Republic of the Philippines MINDANAO STATE UNIVERSITY Fatima, General Santos City SENIOR HIGH SCHOOL The Effect of Digital Fraud on Online Shopping Attitude of MSU-Gensan SHS Students During COVID-19 Pandemic A Research Presented to the Faculty of Mindanao State University Senior High School In Partial Fulfilment of the Requirements in Practical Research 1 Presented by: Jamie Diane Langub Janus Bryle Padilla Alexa Jhel Arabis Shane Tabucon Christian Gil Dondiego January 2021 Contents CHAPTER 1 | Introduction ............................................................................................ 1 Background of the study.................................................................................................. 1 Theoretical Background .................................................................................................. 3 Research Questions ........................................................................................................ 5 Hypotheses ..................................................................................................................... 5 Significance of the Study ................................................................................................. 5 Scope and Delimitation ................................................................................................... 6 Definition of Terms .......................................................................................................... 7 CHAPTER 2 | Review of Related Literature.............................................................. 8 Online Shopping Attitude ............................................................................................. 9 Reasons behind the choice of theories ...................................................................... 10 Theories of planned behavior (TPB) .......................................................................... 10 Perceived risk ............................................................................................................ 12 Positive attitude towards online buying ...................................................................... 16 Factors Influencing Online Buying Behavior of college students: A qualitative analysis ................................................................................................................................... 16 Depth Interview Procedure......................................................................................... 17 Method of Data Analysis ............................................................................................ 17 CHAPTER 3 | Methodology .................................................................................. 19 Research Design .................................................................................................... 19 Research Respondents .......................................................................................... 19 Sampling Design..................................................................................................... 19 Data Gathering Procedure ...................................................................................... 20 CHAPTER 1 Introduction Background of the study E-commerce refers to all aspects of running a business online, while online shopping refers to the online selling and purchasing of goods and services (Cunningham, 2019). Cunningham (2019) defines online shopping as an e-commerce activity in which you buy products from a seller's website using a credit or debit card and have them delivered to your home. Online purchasing also include completing online research and conducting web searches for things. In research from Ken Research (2021) the rise of the e-commerce business in the Philippines has been fueled by COVID-19. Lockdowns and quarantines have hampered people's movement, with traffic in the Philippines dropping by as much as 80%. Consumer activity has expanded as a result of the lockdowns, with the top players in the industry seeing an increase of more than 2 to 3 million visits per month on apps and websites. As the e-commerce business rise amid pandemic, the digital fraud attempts against businesses and consumers in the Philippines also increased (Philippine News Agency & Crismundo, 2021). TransUnion (2021) latest quarterly analysis of global online fraud trends found that the rate of digital fraud attempts against businesses increased by 31% when comparing pre-pandemic to post-pandemic levels. Gen Z, or those born between 1995 and 2002, is currently the most targeted out of any generation with 48% of all online fraud attempts, followed by Millennials, or those born between 1980 and 1994, with 42%. Several studies have been conducted in order to investigate the factors that influence consumers' attitudes and perceptions towards making e-commerce purchases through online shopping. Attitudes toward online shopping are defined as a consumer‟s positive or negative feelings related to accomplishing the purchasing behavior on the internet (Chiu, Lin, & Tang, 2005; Schlosser, 2003). Consumer attitudes research has 1|Page been linked to consumer purchasing behavior research since the mid-1970s. Consumer attitudes are influenced by intention, according to the concept of attitude change and action (Fishbein & Ajzen, 1975). With this, the main goal of the study is to determine which attitudes do students develop after encountering fraudulent activities. This study will investigate the outcome of the purchase transaction when this intention is applied to online buying behavior. Attitude is a multi-faceted concept. One of these dimensions is the Internet's acceptability as a shopping medium (Jahng, Jain, & Ramamurthy, 2001). Previous research has revealed attitude towards online shopping is a significant predictor of making online purchases (Yang et al., 2007) and purchasing behavior (George, 2004; Yang et al., 2007). In a similar study conducted in Jordan about the effects of perceived risks on Online shopping , the researcher, Masoud (2013) discussed consumer perceived risks specifcally the financial risk, product risk, time risk, delivery risk, social risk, and information security risk. However, other variables such as health risk, after-sale risk, website design style & characteristics, and trust in the web site weren’t explored in their study. Thus, this study intend to know how these risks corroborate with fraudulent activities and how it influences the online shopping attitude of MSU GenSan SHS students. In conclusion, the importance of identifying such attitudes is crucial in creating rational decisions for future online shopping transactions. During a time of scarcity, avoidance of such fraudulent acts is a matter of critical thinking and the awareness of dangers in the web in terms of socio-economic, health, and even psychological status. This study aims to uphold online responsibility for students for them to develop skills as future citizens of the country. 