The Effect of Mobile Shopping on Shopping Attitudes: A Field Study on the Food Sector in Istanbul ()
1. Introduction
The digital era has commenced with the advancement of technology, which people are eager to embrace and is becoming increasingly significant. The internet has spread due to advancements in communication, and digital communication has strengthened. In addition to the features offered by other classic phones, mobile phones used as smartphones allow us to organize personal organizations from the web browser with the PDA (Personal Digital Assistant) system, which is a computer technology, and it can be conveniently held in our hands. Consumer’s shopping behavior has turned into mobile shopping due to the rapid growth of digital technology in the changing world. There are several factors that influence the consumer’s decision to purchase or not purchase a product. The consumer’s enjoyment of this process impacts their attitudes. Consumers rely on the food they purchase to meet their physiological needs and sustain their life. The objective of this study is to examine how the rise of mobile shopping in the digital era impacts consumers’ shopping attitudes. After conducting extensive literature research, a questionnaire form was created on Google Form, tailored to the scales, and administered to mobile internet users in Istanbul over a one-month period in May 2023. The data, which were collected from a group of 407 mobile internet consumers, were utilized to evaluate the hypotheses based on the developed model. The SPSS statistical program was utilized to analyze the data, and necessary evaluations were made as a result of conducting ANOVA and t-tests. These evaluations led to the conclusion that mobile shopping had a positive impact on shopping attitude. The study we conducted will mainly focus on mobile shopping for consumers, and this trend will be amplified by the use of smartphones. It has been observed that Generation Z, who grew up in the digital age, is more inclined towards this trend. It is believed that conducting similar studies in various sectors and regions will yield more impactful results in terms of area. Components have been specified for three reasons: 1) ease of use when formatting individual papers, 2) automatic compliance to electronic requirements that facilitate the concurrent or later production of electronic products, and 3) conformity of style throughout a journal paper. Margins, column widths, line spacing, and type styles are built-in; examples of the type styles are provided throughout this document and are identified in italic type, within parentheses, following the example. Some components, such as multi-leveled equations, graphics, and tables are not prescribed, although the various table text styles are provided. The formatter will need to create these components, incorporating the applicable criteria that follow.
2. Mobile Shopping
The act of purchasing and selling goods or services via a smartphone or other mobile device is referred to as mobile shopping. The development of this condition has long been studied. Its usage is expected to further increase due to the rising number of individuals adopting mobile devices like smartphones and tablets for their shopping needs (Abed, 2021) . The way we shop has been significantly changed by the utilization of mobile technologies, extensive connectivity, and contactless transactions. While the iPhone offers shoppers new tools with app options that allow them to make purchases from their phones, it can be also used at home or various locations such as the Amazon Dropbox, Homedepot store, or Walmart Pick-up service as an example of this. Mobile retailing is a new type of consumer buying experience where the customer buys with collections at home or in the store (pickup boutique) with a mobile phone. In addition, e-retail can generally be defined as selling goods and services to consumers via internet B2C. Many studies today focus on how the group of millennials can force them to be more consumption-oriented than their predecessors (Soylemez & Karahan, 2020: pp. 555-575) . Important key performance indicators to monitor for those responsible for developing an m-commerce application include:
· Total mobile traffic,
· Total application traffic,
· Average order value,
· Value of orders over time.
