate comparison of experts' opinions on the potential impacts of robotics on the food service industry. The method was chosen because it allowed for an unbiased collection of information from a large sample size that could be used to make generalizations about the population being studied. Quantitative research involved the collection and storage of numerical data in an electronic database, as well as the analysis of this data using statistical methods (Watson, 2015).

A quantitative study was an appropriate approach for a research project focused on understanding the impacts of robotics on food service. Quantitative studies provided a valuable method of collecting and analyzing data to generate results that could be used to make decisions and assess changes (Leedy & Ormrod, 2015). By using quantitative methods such as surveys, researchers were able to collect factual information regarding attitudes toward robotics in food service, identify any trends or patterns, and measure how different factors may influence participants' responses. Additionally, these methods allowed researchers to compare their findings with those from other studies to gain a more comprehensive picture of the effects of robotics in food service. Quantitative research is often used to identify patterns and relationships between variables by examining data from multiple sources (Taherdoost, 2022). The results of quantitative studies can be used to explain relationships between variables and make predictions about future trends. Moreover, quantitative methods allowed researchers to compare different groups or populations on a variety of characteristics (Taherdoost, 2022).

This study employed a quantitative methodology due to the need for objective measurement and statistical analysis of the data collected. The nature of the research questions, which involved comparing performance and safety between food service robots and human employees, necessitated a quantitative approach. Quantitative methods allowed us to measure these variables in a way that qualitative methods could not, providing numerical data that could be analyzed to draw statistically valid conclusions. The Wilcoxon sign rank test was the primary statistical method used in this study. This non-parametric test was chosen since Likert scale survey responses are ordinal and non-parametric. As Norman (2010) stated, "Sin 1 is using parametric statistics on ordinal data. Sin 2 relates to the assumption of normality and claims that 'Before parametric statistical analysis is appropriate... the study sample must be drawn from a normally distributed population [italics theirs]' and (2) the sample size must be large enough to be representative of the population" (p. 626). It enabled the comparison of two related samples or repeated measurements on a single sample, which was useful in this study, given that we were comparing the performance scores of the same subjects under two different conditions.

Several other statistical tests were used besides the Wilcoxon sign rank test. Cronbach's alpha was used to confirm the survey instrument's reliability, ensuring it consistently measured the intended constructs. A confirmatory factor analysis (CFA) was performed to validate the survey instrument's factor structure, ensuring that each item loaded onto the intended factor. Spearman's rank correlation and Kendall's tau were used to measure correlations between ordinal variables, providing further insight into the relationships between different aspects of performance and safety. The correlations between scales were measured using Spearman's rank correlation and Kendall's tau, which provided additional insight into the relationships between various aspects of performance and safety. An analysis of variance (ANOVA) was also conducted to determine the impact of factors such as the highest level of education and organization size on the performance of service robots. This test allowed for the comparison of means across multiple groups, revealing whether these factors had a significant impact on performance.

Research Design

The research employed a comparative research design to assess the performance and safety of service robots versus human employees in the food service industry. This design was chosen as it allowed for the direct comparison of two groups (service robots and human service employees) on the same variables (performance and safety). This comparative design was deemed more appropriate than other possible design options such as non-experimental correlational, predictive correlation, quasi-experimental, and experimental designs. Non-experimental correlational design, which determines if the variables are related, was deemed inappropriate for this study as it does not determine the influence of one variable upon another (Salkind, 2010). Predictive correlation design, which determines the predictive value of predictor variables on other criterion variables, was also not aligned with the intent of this study (Creswell, 2017). Quasi-experimental design, which requires equivalent groups and examines one group with an applied intervention and a control group, was not considered appropriate due to the lack of a control group in this study (Shadish, Cook, & Campbell, 2002). Lastly, the experimental design, which includes independent variables to be manipulated to determine the causal influence on dependent variables, was also not suitable as this study did not involve any manipulation of variables (Fraenkel, Wallen, & Hyun, 2012). Therefore, the comparative research design was best suited for this study as it allowed for the direct comparison between service robots and human service employees in terms of performance and safety, without requiring any manipulation of variables or the need for a control group.

Sampling Procedures and Data Collection Sources

The population for this study consisted of experts in the development of robotics, including software engineers, data scientists, machine learning experts, those in the field of robotics, and those with knowledge of IT in food service. These experts were typically found in technology-focused organizations, such as universities, research centers, and software engineering companies.