2|Page Theoretical Background The study is anchored on three theories, namely: Martin Fishbein’s Theory of Reasoned Action (TRA), the Theory of Planned Behavior (TPB) by Icek Ajzen, and Bauer’s Perceived Risk Theory. According to Eagly & Chaiken (1993; Gärling et al., 1998), an attitude is defined here as a subjective evaluation of a behaviour that disposes a person to behave in a certain way towards it. Since online shopping represents a form of new technology, psychological theories such as TRA and TPB have always been used as the basis for several studies of Internet purchasing behavior (George, 2002; Khalifa and Limayem, 2003). According to empirical evidence, the TRA operationalization states that intention serves as a bridge between behavior and attitude toward behavior. Intention is influenced not only by attitudes, but also by subjective norms or the perceived social pressure exerted by important others, such as parents and good friends, to perform or not perform a behavior. The theory of planned behavior, on the other hand, is a good place to start when researching online purchase intent. TPB proposes several predictors of technology adoption behavior, including perceived behavioral control, perceived credibility, and subjective norms. Individual perceptions of the availability or lack of necessary resources and opportunities to develop a specific behavior are represented by perceived behavioral control (Ajzen and Madden, 1986). Subjective norms reflect how the user is influenced by significant references' perceptions of his or her individual behavior, such as friends or colleagues (Fishbein and Ajzen, 1973; Schofield, 1974). The TPB begins by defining the behavior of interest in detail. In terms of its intended audience, the action it entails, and the setting in which it takes place, as well as the deadline, these elements can be defined in a variety of ways, specificity and generality levels. Meanwhile, Bauer's Perceived Risk Theory identified a link between a new shopping channel's perceived risk and the decision to buy through it. Examining previous 3|Page debates on consumer perceived risk definitions, scholars favored two major components as an appropriate definition of perceived risk: the probability of a loss and the subjective feeling of unfavorable consequences. Perceived risk is primarily concerned with researching and selecting information about products or services prior to making a purchasing decision. If online customers' actual purchasing experiences differed from their purchasing goals, they would perceive a higher level of risk. Previous research has widely used several types of perceived risk. Financial risk refers to the possibility of monetary loss that consumers may face after purchasing specific products or services. Physical risk is related to safety issues that arise as a result of using the product, particularly those that are directly related to health and security. 4|Page Research Questions The study primarily aims to find out the effect of digital fraud on online shopping attitude of MSU-Gensan Senior High School students during covid-19 pandemic. This study seeks to answer the following questions: 1. What are the common digital fraud experienced by MSU-Gensan Senior High School students during covid-19 pandemic? 2. What are the effects of digital fraud on online shopping attitude of MSUGensan Senior High School students during covid-19 pandemic? Hypotheses H1: Infomercial purchase and mail‐order purchase are the common digital fraud experienced by MSU-Gensan Senior High School students during covid-19 pandemic. H2: The digital fraud experienced by MSU-Gensan Senior High School students will have a negative impact on their online shopping attitude especially on perceived risks, infrastructural variables, easy & convenient return policy, website design and trust & security. Significance of the Study The findings of this study will provide good material for the online buyers to address the implications of digital fraud victimization. This study will be conducted with the goal of gathering critical information and expertise about the chosen issue from respondents, current studies, and associated websites, in order to provide the following to individuals. The direct recipients of the output of this research will be the Senior High School students of Mindanao State University General Santos City. This study will help the SHS students learn the factors of online fraud and how this could affect their decision-making 5|Page in purchasing online. The research topic will also encourage students to evaluate their attitudes when purchasing and prevent them from being a victim of digital fraud. Parents are another group that will benefit from this research. This research will aid them in comprehending how their children's views influence their selections, as well as the things they consider while shopping online. Online consumers are another aspect of this study. Even if the variables are limited to Senior High School Students of Mindanao State University-GSC, they will benefit from this study in the sense that they can learn about the elements that contribute to online fraud along with the factors they contemplate while buying products online. In light of the learning theories mentioned beforehand as well as the reviewed literature and findings of prior studies, this study will be conducted to validate previous studies by making Senior High School Students of Mindanao State University-GSC as variables. Likewise, it will also be beneficial to other researchers in gaining insight to further improve the body of knowledge on online purchasing. Scope and Delimitation The primary focus of the study will be finding out the effects of digital fraud on online shopping attitude of MSU-Gensan Senior High School students during covid-19 pandemic. Narrative approach in qualitative research design will be adopted in this study. Convenience sampling technique will be