Similarly, monitoring the mobile add-to-cart rate will help users see if they have become customers (Çelik & Taş, 2021) . M-commerce developers may also be interested in logging average page load times, mobile cart conversion rates, and SMS (short message service) subscriptions (Kayıkçı et al., 2019) . For mobile payment products, when a mobile device is paired with a user’s bank card information, the phone can be provisioned through a payment terminal to pay for a product. Contactless payment using a mobile device uses near-field communication technology (Liu et al., 2022) . Another major reason not to shop online is that most of the people interviewed live alone and work during the day. These types of scenarios are where general e-retailing is not a good option (Kwateng et al., 2019) . Many people enjoy the convenience of purchasing with a mobile phone. Through Store apps, it is possible to easily, quickly, and efficiently buy products from your favorite brands (Gürün, 2020) . All of this information about mobile retailing is helping marketers understand customer mobility to tailor more personalized customer experiences, especially when it applies to retention and loyalty (Melovic et al., 2021) . If m-commerce is the best way to succeed in the mobile world, it is by creating an e-commerce space, making it accessible to the majority of the population and allowing anyone to easily buy products, regardless of the device they use to shop online (Dharmesti et al., 2021; Gürün and İkikat Tümer, 2021) . For retailers, this means seizing huge opportunities to be there and be useful in shoppers’ micro-moments. It uncovers the mobile retail trends that are influencing the industry and explains their significance. The Internet not only serves as the largest network for computers and communication, connecting numerous devices worldwide and facilitating instant transactions, but it is also extensively prevalent. The internet, known as the micro-moments of shopping, usually begins to think of ideas in this direction in the consumer when the need or desire of people to buy a product occurs. These moments usually fall into one of three categories:
· I need some ideas,
· Which is the best,
· When the research is finished and it’s time to make a decision.
The shopping experience is no longer exclusive to physical spaces, which necessitates a seamless retail experience. To meet customers’ needs and enhance their experience, retailers are currently embracing new in-store technologies and looking to enhance their online presence through a website, app, or social media. The use of mobile devices, which can also help the customer in the decision-making process, influences customers’ decisions in ways that retailers cannot control (Çil, 2021) . Brands have become increasingly visible in consumers’ daily lives, all thanks to smartphones (Melovic et al., 2021) . Businesses have increasingly turned their attention to the development of mobile applications (Apps) because of the rapid growth of smart devices (Dakduk et al., 2020) . In today’s workforce, data and technology provide digital information (Dharmesti et al., 2021) . Today’s market environment is becoming increasingly complex, and mobile-friendly websites are a crucial aspect of it. Furthermore, the importance of these websites is heightened during the implementation process (Gürün and İkikat Tümer, 2021) . They need to create communication strategies that can be customized and tailored to their shopping group, specifically offering online transactional services (Karaoğlan and Durukan, 2022) . Retailers must adopt a hyper-personalization approach that incorporates entertainment, functionality, information, socialization, and intellectual stimulation, while simultaneously reducing features to enhance value for both shoppers and brands (Kayıkçı et al., 2019) . The most well-known mobile shopping sites are Sahibinden.com, Hepsiburada, N11.com, GittiGidiyor, Trendyol, Yemeksepeti, AliExpress. In general, instead of developing mobile-specific applications, companies produce content similar to that of internet-based applications for mobile platforms. In this respect, leading business system entrepreneurs (such as Apple, Google, Microsoft) have defined various principles to aid developers in creating more effective and user-friendly mobile applications (Semiz and Tarık, 2021) .
3. Shopping Attitude
Attitude refers to a person’s capacity to form evaluations about an object or the trajectory of an idea, as well as to establish their own objectives. Attitudes occur when individuals mentally position themselves to either favor or disfavor things and either approach or away from them. For instance, numerous individuals who have developed the attitude that maintaining a nutritious diet is crucial view vegetables and chicken as a viable healthy option. Consequently, the trend of per capita vegetable consumption in recent times has influenced consumer attitudes, suggesting that meat and chicken are detrimental to health. Conducting research on consumer attitudes can prove advantageous for businesses. Understanding attitudes and beliefs is the first step toward forcing or restricting them. It is very difficult to change attitudes. If a person’s attitudes fit a pattern and change a style, it may require making many difficult adjustments. It is easier for a company to create products that align with existing attitudes than it is to change attitudes towards products. Of course, there are exceptions where the high costs of increasing attitudes can work (Taş and Kaçar, 2019) . Studying consumer behavior is important because it helps marketers understand what influences consumers’ purchasing decisions. By gaining insight into consumers’ decision-making process, marketers can fill the market gaps and identify necessary updates for products. Studying consumer behavior assists marketers in determining the most effective way to present their products, thereby having a significant impact on consumers. The key to successfully reaching and engaging customers and ultimately converting them into making a purchase lies in comprehending their buying behavior.