The sample frame for this study comprised Survey Monkey Audience and the Prolific Audience panel. Survey Monkey Audience is an online panel that provides access to a diverse range of professionals and experts in various fields, including robotics, software engineering, data science, machine learning, and those who have interacted with robots, as well as employees and managers in the food service sector. This platform allowed researchers to target specific demographics and industries, ensuring that the sample frame accurately represented the target population. In addition to Survey Monkey Audience, the study also leveraged the Prolific Audience panel. Prolific is a platform connecting researchers with individuals actively participating in research studies. The inclusion of the Prolific Audience panel expanded the sample and engaged individuals with relevant expertise and knowledge. A power analysis was conducted to determine the appropriate sample size for this study. Using G*Power, it was determined that 57 participants were needed, with the results detailed in Appendix E. This analysis considered factors such as the desired statistical significance level, effect size, and power. The power analysis estimated the minimum number of participants required to detect meaningful differences or relationships within the data while minimizing the risk of Type I and Type II errors. Participants were randomly selected from the Survey Monkey and Prolific Audience panels' pool. This expert sampling technique ensured that qualified individuals were included in the study.

The desired sample for this study included experts in the development of robotics and individuals with IT knowledge applicable to robotics. These professionals encompassed software engineers, data scientists, machine learning experts, and those working in the field of robotics. They were typically found in technology-focused organizations like universities, research centers, and software engineering companies. The study utilized both Survey Monkey Audience and the Prolific Audience panel to ensure a diverse and representative sample. Survey Monkey Audience is an online panel that grants access to a wide range of professionals and experts in various fields, including robotics, software engineering, data science, and machine learning development. This platform enabled researchers to target specific demographics and industries, ensuring that the sample frame accurately represented the target population. Additionally, the researcher leveraged the Prolific Audience panel. By utilizing the Prolific Audience panel, the study further expanded the sample and engaged individuals who possessed expertise and knowledge relevant to the study. To access the target audience and survey data, the researcher contracted an account with Survey Monkey. A fee was paid to Survey Monkey and Prolific to obtain the desired audience and data for the survey. This fee covered the cost of accessing the panel and collecting the necessary information from the participants.

The desired sample for this study included a diverse group of individuals representing various aspects of the robotics and food service industries. This group comprised experts in robotics development, software engineers, data scientists, machine learning experts, as well as individuals who had interacted with robots in a food service setting, such as employees, managers, and consumers. Including participants with a wide range of experiences and perspectives, the study was better equipped to capture the full scope of potential impacts that robotics may have on the food service industry. The SurveyMonkey Audience panel and Prolific Audience panel served as powerful tools for accessing a diverse and representative sample. As the sample frame for this study, these panels offered several advantages: access to a large, diverse pool of participants, customizable targeting, quota management, quality control, and efficient data collection. The study considered the following demographic factors to ensure the sample was representative of the target population:

2. Job title: Participants held job titles that aligned with their expertise in robotics, artificial intelligence, or data science. Examples of relevant positions included robotics engineers, AI developers, machine learning specialists, data scientists, and IT professionals working with robotics-related technologies. This helped gather diverse opinions on the development and deployment of robots in the food service industry from individuals directly involved in these fields.
4. Company size: Participants came from companies of varying sizes, from small startups to large multinational corporations. This provided insights into the adoption and impact of robotics in food service within different organizational structures and resource levels.
6. Level of education: Individuals with diverse educational backgrounds in technology, robotics, artificial intelligence, and related fields were included in the sample. This aided in gathering a range of perspectives and views on the potential consequences of robotics in the food service business.
8. Years of experience: Participants had varied years of experience working in areas such as robotics development, artificial intelligence or machine learning development, and data science or IT related to robotics. This range of experience ensured a comprehensive understanding of the potential impact of robotics on the food service sector and provided a variety of perspectives on the challenges and opportunities associated with integrating robotic systems in the industry.

The study employed an expert sampling technique to recruit participants. The target population for this survey included experts in information technology, programming, robotics, and machine learning who possessed experience and knowledge of automation robotics technology, along with its industry implications. The researcher used Survey Monkey Audience and Prolific Panel to identify potential respondents. These platforms allowed for recruiting professionals with expertise in the relevant areas of robotics and machine learning in the food service industry. A screening process was developed to ensure that only qualified individuals participated in this research project. Potential participants had to meet certain criteria before gaining access to the survey. These criteria included demonstrating expertise or knowledge in robotics technology or machine learning and having some understanding of its potential application within the food service industry. Participants were asked to provide evidence of their qualifications or work experience via Survey Monkey before being granted access to the survey. Once identified and screened, eligible participants were provided with access to the survey through Survey Monkey. Before answering the survey questions, all relevant information necessary for participants to make an informed decision was provided. This included details about the purpose of the study, confidentiality measures, and any other relevant information (see invitation to the study in Appendix H). By employing expert sampling and implementing a screening process, the study aimed to ensure that the participants were qualified and knowledgeable in the specific areas of robotics and machine learning within the food service industry.