used for the sampling design. The respondents will undergo depth interview to collect primary data for the research. The depth interviews will be conducted one-to-one basis with each student via online. The depth interviews will be an unstructured to obtain information in direct way. The critical variable will be MSUGensan Senior High School students of S.Y. 2021-2022, as the respondents of the research.The study will be conducted within the vicinity of Mindanao. This study will be limited to the effects of digital fraud on online shopping attitude only. The online shopping behavior and its factor will not be addressed in this study. The study was conceptualized 6|Page on November 2021, including all data gathering processes involved, it will end on January of the 2022. Definition of Terms E-commerce refers to the exchange of business information using network-based technologies (Bajaj & Nag, 2005, p. 14). The activity of purchasing products and services on the internet. Online Shopping is the process or action of making purchases or services via the Internet. Online Shopping Attitude is the psychological state in terms of making purchases via the internet. It is the perception and evaluation of an individual during an online purchase. Digital Fraud refers to cybercrime activity that takes place over the internet which is designed to scam people out of money. Infomercial Purchase refers to the intention of purchase due to infomercial influence. Infomercial is a term derived from the words “information” and “commercial” and is a type of a marketing strategy used by companies to sell products (Jampani, 2021). Mail-Order Purchase is the process of buying products and services by mail delivery. Perceived Risk is the consumer’s perception of a potential hazard or chance of loss (Bhasin, 2018). Consumers' uncertainty before making a purchase. 7|Page CHAPTER 2 Review of Related Literature This section contains the review of related literature to further clarify the study’s variables and methods. The supporting information was taken from online websites and the gathered information will support the current study. This study intend to know the effect of digital fraud on online shopping attitude of MSU-Gensan Senior High School students during covid-19 pandemic and its impact on their influences when buying online. This study will be beneficial to eCommerce particularly to buyers. The business world is evolving and so are the scammer especially amid the pandemic where e-commerce business rise. In business transactions, e-commerce has become an indispensable marketing channel. Stores and services available on the internet in B2C transactions, are important sales channels. The investigation of online consumer behavior has been conducted in multiple disciplines including information systems, marketing, management science, psychology and social psychology, etc. (Hoffman and Novak, 1996; Koufaris, 2002; Gefen et al., 2003; Pavlou, 2003, 2006; Cheung et al., 2005; Zhou et al, 2007). Generally speaking the trend of e-commerce has increased rapidly in the recent years with the development of the internet and due to the easy accessibility of internet usage. Easy access to the internet has driven consumers to shop online, in fact, according to the University of California, Los Angeles (UCLA) communication policy (2001). Over the last few years' different online consumer behavior models have been developed to understand and predict the wide range of decisions that consumers make based on the background of customer profile, online shop profile, and other intervening factors. However, researchers have suggested that as compared to traditional consumer behavior, online behaviors of consumers are subtly different in nature because of unique characteristics and interplay of technology, culture and differences in diffusion of ecommerce (Chau et al., 2002). Online Shopping Attitude The psychological state of consumers when making purchases over the Internet is referred to as their attitude toward online shopping. The process of online purchasing behavior refers to the products purchased online. The five-step process of online purchasing behavior is similar to that of traditional shopping behavior (Liang & Lai 2000). For example, when a consumer recognizes the need to purchase a product (book), they turn to the internet to buy online and begin to search for information and alternatives before making the purchase that best meets their needs. Before making a final purchase, consumers are bombarded with a variety of factors that limit or influence their final decision. Muhammad Umar Sultan and MD Nasir Uddin (2011) decided to research consumer attitudes toward online shopping, focusing on the factors that influence consumers to shop online in Gotland. The researchers of this study decided to study four factors such as convenience, time savings, website design/features, and security and analyzed who are the online shoppers based on their demography using primary data collection methods such as questionnaires from consumers as to what factors influence consumers to purchase online and their demographics. The population chosen for the study is Gotland, and it has been narrowed down to Gotland University students, the University cafeteria, and the Gotland Public Library. The sample size chosen for this study is 100, using the convenience sampling technique. According to the findings, website design/features are the most appealing and influencing factor for online shoppers in Gotland, followed by convenience as the second most influencing factor and time saving as the third most influencing factor. Results have also showed that security is of important concern among online shoppers in Gotland. The research has also found that there are some other factors which influence online shoppers including, less price, discount, feedback from previous customers and quality of product. For the second research question, who are online shoppers in term of demography: the correlation results for the age and attitudes towards online shopping has showed that elderly people are not so keen to shop online. Whereas for education, it is concluded that higher education makes 9|Page online shopping less attractive, for the income the correlation results are so weak hence, they could not conclude anything out of it. Reasons behind the choice of theories One of the most important and essential aspects of thesis writing is selecting theories to support the research