Types of consumer behavior; There are four main types of consumer behavior:
1) Complex buying behavior; Imagine buying a house or a car; these are examples of complex buying behavior (Gürün and İkikat Tümer, 2021) .
2) Dissonance-reducing buying behavior; When considering the purchase of a lawn mower, it is necessary to choose one based on price and convenience. However, after the purchase, it is necessary to ask for confirmation that the right choice has been made.
3) Habitual buying behavior; Imagine you are shopping at the grocery store: you go to the supermarket and bought your preferred type of bread. This is not a strong brand loyalty, but a familiar pattern.
4) Diversity seeking behavior; In this case, the consumer purchases a different product not because of dissatisfaction with the previous one, but rather due to a desire for variety. It is similar to when one explores new scents of shower gel (Yahşi and Hopcam, 2021) .
Consumer attitude is defined as the good or bad feelings that a person has towards a situation or object. As it is known, having a positive attitude increases the consumer’s purchase of that product, while this also results in the possibility of liking or not wanting it (Biçer et al., 2019; Yapraklı et al., 2019) . Consumer attitudes basically consist of beliefs, emotions and behavioral intentions towards certain objects. Belief plays a vital role for consumers because it can be positive or negative towards an object. For example, some may say that tea is good and relieves tension, while others may say that too much tea is not good for health. Human beliefs are not true and can vary from case to case. Consumers have certain feelings towards certain products or brands. These feelings are sometimes based on certain beliefs, sometimes they are not. For instance, when an individual contemplates cheese pizza, he or she feels uneasy due to the excessive quantity of cheese or fat present in it. The study of consumer behavior involves analyzing the processes and actions that consumers employ when choosing, using (consuming), and getting rid of products and services, as well as their emotional, mental, and behavioral reactions. Comprehending consumer behavior is vital for businesses in developing impactful marketing strategies capable of influencing consumers’ decision-making procedures (Erserim, 2019; Harnadi et al., 2019; Rachbini et al., 2020; Çil, 2021) . The hierarchy of influences is a theory that discusses the impact of advertising on customers’ decisions to purchase certain products and brands. The theory encompasses a number of stages that advertisers must follow, from gaining customers’ awareness to eventual buying behavior (Yahşi and Hopcan, 2021) . Hierarchy of influences theory explains the impact of advertising on customers’ decisions to purchase certain products and brands at a number of behavioral stages. The hierarchy of influences model consists of three main stages: the cognitive stage (awareness, knowledge); the affective stage (liking, preference, conviction); and the behavioral stage (purchase). Influence theory, a study based on behavioral psychology, has been criticized by cognitive psychologists for ignoring its applicability and different advertising goals to understand each stage of purchasing behavior (Soni et al., 2019) . The functions of attitudes are as follows:
· Utilitarian function; Attitudes help people adapt to different situations and circumstances.
· Ego-defensive function; Attitudes are formed to protect the ego. We are all uncomfortable with our self-esteem and image, which is why the product that raises our ego is the target of this type of attitude.
· Value Expressive function; Attitudes often represent the values that an individual has. Values are gained through our upbringing and education. Our value system encourages or discourages us from buying certain products. For example, our value system may or may not allow us to buy cigarettes, alcohol, drugs, etc.
· Knowledge function; Individuals are constantly looking for knowledge and information. Upon receiving information about a specific product, individuals have the ability to shape or alter their attitudes towards it (Dharmesti et al., 2021; Gürün and İkikat Tümer, 2021; Melovic et al., 2021) .
4. Similar Studies in the Field
If we refer to similar studies related to this study;
In their study titled “Effects of online shopping trends on consumer-buying behavior,” Irsa et al. (2015) found that out of the five factors (trust, convenience, privacy, time, and product variety), trust plays a more significant role for young individuals; and consequently, young individuals can purchase desired products more comfortably from websites they trust.