This quantitative descriptive study encompassed a single comprehensive survey. The survey incorporated questions that assessed the performance and safety of robotics in the food service industry, as well as compared these measures to the performance and safety of human employees working in the same industry. Several of the scales used in the survey had been adapted from previously tested instruments to ensure their validity and reliability. This process contributed to the development of a robust and comprehensive survey that accurately captured the perspectives of food service industry professionals on the performance, safety, and overall implications of robotics in their field. The survey outline of variables is in Tables 1 and 2 below.

Statistical Tests

The research questions were designed to compare the performance and safety of food service robots with human employees across various metrics or subscales: Usefulness, Social Capability, Anticipated Service Quality, Reliability, Responsiveness, Assurance, and Perceived Intelligence. Each subscale is operationalized through a series of Likert scale items. For example, the "Usefulness" subscale includes items such as "The food service robot would be useful" and "Using the food service robot would save me time." Respondents rate their level of agreement with these statements on a 7-point scale, and the mean score for each subscale is calculated. This process is repeated for all seven subscales. The comparison between food service robots and human employees is made by comparing the mean scores for each subscale. A significant difference in the mean scores would suggest a disparity in performance between the two groups.

To assess the performance of food service robots and employees, various subscales, including usefulness, social capability, anticipated service quality, perceived intelligence, reliability, and responsiveness, were evaluated. Safety is a crucial factor in the food service industry, directly impacting the well-being of employees and customers. The study acknowledged that if food service robots cannot maintain a high level of safety, their performance in other areas may be overshadowed by safety concerns.

A Wilcoxon signed-rank test was conducted to compare the safety and performance scores of food service robots and human employees. This non-parametric test is suitable for comparing two related samples or repeated measurements on a single sample, making it an appropriate choice for the ordinal nature of Likert scale data. The test provided a p-value used to assess the significance of the differences observed. If the p-value was less than the predetermined significance level (e.g., 0.05), the null hypothesis (H0) that there is no difference was rejected in favor of the alternative hypothesis (Ha), indicating a significant difference between the groups. Conversely, a p-value greater than the significance level would fail to reject the null hypothesis, suggesting no significant difference in safety levels.

This analysis is crucial for determining the feasibility of adopting food service robots and evaluating whether they can be considered a safe and reliable alternative to human employees. The operationalization of these subscales and the method for comparing them are based on established research methods in the field of service quality and customer satisfaction. The specific items used in this study were adapted from previous research by Song & Kim (2022), Qin & Prybutok (2009), and Bartneck et al. (2009), with permission, to ensure the validity and reliability of the constructs measured.

Table 2 First Set of Scales

Category	Scale Item
Usefulness (Robots)	The food service robot would be useful.
Using the food service robot would save me time.
It would be easy to dine in/get food with the food service robot.
Using the food service robot would improve my meal/dining experience.
Using the food service robot would enhance my effectiveness.
Usefulness (Humans)	The food service employee would be useful.
Using the food service employee would save me time.
It would be easy to dine in/get food with the food service employee.
Using the food service employee would improve my meal/dining experience.
Using the food service employee would enhance my effectiveness during my meal/dining experience.
Social Capability (Robots)	The food service robot appears to listen attentively.
The food service robot appears to say appropriate things.
The food service robot listens without interrupting when the customer is talking.
The food service robot seems to remember the detailed information about the customer's questions.
The food service robot appears to be polite.
Social Capability (Humans)	The food service employee appears to listen attentively.
The food service employee appears to say appropriate things.
The food service employee listens without interrupting when the customer is talking.
The food service employee seems to remember the detailed information about the customer's questions.
The food service employee appears to be polite.
Anticipated service quality (Robots)	Overall, I would be pleased with the services provided by the food service robot
Overall, the service quality of the food service robot is excellent.
Overall, the food service robot would meet my expectations of what makes a good food service provider.
Anticipated service quality (Humans)	Overall, I would be pleased with the services provided by the food service employee.
Overall, the service quality of the food service employee is excellent.
Overall, the food service robot would meet my expectations of what makes a good food service provider. Overall, I would be pleased with the services provided by the food service robot.
Reliability (Robots)	Providing service as promised
Sympathetic and reassuring
Dependable
On-schedule service
Accurate charge
Reliability (Humans)	Providing service as promised
Sympathetic and reassuring
Dependable
On-schedule service
Accurate charge
Responsiveness (Robots)	Telling exact service time
Robot employees available to requests
Prompt service
Robot employees willing to help
Responsiveness (Humans)	Telling exact service time
Employees available to requests
Prompt service
Service employees willing to help
Assurance (Robots)	Trust robots
Feel safe for financial transactions
Friendly robots
Knowledgeable employees
Assurance (Humans)	Trust employees
Feel safe for financial transactions
Friendly employees
Knowledgeable employees