issue. Reading past publications and research papers in the subject of consumer behavior and online purchasing led to the development of some key theories, which will be described in the following paragraphs. One of them is the Consumer Buying Behavior Process, which assists in determining what factors influence a consumer's decision to make an online purchase. Researchers discovered that when it comes to online purchasing, customers start with a need or an issue that needs to be addressed in some way, then move on to information search, and eventually make a purchase. This buying process explains the stages that one must do in order to complete a purchase. Icek Ajzen (1988, 1991) presented Theory of Planned Behavior (TPB) as an extension of Theory of Reasoned Action. This is a crucial notion because it establishes a link between attitudes and actions. It enables one to comprehend how one can influence people's conduct. This idea will aid in understanding how consumers' shopping habits have shifted to internet purchases rather than in-store purchases. Theories of planned behavior (TPB) Theory of planned behavior proposed by Icek Ajzen (1988, 1991) actually provides a link between attitude and behavior. Consumer action is guided by three considerations: Behavioral Beliefs, Normative Beliefs, and Control Beliefs. TPB is essentially a development of TRA (theory of reason action), which was proposed by (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975). As proposed by Icek Ajzen (1988, 1991), intention plays a crucial role in the execution of a behavior. Icek Ajzen also claimed that some elements influence a specific conduct, and that these factors are 10 | P a g e recorded by the purpose. These intents, according to Icek Ajzen, demonstrate how much effort one is prepared to put forth in order to do a specific behavior. As you can see in the diagram below, attitude toward behavior and subjective norm are the main elements that determine intention, and as previously said, intention plays a crucial role in conduct. On the other hand, perceived behavioral control influences intention, which leads to actual behavioral control. Behavioral belief: It’s about one’s belief about consequences of particular behavior, Icek Ajzen (1988, 1991). Attitude toward behavior: An individual's positive or negative evaluation of self-performance of the particular behavior. The concept is the degree to which performance of the behavior is positively or negatively valued. It is determined by the total set of accessible behavioral beliefs linking the behavior to various outcomes and other attributes. Normative belief: An individual's perception about particular behavior, which is influenced by the judgment of significant others. These actors can be your parents, spouse, friends, teachers, etc. Subjective norm: It refers to the belief about whether most people approve or disapprove of the behavior. It relates to a person's beliefs about whether peers and people of importance to the person think he or she should engage in the behavior, Wayne W. LaMorte (2019). Subjective influence is basically the social influence factor. 11 | P a g e Perceived behavioral control: It refers to a person's perception of the ease or difficulty of performing the behavior of interest. Perceived behavioral control varies across situations and actions, which results in a person having varying perceptions of behavioral control depending on the situation. This construct of the theory was added later, and created the shift from the Theory of Reasoned Action to the Theory of Planned Behavior, Wayne W. LaMorte (2019). Control beliefs: Person’s beliefs about the presence of circumstances that may help or prevent performance of the behavior, Ajzen (2001). Perceived risk Despite the advantages of internet commerce over traditional commerce and the positive outlook for future growth of online buying, the drawbacks of this shopping approach are becoming increasingly apparent (Ko et al., 2004). Risk plays an essential role in consumer behavior, and it makes a valuable contribution towards explaining information-searching behavior and consumer purchase decision making. While compared to traditional retail formats, consumers perceive a larger amount of risk when shopping on the Internet (Lee & Tan, 2003). Perceived risk is described as the prospect of losing money during online buying in order to get a desired result; it is a combination of uncertainty and the likelihood of a serious outcome (Ko et al., 2010). The concept of perceived risk has been recorded through the use of numerous scales that assess how threatening events are viewed (Featherman & Pavlou, 2002). Consumers' desire to acquire items over the internet is reduced by their perception of danger (Barnes et al., 2007). Consumers may be hesitant to provide the credit card information to any commercial web provider on the Internet, and they simply, do not trust most web companies sufficiently to engage in money-related exchange relationships. However, not all consumers have the same impression of risks and costs. While some purchasers view electronic commerce as a risky and expensive way to shop, others enjoy its benefits, such 12 | P a g e as the simplicity with which they can find information and compare items and pricing. In any event, it's reasonable to assume that consumers will consider a variety of signals when formulating their opinions and thoughts about a website (Martin & Camarero, 2009). Forsythe and Shi (2003) proposed that private risk, product risk and the risk of unknown origin would impact on the online shopping and could explain the barriers of online shopping. Financial risks, product risks, convenience risks, health risks, convenience risks, time risks, delivery risks, after-sale risks, performance, psychological, social, and privacy risks, website design style and characteristics, and trust in the web site, according to previous studies, all influence online consumers' purchasing behavior significantly (Martin & Camarero, 2009; Tasi & Yeh, 2010; Almousa, 2011; Javadi et al., 2012; Zhang et al., 2012). This research purposes six important perceived risk variables such as financial risk, product risk, nondelivery risk, health risk, social risk, and convenience risk, affecting purchasing behavior were chosen in this research model according to traditional literature on them, and the empirical evidence obtained from online stores experts and customers. Financial risk The monetary cost