In their study titled “The effect of social media marketing on consumer purchasing behavior”, Şahin et al. (2017) found that users can connect with other users through social media tools, find the opportunity to express themselves through shares and thus create a different communication environment. In these environments, consumers have the chance to express themselves by creating their own content and can also engage in communication and marketing activities to reach their current and potential target groups within the enterprises. Consumers expect businesses to listen to them, engage in activities that are appropriate to their demands, and build a sense of trust in them. Today, businesses are increasingly relying on social media and internet-based applications due to the effectiveness and power of communication they offer. This situation makes social media activities inevitable for both consumers and businesses.
In their study titled “Factors Affecting Consumers’ Attitudes Towards Online Shopping and Their Intention to Make Online Shopping: Türkiye-Slovenia Comparison,” Yağcı et al. (2017) discuss various factors that influence consumers’ behavior when it comes to online shopping, and these factors include privacy, security, trust, the concept of saving time, the ease of using the website, and the enjoyment of shopping. The results of the study showed that the factors of safety and enjoyment of shopping had an impact on attitude in both countries.
5. Method
5.1. Purpose and Importance of the Study
With the advancements in information technologies, the store environments have become more crowded on specific days or during peak times, this results in an unsuitable atmosphere, long queues, and the need to choose between products in showcases or aisles, and many negative and tedious processes are left behind, making it easier to make choices through the internet. Regardless of the time, the shopping is one of the most important factors that direct consumer behavior and attitudes. The advancements and progress in the communication industry today have brought about numerous alterations in the shopping habits of consumers. Concurrently, businesses have begun addressing not only the physical needs of consumers but also their emotional desires. Today, the preferred situation in mobile shopping is the expectation of benefits in the abstract direction, in addition to the tangible benefits of post-modern consumers, provided by enterprises. It is one of the important points for individuals to evaluate their ongoing feelings, tendencies, bias and objectivity towards an object or an idea. Their attitudes are formed based on three basic elements. Attempting to address all three of these elements in the formation of attitudes or the alteration of current attitudes involves the utilization of messages in marketing communication. This study aims to investigate the consumer’s attitude towards mobile shopping, an area where they have a balanced interest and frequently engage in. The research will focus specifically on the food sector in Istanbul. The development of the internet has led businesses in the direction of meeting the shopping needs of consumers through their phones or tablets. Mobile shopping is the rapid delivery of products to consumers based on their personal information that will help promote them in order to provide value. The acceptance and rejection attitudes of mobile phone users towards mobile ads, along with the corresponding behavior patterns they have developed, are mainly rational factors, and these factors include the cognitive element of understanding, as well as the beliefs that shape their opinions based on their attitudes, information, and personal beliefs. The element in the emotional sphere includes even more positive happiness, joy, appreciation as well as satisfaction of a neutral knowledge in it, as well as regret, anger, boredom, which is a negative feeling and emotion. According to the element in the behavioral field, an attitude describes the transformation tendency of behavior. In the cognitive element, it includes the consumer’s product, brand features, as well as beliefs and knowledge about the services offered in the store. Determining the impact of all these factors on the consumer’s mobile shopping and conducting a field study on the consumer’s response in the food sector, which is one of the primary sectors in a city like Istanbul, is crucial.
5.2. Study Model
In this section, the purchases made through mobile phones and tablets for mobile shopping will be analyzed, and this analysis will use a statistical model designed to determine the level of attitudes among mobile users in Istanbul; the aim is to assess the impact of questionnaire responses on consumer attitudes and identify the specific factors influencing this situation. The exploratory system, which utilizes mixed research methods, will be employed for this purpose. Within this system, quantitative data will be gathered and subsequently interpreted. A research model was developed specifically to assess the attitudes of consumers in Istanbul towards marketing activities within the food sector, particularly those conducted through the mobile shopping method (Figure 1).
5.3. Hypotheses of the Study
The approach designed while constructing hypotheses is observed as follows; What is the impact of mobile shopping on the attitude of the consumer: here it is
expected to determine the situation of consumers who shop for food via the Internet.
HYPOTHESIS 1: There is a strong and positive correlation in the effect of mobile shopping marketing on consumer attitude (Agreed).