Note.This table presents scale items based on a 7-point Likert-type scale (1 = Strongly Disagree, 7 = Strongly Agree). Respondents are asked to rate their level of agreement with statements related to the service of robots and employees in food service.

Table 3 Second Set of Scales

Category	Rating Scales
Perceived Intelligence	Incompetent (1 2 3 4 5) Competent
	Ignorant (1 2 3 4 5) Knowledgeable
	Irresponsible (1 2 3 4 5) Responsible
	Unintelligent (1 2 3 4 5) Intelligent
	Foolish (1 2 3 4 5) Sensible
Perceived Safety	Anxious (1 2 3 4 5) Relaxed
	Calm (1 2 3 4 5) Agitated
	Quiescent (1 2 3 4 5) Surprised

Note. Scale items adapted from Bartneck et al. (2009). Please rate your impression of the food service robot and your emotional state during interactions with the robot and service employee on these scales. Scale Items (5-point Likert-type scale: 1 - 5 based on left to right)

Table 4

Demographic Variables

Variables	Description
Job Title	Description of job title
Size of company	Size of the company that is worked at
Education	Highest level of education completed by the participant
Experience	Number of years of experience in field

The survey instrument was meticulously crafted by integrating Likert scale items from established surveys in the field, specifically those developed by Song & Kim (2022), Qin & Prybutok (2009), and Bartneck et al. (2009). Permission for the use of these items was secured prior to the survey's deployment, ensuring adherence to ethical standards of research and intellectual property rights. The chosen items were selected for their proven reliability and validity in measuring user perceptions and attitudes toward robotics and technology in service settings.

Song & Kim (2022) reported that Cronbach's alpha values for the scales ranged between 0.78 and 0.89, indicating good reliability. Confirmatory factor analysis (CFA) was conducted to assess the convergent and discriminant validity of the scales. The results showed that all factor loadings were significant and above 0.5, indicating good convergent validity. The average variance extracted (AVE) values were above 0.5, and the square root of AVE for each construct was greater than its correlation with other constructs, indicating good discriminant validity

Qin & Prybutok (2009) reported Cronbach's alpha values for the scales ranging between 0.72 and 0.88, suggesting acceptable to good reliability. Their study also utilized CFA to assess convergent and discriminant validity. The factor loadings were significant and above 0.5, and the AVE values were above 0.5, indicating good convergent validity. The square root of AVE for each construct was greater than its correlation with other constructs, supporting discriminant validity.

Bartneck et al. (2009) reported Cronbach's alpha values for the Perceived Intelligence scale at 0.83 and the Perceived Safety scale at 0.78, indicating good reliability. They also conducted CFA to assess convergent and discriminant validity. The factor loadings were significant and above 0.5, and the AVE values were above 0.5, indicating good convergent validity. The square root of AVE for each construct was greater than its correlation with other constructs, supporting discriminant validity.

In conclusion, the scales from Song & Kim (2022), Qin & Prybutok (2009), and Bartneck et al. (2009) have demonstrated good validity and reliability through Cronbach's alpha and CFA tests, indicating their suitability for measuring perceptions of food service robots and employees. These scales focus on various aspects of service quality, such as usefulness, responsiveness, reliability, assurance, and perceived intelligence, which are essential indicators of the overall performance of food service robots and employees. The scales were adapted with only minor changes to make them suitable for the food service industry context. These minor changes included slight adjustments to the wording of the scale items to make them relevant to food service robots and employees. By keeping the changes minimal, the original validity and reliability of the scales were preserved.