connected with the purchase price as well as the subsequent maintenance cost is referred to as financial risk (Jacoby & Kaplan, 1972; Peter & Ryan, 1976; Stone & Gronhaug, 1993). Many internet clients' main financial issue is credit card fraud, with increasing concerns about financial loss during online transactions. The procedure, rather than the product or service, is more closely linked to perceived financial risk (Akram, 2008). Because certain E-commerce websites are not secure enough, credit card fraud is one of the most commonly expressed concerns when shopping online (Saprikis et al., 2010). It's also defined as a potential net loss of money, and it includes consumers' fear of using their credit cards online, which is a big deterrent to making online purchases (Maignan & Lukas, 1997). Product risk Product risks include many categories of product failure among users. Physical examination of a product as well as product features are included in these areas (Alreck & Settle 2002). This risk also involves a product's financial damage, as well as the loss of items from the beginning to the end. The key cause for suspicion is that it could be a case of money fraud (Crespo, del Bosque et al. 13 | P a g e 2009). According to Aliff et al. (2014), a product is discussed about a product that may not work as planned after purchase. As a result, buyers' inability to sense or sample things before purchasing them is a major worry when shopping online, and this increases the perceived risk of the product (Saprikis, Chouliara & Vlachopoulou, 2010). Convenience risk Convenience risk is associated with consumers’ perception that they will face difficulty in order place, or be unable to cancel one place order, or there will be delays in receiving or returning products (Forsythe, Liu, Shannon, & Gardner, 2006). Potential loss of time when a customer searches about product on website and compares one company’s product to another product, is irritating for customers because most people do not know how to operate and how to search right products; Furthermore, purchasing products takes long time before using them (Hsin Chang & Wen Chen, 2008). Non delivery risk According to Aliff et al. (2014) stated that non-delivery risk as the potential fail of delivery. Therefore, one of the biggest worried that is non-delivery risk occur when customers decided to shops online to purchase. The product may get corrupt when delivery process, delivered to a wrong places, or in some cases, delayed (Naiyi, 2004). Besides, consumer scale about the delivery will be delayed because various reasons, such as the delivery company won’t deliver the purchased item within on time (Aliff et al., 2014). Social risk Social risk refers to the consumer's notion that the goods he or she purchased will cause disapproval from family and friends, as well as a loss of social status, as a result of selecting an incorrect product via an inappropriate channel (Li & Zhang, 2002; Popli & Mishra, 2015). Furthermore, social risk erodes people's confidence and self-esteem with the product (Amin & Mahasan, 2014). Social risk has a detrimental impact on online purchase behavior, and consumers avoid it (Khan-szabist & Arshad-szabist, 2010; Qureshi, Fatima, & Sarwar, 2014; Rind et al., 2017). Furthermore, some research has discovered a link between social risk and online buying habits (Almousa, 2014). 14 | P a g e Health risk It claims that consumers who shop online strain their eyes by sitting in front of the computer for extended periods of time, or that consumers suffer from back pain as a result of sitting for long periods of time in order to find actual products online. Zhang et al. (2012) identify the risk that consumers face when they sit in front of the system for an extended period of time. They may be subjected to vision impairment or high pressure, which may lead to the purchase of a counterfeit product that is harmful or dangerous to one's health. According to Amin and Mahasan (2014), consumers purchasing unsafe products or the amount of energy expended on internet shopping may cause health risks. Easy & convenient return policy Jarvenpaa and Todd (1997) suggested to offer discounts, focus on products that have low delivery cost, benchmark the e-store against traditional retail stores and catalog stores, emphasize brand name products and product quality, reduce shopping effort by providing search techniques, respond promptly to questions, provide no-cost/no-hassle return policy, provide rich product descriptions including images and words, emphasize security measures and provide customer testimonial. Website design and trust Helander et al., 1997) suggested that many general principles for design of human-computer interfaces like, simplicity, support, visibility, reversible action, feedback, accessibility and personalization apply to the design of e-commerce environments also. It was also observed that frequent and occasional web buyers are indeed not more price-sensitive than non-web buyers as online price comparison is time-consuming and may not be worth it given the small differences in price between different vendors. Further smart online retailers will try to differentiate their products or services to make direct price-comparisons less important. Security in the web Gefen (2002) highlighted recent uses of SERVQUAL (a multi-dimensional research tool for capturing consumer expectations and perceptions of a service along five dimensions thought to represent service quality) in evaluating constructs in the B2C domain have found significant results. Tangibility has been found to be associated with increased consumer loyalty and a combined dimension of responsiveness, reliability, and assurance with increased 15 | P a g e consumer trust. Constantinides (2004) highlighted that components of uncertainty reducing elements are ―frequently asked questions‖ (FAQs) and conflict-resolution policies. An easy access to this type of information will enhance trust. It will also reduce the number of inquiries of customers with questions on such issues. Constantinides (2004) suggested that online marketers should identify elements enhancing or undermining trust among potential customers and try to understand how those can affect the online customer‘s perceptions. Such knowledge is valuable for incorporating the right mix of trust-establishing elements in the web site and creating the proper organizational infrastructure, i.e. technological, organizational as well as