HYPOTHESIS 2: Mobile shopping marketing does not have a strong and positive behavioral correlation with consumer attitudes towards the food sector.
HYPOTHESIS 3: There is a significant difference between demographic variables and shopping attitude.
5.4. Universe and Sampling
This study will be carried out with users who use mobile phones for mobile shopping in Istanbul. The population of Istanbul in 2023 is 16,067,031 according to the estimated data and this population data is calculated according to the population growth rates in the previous years (Population, 2023) . As of September 2022, the number of mobile subscribers in Türkiye has increased to 90.82 million. According to data for year 2021, the number of mobile phone subscribers in Istanbul was determined as 23,689,626 individuals (BTK, 2022) . Therefore, in order to ascertain the shopping habits of mobile shoppers in Istanbul, we will examine all individuals who use the internet on their mobile phones and have reached the age of 18. Out of the 410 people surveyed, 3 were not included in the evaluation because they were unavailable, resulting in a total of 407 people being evaluated. The questionnaire will be created according to the 5-point Likert layout and will be filled with a face-to-face interview and the application of the questionnaire prepared in Google Form. The study will be divided into layers of mobile shopping consumers and will evaluate the descriptive demographic characteristics of consumers; it aims to determine the attitudes of consumers for food consumption in Istanbul towards shopping here, along with a 90% confidence interval that includes the study universe and an error rate of 0.10 percent. Sampling method is a sampling system in which the representation of the subgroups in the sample in relation to the subgroups in the research universe is handled within the scope of the guarantee (Balcı, 2013: s. 100) .
5.5. Data Collection Technique
Mobile internet providers have been guaranteed, and a checklist has been created for consumers who prefer to shop. The validity index will be determined by assessing the checklist based on the opinions of the experts who took it. When creating the scale on the effect of mobile shopping on consumer attitudes, the final shape of the scale will be given as a result of the opinions of the experts on the subject. As a result of preliminary research and pilot studies, it will be applied at a rate of 10 times the number of items applied to the consumer. The validity and reliability analyses will determine the final version of the questionnaire to be used, based on the data obtained from these processes. The survey includes the first part, which primarily comprises demographic variables like age, gender, marital status, education, income, and profession. In the second part, there are 28-question scale statements. Of these, 19 is related to the mobile shopping situation and 9 are expressions of the shopping attitude. The six sub-dimensions of the mobile shopping attitude scale are Privacy, Pleasure, Location, Comfort, Product and Price. Again, the sub-dimensions of shopping attitude consist of three sub-dimensions: Emotional, Cognitive and Behavioral. Serhat Karaoğlan’s July 2020 doctoral thesis titled “Developing and Implementing a Scale Regarding the Perceived Benefit of Mobile Shopping in the Age of Digital Marketing” was utilized for the mobile shopping scale, and Hasan (2010) and Zhou et al. (2007) used the shopping attitude scale in their studies, while Özgüven (2011) “Analysis of the Relationship Between Consumers’ Attitudes Towards Online Shopping and Demographic Characteristics” was used for the same purpose. The questionnaire was primarily administered to a pilot group of 50 individuals and evaluated for its appropriateness in the study. The authors’ consent was obtained regarding the relevant scales. Furthermore, the study obtained approval from the ethics committee. However, the study could not be finished within the one-month timeframe at May 2023. The study conducted validity and reliability analyses, resulting in the final version of the questionnaire being provided.
5.6. Limitations of the Study
These are global standardization efforts in the methods applied in calculating data. In addition to the temporal irregularities of the scope of the study, financial limitations are also encountered. Due to these limitations, a study that can be applied areal throughout Türkiye was investigated only in Istanbul.