A review of four studies that employed the Godspeed questionnaire series, a widely-used tool for evaluating human-robot interaction, highlighted its reliability, validity, and possible improvements. Weiss and Bartneck (2015) conducted a meta-analysis of the usage of the Godspeed questionnaire series, analyzing 107 studies. Tan et al. (2013) used the Godspeed questionnaire to evaluate the pet robot CuDDler, and Ho and MacDorman (2010) revisited the uncanny valley theory, proposing an alternative evaluation tool, the "Eeriness-Attractiveness Scale." Lee et al. (2019) investigated the impact of speech style and embodiment on user experience in autonomous vehicles, using the Godspeed questionnaire to assess users' perceptions.

The instrument employed in this study was based on scales developed by Qin & Prybutok (2009), adapted from the SERVQUAL model introduced by Parasuraman, Zeithaml, and Berry (1985). The SERVQUAL model measures service quality across dimensions including reliability, assurance, tangibles, empathy, and responsiveness. Qin and Prybutok modified this model to suit the context of service robots and employees. The instrument's development involved an extensive literature review on service quality and customer satisfaction, with a focus on the SERVQUAL model's application across various industries. Dimensions of service quality relevant to the research context were identified, and scale items for each dimension were generated and refined through content validation with field experts.

Empirical testing of the scales utilized survey data from customers who had interactions with both service robots and human employees. The scales' validity and reliability were established through statistical tests such as Cronbach's alpha and confirmatory factor analysis (CFA). The results indicated that the adapted scales possessed good psychometric properties, rendering them suitable for measuring service quality in the context of service robots and employees. The research drew upon studies that have used or adapted the scales from Qin and Prybutok (2009), demonstrating their versatility. For instance, Ladhari (2009) reviewed SERVQUAL research spanning two decades, while Kim et al. (2015) applied the model to the South Korean restaurant industry. Palacios-Marqus et al. (2015) used the model to investigate technological, organizational, and competition factors on web knowledge exchange in SMEs.

The current study aimed to measure the performance of food service robots and employees using a comprehensive instrument adapted from prior research. This instrument was based on scales developed by Song & Kim (2022), who investigated consumer acceptance of humanoid retail service robots. The scales encompassed various service quality and performance aspects such as usefulness, social capability, and anticipated service quality.

Song and Kim (2022) constructed their scales by reviewing literature on human-robot interaction, service quality, and technology acceptance, adopting scales from seminal studies to MAKE

an instrument for assessing performance in the context of humanoid retail service robots. Minor modifications were made to these scales to adapt them to the food service robot context, ensuring the preservation of their validity and reliability.

Data collection and management procedures were meticulously implemented:

2. IRB Approval: Institutional Review Board (IRB) approval was secured from the University before the study commenced.
4. Question Development: The survey questions underwent approval by an expert panel and were field-tested to eliminate bias.
6. Participant Recruitment: Participants were sourced via Survey Monkey and Prolific.
8. Participant Screening: A screening process was conducted to confirm participant eligibility.
10. Formal Consent: Informed consent forms were provided and signed by all participants.
12. Interview Selection: Interviewees were chosen from the pool of participants who met the screening criteria.
14. Anonymity: The anonymity of participant information was maintained, with identifiable information redacted from any study-related reports or publications.

Inferential statistics are based on probability theory, which allows researchers to use knowledge of a sample to make general statements about a population (Asadoorian & Kantarelis, 2005). Inferential statistics are appropriate for making inferences about populations from samples using the data to draw conclusions and gain insight into what the data means and how it connects. They involve using sample data to make inferences and predictions about a larger population. This can help businesses and organizations make better decisions based on insights gleaned from the data (Marshall & Jonker, 2011).

Data was analyzed using Jeffrey's Amazing Statistics Program (JASP, 2023). Descriptive statistics, such as frequencies and means, were used to analyze demographic information. Inferential statistics like ANOVA were also used to determine significance. Correlation analyses were conducted to evaluate any relationship between variables. The survey responses were coded and analyzed using JASP. The survey results were analyzed using descriptive statistics, such as frequencies and means, to determine how participants responded.