managerial, which is required for delivering this mix. Positive attitude towards online buying The results of the study done in US by Dillon and Harry (2004), indicates that young adults with a history of e-commerce purchasing experience have a more positive attitude towards online buying than do young adults without e-commerce purchasing experience. In a related finding, a history of e-commerce purchasing experience serves as a good predictor of future e-commerce commodity purchases. Additionally, consumer risk and shopping experience perceptions were found to influence experienced e-commerce shoppers’ commodity purchase decisions more than customer service or consumer risk. Factors Influencing Online Buying Behavior of college students: A qualitative analysis In a study conducted by Jadhav and Khanna (2016), an attempt was made to explore the factors influencing the online buying behaviour of the college students, in Mumbai. The main influencing factors for online shopping were identified as availability, low price, promotions, comparison, convenience, and customer service, perceived ease of use, attitude, time consciousness, trust and variety seeking. Flipkart.com and Myntra.com were the most preferred choice of online retailers mentioned by the students to shop from the online stores. Tickets, electronic goods accessories, apparels, books, electronic goods, footwear, instant recharge of cell phone, gifting items, were the major 16 | P a g e categories of products / services bought by most of the students. Cash on delivery was the most preferred mode of payment stated by the students while doing online shopping. Convenience sampling method was used to select the sample of 25 college students and qualitative content analysis was used for analysing the textual content of the depth interview data. Depth Interview Procedure To effectively manage the depth interviews, based on the literature review and pilot interviews of three regular online shoppers an interview guide was prepared by the researchers before conducting the depth interviews. The questions in the interview guide covered the points which helped in getting an idea of the factors influencing the online buying behaviour of the college students. The interviews were conducted in English language, over a period of two months (i.e., May – June 2014) and the response of each student was manually noted for analysis and interpretation. The length of each interview varied from 45mins to 60mins. A total of 25 college students gave the consent and participated in the depth interviews from both undergraduate and post graduate levels. According to the study by Griffin and Hauser (1993), 20-30 interviews are necessary to get 90-95% of customer needs. (Jadhav & Khanna, 2016) Method of Data Analysis As cited in the study conducted by Jadhav and Khanna (2016), qualitative content analysis was used for analysing the textual content of the depth interview data. Qualitative content analysis is defined as a research method for the subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns (Hsieh & Shannon, 2005). The final result of the qualitative content analysis is a list of categories and themes (Cho & Lee, 2014). The assistance of an independent researcher was taken for the data analysis. The analysis began with word by word understanding of the interview transcripts created from the researcher’s notes. Similar answers were grouped together and coded. Coding is an essential procedure of 17 | P a g e doing a data analysis in a qualitative research (Strauss, 1987). It is not necessary that coding requires data to be collected through tape recordings and videotapes, in fact, one can code microscopically on researcher notes from interviews, field observations, and other documents including published material (Strauss, 1987). The themes and codes from the data analysis done by the independent researcher were compared with the themes and codes derived by the main researchers. The labelling of the main factors derived from the data analysis was discussed and finalized by the researchers. Thus, the data analysis process incorporated the review and coding of the depth interview data, identifying the themes or patterns, organizing, labelling and presenting the findings. 18 | P a g e CHAPTER 3 Methodology Research Design Our research regarding The effects of digital fraud on online shopping attitude of MSU-Gensan Senior High School students during covid-19 pandemic is qualitative research. Since the response of the research participants will be from their experience, the research approach of this study will be narrative research. The study will be done in four (4) phases. Phase one will be gathering of information from secondary source, in order to obtain background knowledge. In phase two, the depth interview will be conducted. During phase three, the answers of the respondents will be analysed. Lastly, the drawing of conclusion will be done in phase four. Research Respondents In this study, there will be five (5) respondents needed. Participants will come from a population of Senior High School students of MSU-Gensan, an age group that ranges from 15 to 19 years old. In order to gather information about Online Shopping attitudes, the respondents should at least know about Online Shopping and regularly engage in online entrepreneurial activities. Also, the participants should have at least experienced online-shopping fraud. Sampling Design Non-probability method of sample selection will be used for this study because of its ability to target particular groups of the population. Among the several methods of nonprobability sampling, the convenience sampling technique will be used since it is convenient and easily available. MSU-Gensan Senior High School students are considered as a large population, gathering hundreds of students. Therefore, they will be chosen as the selected population for this study. Data Gathering Procedure The data will be gathered from primary source, therefore, it will come from the response of the respondents. The primary data collection will include depth interview using open-ended questions formulated by the researcher based on to the secondary data gathered. The selected respondent will undergo depth interviews one-to-one basis with each student via online. Questions will be constructed in simple language in order to reduce the risk of ambiguity. The questions will be taken from previous literature on Consumer’s attitudes towards online shopping with a view to validate the research more and some of the questions will be self structured to cover the diversity of research problems. The questionnaire consists of two main parts and one sub part, first part will mainly focused on questions pertaining to the effects of digital fraud on online shopping attitude. Second part of the questionnaire will cover one of our research question that is who are online shoppers in terms of demography and to see if there will be any difference in relation to digital fraud experienced by MSU-Gensan Senior High School students to online shopping attitude. 