6. Statistical Analysis of Data
6.1. Validity Reliability Analysis Results for the Scales Used in the Pilot Questionnaire
Table 1 presents the Cronbach’s Alpha coefficient results for the scales and their sub-dimensions. Based on the results, the Mobile Shopping scale and its sub-dimensions, namely “Mobile Shopping Comfort”, “Mobile Shopping Location”, and “Mobile Shopping Privacy”, are deemed to be highly reliable. The sub-dimensions, namely “Mobile Shopping Product Price” and “Mobile Shopping A Pleasure” within the Mobile Shopping scale are also considered highly reliable. Similarly, the Shopping Attitude Scale and its sub-dimensions, namely “Shopping Attitude Emotional” and “Shopping Attitude Behavioral,” are highly reliable. Additionally, the sub-dimension, namely “Shopping Attitude Cognitive” within the Shopping Attitude Scale is also highly reliable.
6.2. Frequency Status
Based on the results from the frequency distributions categorized by gender, the study had a participation rate of 34.0% for females and 66.0% for males. Based on the frequency distributions regarding their marital status, 54.0% of participants
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Table 1. Cronbach’s Alpha coefficient results for the scales and sub-dimensions within the scope of the research.
were single, while 46.0% were married. According to the frequency distributions based on age status, it was deduced from the obtained proportional results within the research scope that the highest accumulation was observed in the 33 - 42 age group, accounting for 26.0%, while the lowest accumulation was found in the 50 - 57 age group, representing 8.0%. The frequency distributions of the participants based on their educational status revealed that 32.0% had a High School education, 2.0% had an Associate Degree, 52.0% had an Undergraduate education, and 14.0% had a postgraduate education. Based on Monthly Income status of the participants, frequency distributions were analyzed in the study, and the proportional results revealed that the highest accumulation was observed in the groups with incomes ranging from 8501 to 11,500 TL (Turkish Lira) and from 11,501 to 15,500 TL (Turkish Lira), accounting for 28.0%; conversely, the lowest accumulation was found in the group with incomes 19,501 TL (Turkish Lira) and above, accounting for 10.0%. The frequency distributions based on profession status showed that the highest percentage was found in the participant group who preferred the other option, with 30.0%; conversely, the Housewife and Retiree groups had the lowest percentage, at 4.0%.
Upon examination of the statistical ratios in the frequency distribution of the items within the “Mobile Shopping Product Price” sub-dimension of the Mobile Shopping Scale, as well as the statistical ratios in the frequency distribution of the items within the descriptive statistics sub-dimension, and the averages within the descriptive statistics, it becomes apparent that the highest average is found in the “Mobile Shopping Product Price2” item, with a value of 4.18, whereas the smallest average is observed in the “Mobile Shopping Product Price5” item, with a value of 3.54.
Upon examination of the statistical ratios in the frequency distribution of the items within the “Mobile Shopping Comfort” sub-dimension of the Mobile Shopping Scale, as well as the statistical ratios in the frequency distribution of the items within the descriptive statistics sub-dimension, and the averages within the descriptive statistics, it becomes apparent that the highest average is found in the “Mobile Shopping Mobile Shopping Comfort4” item, with a value of 3.90, whereas the smallest average is observed in the “Mobile Shopping Comfort3” item, with a value of 3.54.
Upon examination of the statistical ratios in the frequency distribution of the items within the “Mobile Shopping Location” sub-dimension of the Mobile Shopping Scale, and the averages within the descriptive statistics, it becomes apparent that the highest average is found in the “Mobile Shopping Location3” item, with a value of 3.98, whereas the smallest average is observed in the “Mobile Shopping Location1” item, with a value of 3.48.
Upon examination of the statistical ratios in the frequency distribution of the items within the “Mobile Shopping Pleasure” sub-dimension of the Mobile Shopping Scale, and the averages within the descriptive statistics, it becomes apparent that the highest average is found in the “Mobile Shopping Pleasure2” item, with a value of x = 3.80, whereas the smallest average is observed in the “Mobile Shopping Pleasure3” item, with a value of 3.32.
Upon examination of the statistical ratios in the frequency distribution of the items within the “Mobile Shopping Privacy” sub-dimension of the Mobile Shopping Scale, as well as the statistical ratios in the frequency distribution of the items within the descriptive statistics, and the averages within the descriptive statistics, it becomes apparent that the highest average is found in the “Mobile Shopping Privacy2” item, with a value of 4.14, whereas the smallest average is observed in the “Mobile Shopping Privacy1” item, with a value of 4.10.