Specific tests were conducted for each hypothesis to analyze the relationships between variables and compare the performance of food service robots and service employees. An analysis of variance (ANOVA) was conducted to compare the performance of food service robots and humans. ANOVA is a statistical technique used to determine whether there are any significant differences between the means of multiple groups. In this case, the performance scores of food service robots and humans were compared using ANOVA, derived from the composite scales for usefulness, social capability, anticipated service quality, perceived intelligence, reliability, and responsiveness. This analysis provides insights into the overall performance and acceptance of food service robots compared to human employees. For the safety aspect, an independent samples t-test was conducted to compare the perceived safety of food service robots and humans. The t-test is a statistical method used to determine whether there is a significant difference between the means of two groups. By comparing the mean safety scores for robots and humans, researchers can identify significant disparities in the perceived safety between the two and investigate potential factors contributing to these differences. This analysis is crucial in understanding the concerns and preferences of customers regarding safety when interacting with food service robots and humans, which can guide the development and implementation of food service robots to ensure a safe and comfortable experience for customers.

Multiple variables were assessed to understand the overall perception of food service robots and service employees. Most questions were rolled up to measure the overall performance, except safety. This allowed for a more streamlined comparison between service robots and employees regarding their perceived usefulness, social capability, anticipated service quality, reliability, responsiveness, and perceived intelligence. A composite scale was created, and the individual scores for each question within a specific category (e.g., usefulness, social capability, etc.) were averaged to obtain a single score representing the overall performance in that category. For example, the five questions related to the usefulness of robots are averaged to generate a single usefulness score for service robots. The exact process is applied to the questions pertaining to service employees and other categories. By rolling up the questions into a single scale for each class, the analysis is more straightforward to interpret. On the other hand, safety is rolled up onto a single scale, as it is a distinct aspect that warrants separate analysis. This is because safety is crucial in adopting and accepting new technologies, particularly in the food service industry. By analyzing safety separately, researchers could better understand the participants' emotional states when interacting with food service robots and service employees and identify any potential concerns or areas for improvement in the design and implementation of food service robots. This information could be valuable for manufacturers and food service providers, as it can guide them in ensuring that their robotic systems are safe, user-friendly, and well-received by customers.

Understanding ethical considerations is very important when involving human subjects in a study. There are not only morally correct actions that researchers are held up to but also laws and regulations that can affect how studies are conducted when the use of personal information is considered. Understanding ethical considerations is very important when involving human subjects in a study. There are not only morally correct actions that researchers are held up to but also laws and regulations that can affect how studies are conducted when using personal information is considered. When conducting research involving human subjects, researchers must adhere to the ethical principles of respect for persons, beneficence, and justice. Respect dictates that individuals should be treated with respect, and their autonomy should be respected by allowing them to make their own decisions (U.S. Department of Health and Human Services, 2018). Beneficence requires the researcher to maximize the research's benefits while minimizing risks (U.S. Department of Health and Human Services, 2018). Finally, justice requires that the researcher allocate study resources fairly (U.S. Department of Health and Human Services, 2018). In addition to ethical principles, researchers must also comply with laws and regulations such as the Common Rule (45 CFR 46), designed to protect human subjects' rights and safety throughout a research study (U.S. Department of Health and Human Services, 2018). The Common Rule provides guidance on informed consent, confidentiality, data protection, and compensation for injury or harm (U.S. Department of Health and Human Services, 2018). Researchers need to adhere to ethical principles and laws when conducting research involving human subjects to protect the integrity of the research study and the rights of the individuals involved in the study.

Summary

This chapter describes the method and design for this study, which uses quantitative data to study the impact of robotics in food service. It outlined research questions, study population, sample size, source of data used in the study, data collection methods, and data analysis techniques. Furthermore, it discussed ethical considerations, limitations, and delimitations associated with the study. This quantitative descriptive study employs a survey to examine the impact of robotics on food service. This design was chosen because it allows data collection from a large sample with minimal resources and time constraints. The population for this study consists of experts in the development of robotics software addressing uncertainty and error checking who can comment on the potential impacts of robotics on the food service industry. These experts are typically found in technology-focused organizations such as universities, research centers, and software engineering companies. The desired sample size for this study is 103 participants recruited through the online source Prolific. The survey included closed-ended questions.

The survey results are analyzed using descriptive statistics such as frequencies and means to determine how participants responded. Inferential statistics such as chi-square tests, t-tests, and logistical regression was also used to determine any significance. Correlation analysis was conducted to evaluate any relationship between variables. The study is limited by the amount of data collected and its sole use of quantitative research. Additionally, the research focuses on the food service industry within the United States, which may limit the generalizability of findings. Despite these limitations, this study is expected to provide valuable insights into opinions on the potential impacts of robotics on the food service industry and inform decision-making, policy development, and future research in this field.