20 | P a g e References Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. http://dx.doi.org/10.1016/07495978(91)90020-T Akram, Qaisar; Dagfinn Rime and Lucio Sarno, (2008), Does the law of one price hold in international financial markets Evidence from tick data, No 2008/19, Working Paper, Norges Bank Almousa, M. (2011), “Perceived Risk in Apparel Online Shopping: A Multi Dimensional Perspective”, Canadian Social Science, 7(2), 23-31. Almousa, M. (2014). The influence of risk perception in online purchasing behavior: Examination of an early-stage online market. International Review of Management and Business Research, 3(2), 779. Alreck, P. L. and Settle, R. B. (2002) ‘F-A-S-T: A Standardized Measure of Time Traits’, Proceedings of the Special Conference on Time and Consumer Behavior. Amin, S. and Mahasan, S.S. (2019) Difference between Consumer Preferences to Choose between the Traditional Retailing and Modern Retailing. Journal of Business and Retail Management Research, 14, 63-70. https://doi.org/10.24052/JBRMR/V14IS01/ART-06 Ariff, M. S. M., Sylvester, M., Zakuan, N., Ismail, K., & Ali, K. M. (2014). Consumer Perceived Risk, Attitude and Online Shopping Behaviour; Empirical Evidence from Malaysia. IOP Conference Series: Materials Science and Engineering, 58, 012007. https://doi.org/10.1088/1757-899x/58/1/012007 Bajaj, K. K., & Nag, D. (2005). E-Commerce (p. 14). Retrieved from https://books.google.com.ph/books?id=Co8iBAAAQBAJ&dq=e+commerce&lr=&s ource=gbs_navlinks_s Barnes, S., Bauer, H., Neumann, M. & Huber, F. (2007), “Segmenting cyberspace: a customer typology for the internet”, European Journal of Marketing, 41(1/2), 7193. Bhandari, P. (2020, July 30). An introduction to qualitative research. Retrieved from https://www.scribbr.com/methodology/qualitative- research/#:%7E:text=Revised%20on%20July%2030%2C%202020,generate%20 new%20ideas%20for%20research. Bhasin, H. (2018, July 18). What Is Perceived risk? Definition & 5 Types of Perceived Risks. Retrieved from Marketing91 website: https://www.marketing91.com/perceived-risk/ Bhatnagar, A., Misra, S., & Rao, H. R. (2000). On Risk, Convenience and Internet Shopping Behavior. Communications of the ACM, 48(2), 98-105. http://dx.doi.org/10.1145/353360.353371 Chang, H. H., & Chen, S. W. (2008). The impact of online store environment cues on purchase intention: Trust and perceived risk as a mediator. Online information review. Chiu, Y., Lin, C., & Tang, L. (2005). Gender differs: assessing a model of online purchase intentions in e‐tail service. International Journal of Service Industry Management, 16(5), 416–435. https://doi.org/10.1108/09564230510625741 Cho, J., & Lee, E. H. (2014). Reducing Confusion about Grounded Theory and Qualitative Content Analysis: Similarities and Differences. The Qualitative Report. https://doi.org/10.46743/2160-3715/2014.1028 Comegys, C., Hannula, M., & Váisánen, J. (2009). Effects of consumer trust and risk on online purchase decision-making: A comparison of Finnish and United States students. International Journal of Management, 26(2), 295-308. Cheung, C. M., Chan, G. W., & Limayem, M. (2005). A Critical Review of Online Consumer Behavior. Journal of Electronic Commerce in Organizations, 3(4), 1– 19. https://doi.org/10.4018/jeco.2005100101 Crespo, A. H., del Bosque, I. R., & de los Salmones Sanchez, M. M. G. (2009). The influence of perceived risk on Internet shopping behavior: A multidimensional perspective. Journal of Risk Research, 12(2), 259-277. Cunningham, C. V. (2019, July 23). The Difference Between E-Commerce and Online Shopping. Retrieved December 11, 2021, from https://medium.com/@candicevcunningham/the-difference-between-ecommerce-and-online-shopping-7a7fc86185c5 Dillon, T. and Reif, H.L. (2004) Factors Influencing Consumers Factors Influencing Consumers: E-Commerce Commodity Purchases Commerce Commodity Purchases. Information Technology, Learning, and Performance Journal, 22, 230-233. Featherman, M. & Paul A. (2002), “Predicting E-Services Adoption: A Perceived Risk Facets Perspective”, Americas Conference in Information Systems, Dallas, TX: Association for Information Systems, 1034-1046. Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley. Forsythe, S., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of interactive marketing, 20(2), 55-75. Forsythe, S. & Shi, B. (2003), “Consumer Patronage and Risk Perceptions in Internet Shopping”, Journal of Business Research, 56(11), 867-875. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519 George, J. F. (2004). The theory of planned behavior and Internet purchasing. Internet Research, 14(3), 198–212. https://doi.org/10.1108/10662240410542634 Hoffman, D. L., & Novak, T. P. (1996). Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations. Journal of Marketing, 60(3), 50. https://doi.org/10.2307/1251841 Hsieh, H. F., & Shannon, S. E. (2005). Three Approaches to Qualitative Content Analysis. Qualitative Health Research, 15, 1277-1288. http://dx.doi.org/10.1177/1049732305276687 Isabelle So, M. (2019, December 6). Filipino Consumers’ Behavior towards Online Shopping. Retrieved from https://www.academia.edu/41199650/Filipino_Consumers_Behavior_towards_O nline_Shopping?pop_sutd=false Jacoby, J. & Kaplan, L. B. (1972) ,"The Components of Perceived Risk", in SV Proceedings of the Third Annual Conference of the Association for Consumer Research, eds. M. Venkatesan, Chicago, IL : Association for Consumer Research, Pages: 382-393. Jadhav, V., & Khanna, M. (2016). Factors Influencing Online Buying Behavior of College Students: A Qualitative Analysis. The Qualitative Report. https://doi.org/10.46743/2160-3715/2016.2445 Jahng, J., Jain, H., & Ramamurthy, K. (2001). The Impact of Electronic Commerce Environment on User Behavior: The Case of a Complex Product. E-Service Journal, 1(1), 41. https://doi.org/10.2979/esj.2001.1.1.41 Jampani, S. K. Goud. (2021). Effect of Consumers’ Demographic Characteristics on Infomercial Characteristics and Purchase Intention towards Infomercial Advertised Products. Revista Gestão Inovação E Tecnologias, 11(4), 276–288. https://doi.org/10.47059/revistageintec.v11i4.2106 Javadi , M., Dolatabadi, H., Nourbakhsh, M., Poursaeedi, A., & Asadollahi, A. (2012). “An Analysis of Factors Affecting on Online Shopping Behaviour of consumers”, International Journal of Marketing Studies; 4(5), 81-98. Johnson, D. P. (2017, November 28). How Attitude Toward the Behavior, Subjective Norm, and Perceived Behavioral Control Affects Information Security Behavior Intention. Retrieved from https://scholarworks.waldenu.edu/dissertations/4454/ Kalia, P., Kaur, N., & Singh, T. (2018). A Review of Factors Affecting Online Buying Behavior. Apeejay Journal of Management & Technology, 11(2), 49. https://doi.org/10.29385/apeejay.11.2.2016.58-73 Kayworth, T. & Whitten, D. (2010), “Effective information security requires a balance of social and technology factors”, MIS Q. Ex., 9(3): 163 – 175. Ken Research. (2021, April 7). Increasing Online Shoppers Coupled with Growing Middle Class Population has Stimulated Growth in Philippines E-Commerce Logistics Market: Ken Research. Retrieved December 11, 2021, from https://www.kenresearch.com/blog/2021/04/covid-19-impact-n-philippines-ecommerce-logistics/ Khan-SZABIST, S. H., & Arshad-SZABIST, S. Z. (2010). Why E-Commerce Remains Unsuccessful in Pakistan Ko, H., Jung, J., Kim J., & Shim, S.W. (2004), “Cross-cultural differences in perceived risk of online shopping”, Journal of Interactive Advertising, 4(2), 20-29. Koufaris, M. (2002). Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior. Information Systems Research, 13(2), 205–223. http://www.jstor.org/stable/23011056 LaMorte, MD, PhD, MPH, W. W. (2019, September 9). The Theory of Planned Behavior. Retrieved from https://sphweb.bumc.bu.edu/otlt/mphmodules/sb/behavioralchangetheories/BehavioralChangeTheories3.html Lee, K., & Tan, S. (2003), “E-retailing Versus Physical Retailing: A Theoretical Model and Empirical Test of Consumer Choice”, Journal of Business Research, 56(11), 877–885. Li, N., & Zhang, P. (2002). Consumer online shopping attitudes and behavior: An assessment of research. Former Departments, Centers, Institutes and Projects, 57. Maignan I, Lukas BA(1997). The nature and social uses of the internet: a qualitative investigation. J Consum Aff 1997;31(2):346 – 71. Martin, S. & Camarero, C. (2009), “How perceived risk affects online buying”, Online Information Review, 33(4), 629-654. Masoud, E.Y. (2013). The Effect of Perceived Risk on Online Shopping in Jordan. Masoud, E. (2013). The Effect of Perceived Risk on Online Shopping in Jordan. European Journal of Business and Management, 5, 76-87. Moshrefjavadi, M. H., Rezaie Dolatabadi, H., Nourbakhsh, M., Poursaeedi, A., & Asadollahi, A. (2012). An Analysis of Factors Affecting on Online Shopping Behavior of Consumers. International Journal of Marketing Studies, 4(5). https://doi.org/10.5539/ijms.v4n5p81 Naiyi Ye, (2004) “Dimensions of consumer‟s perceived risk in online shopping”, Journal of Electronic Science and Technology of China, vol. 2, no.3, pp. 177-182. Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275 Pavlou, P. A., & Fygenson, M. (2006). Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior. MIS Quarterly, 30(1), 115–143. https://doi.org/10.2307/25148720 Peter, J. P., & Ryan, M. J. (1976). An investigation of perceived risk at the brand level. Journal of Marketing Research, 13(2), 184–188. https://doi.org/10.2307/3150856 Philippine News Agency, & Crismundo, K. (2021, March 24). Digital fraud attempts in PH rise amid pandemic. Retrieved December 11, 2021, from https://www.pna.gov.ph/articles/1134735 Popli, A., & Mishra, S. (2015). Factors of Perceived Risk Affecting Online Purchase Decisions of Consumers. Pacific Business Review International, 8 (2), 49, 58. Qureshi, H. A., Fatima, R., & Sarwar, A. (2014). BARRIERS TO ADOPTION OF ONLINE SHOPPING IN PAKISTAN. Science International, 26(3). Schlosser, A. (2003). Experiencing Products in the Virtual World: The Role of Goal and Imagery in Influencing Attitudes versus Purchase Intentions. Journal of Consumer Research, 30(2), 184–198. https://doi.org/10.1086/376807 Sin, S. S., Nor, K. M., & Al-Agaga, A. M. (2012). Factors Affecting Malaysian young consumers’ online purchase intention in social media websites. Procedia-Social and Behavioral Sciences, 40, 326-333. Tasi, Y. & Yeh, J. (2010), “Perceived risk of information security and privacy in online shopping: A study of environmentally sustainable products”, African Journal of Business Management, 4(18), 4057-4066. TransUnion. (2021, March 24). One Year after COVID-19 Pandemic Declared, New TransUnion Research Shows Digital Fraud Attempts from the Philippines Have Increased. Retrieved December 11, 2021, from https://newsroom.transunion.ph/one-year-after-covid-19-pandemic-declared-newtransunion-research-shows-digital-fraud-attempts-from-the-philippines-haveincreased/ Saprikis, V., Chouliara, A., & Vlachopoulou, M. (2010). Perceptions towards Online Shopping: Analyzing the Greek University Students’ Attitude. Communications of the IBIMA, 1–13. https://doi.org/10.5171/2010.854516 Stone, R. N., & Grønhaug, K. (1993). Perceived Risk: Further Considerations for the Marketing Discipline. European Journal of Marketing, 27(3), 39–50. https://doi.org/10.1108/03090569310026637 Strauss, A.L. (1987) Qualitative Analysis for Social Scientists. Cambridge University Press, New York. http://dx.doi.org/10.1017/CBO9780511557842 Sultan, M.U., & Uddin, M.N. (2011). Consumers’ Attitude towards Online Shopping : Factors influencing Gotland consumers to shop online. What Is Internet Fraud? Types of Internet Fraud. (n.d.). Retrieved from Fortinet website: https://www.fortinet.com/resources/cyberglossary/internet-fraud Yang, B., Lester, D., & James, S. (2007). Attitudes Toward Buying Online as Predictors of Shopping Online for British and American Respondents. CyberPsychology & Behavior, 10(2), 198–203. https://doi.org/10.1089/cpb.2006.9968 Zhang, L., Tan,W., Xu, Y. & Tan, G. (2012) , “Dimensions of Consumers’ Perceived Risk and Their Influences on Online Consumers’ Purchasing Behaviour”, Communications in Information Science and Management Engineering, 2(7), 814. Zhou, L., Dai, L., & Zhang, D. (2007). Online shopping acceptance model — a critical survey of consumer factors in online shopping. Journal of Electronic Commerce Research, 8, 41.