Table 2 presents the descriptive statistics for the Mobile Shopping Scale and its sub-dimensions. Upon examining the averages, it becomes apparent that the sub-dimension with the highest average is “Mobile Shopping Scale Privacy Mean” at 4.12, while the sub-dimension with the lowest average is “Mobile Shopping Scale Pleasure Mean” at 3.56.
Upon examination of the statistical ratios and averages of the frequency distribution in the “Shopping Attitude Cognitive” sub-dimension of the Shopping Attitude Scale and the averages within the descriptive statistics, it becomes apparent that the highest average is found in the “Shopping Attitude Cognitive2” item with the value of 3.90, whereas the smallest average is observed in the “Shopping Attitude Cognitive3” item with the value of 3.52.
Upon examination of the statistical ratios and averages of the frequency distribution in the “Shopping Attitude Emotional” sub-dimension of the Shopping Attitude Scale and the data averages within the descriptive statistics, it becomes apparent that the highest average is found in the “Shopping Attitude Emotional2” item with the value of 3.90, whereas the smallest average is observed in the “Shopping Attitude Emotional1” item with the value of 3.32.
Upon examination of the statistical ratios and averages of the frequency distribution in the “Shopping Attitude Behavioral” sub-dimension of the Shopping Attitude Scale and the averages within the descriptive statistics, it becomes apparent that the highest average is found in the “Shopping Attitude Behavioral1” item with the value of 4.16, whereas the smallest average is observed in the “Shopping Attitude Behavioral2” item with the value of 2.04.
Table 3 presents descriptive statistics for the Shopping Attitude Scale and its sub-dimensions. Upon examining the averages, it becomes evident that the sub-dimension with the highest average is “Shopping Attitude Scale Cognitive” at 3.71, whereas the sub-dimension with the lowest average is “Shopping Attitude Scale Behavioral” at 3.03.
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Table 2. Descriptive statistics for mobile shopping scale and its sub-dimensions.
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Table 3. Descriptive statistics for mobile shopping scale and its sub-dimensions.
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Table 4. Cronbach’s Alpha coefficient results for the scales and sub-dimensions within the scope of the research.
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Table 5. Descriptive statistics for mobile shopping scale and its sub-dimensions.
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Table 6. Descriptive statistics for the shopping attitude scale and its sub-dimensions.
Table 4 presents the Cronbach’s Alpha coefficient results for the scales and sub-dimensions within the scope of the research. Based on the results, it was determined that the scales and sub-dimensions examined in the research were highly reliable, except for one sub-dimension. The sub-dimension “Shopping Attitude Behavioral” within the Shopping Attitude scale was found to be “fairly reliable”.
Table 5 presents the descriptive statistics for the Mobile Shopping Scale and its sub-dimensions. Upon examining the averages, it becomes evident that the “Mobile Shopping Scale Privacy Mean” sub-dimension has the highest average of 4.00, while the “Mobile Shopping Scale Pleasure Mean” sub-dimension has the smallest average of 3.54.
Table 6 presents descriptive statistics for the Shopping Attitude Scale and its sub-dimensions. Upon examination of the averages, it is evident that the “Shopping Attitude Scale Cognitive” sub-dimension has the highest average of 3.62, whereas the “Shopping Attitude Scale Behavioral” sub-dimension has the smallest average of 3.08. The study used a t-test to determine if there was a difference
in the Mobile Shopping Scale and its sub-dimensions based on gender. The results indicated that there was no statistically significant difference between males and females in their use of the Mobile Shopping Scale and its sub-dimensions, as measured in the questionnaire.
The study used a t-test to determine if there were any differences between the Mobile Shopping Scale and its sub-dimensions based on Marital Status. The results indicated that there were no statistically significant differences in the Mobile Shopping Scale and its sub-dimensions when considering the Marital Status variable.
The study used ANOVA test to determine if there were any differences between the Mobile Shopping Scale and its sub-dimensions based on Age group variable. The results indicated that there were no statistically significant differences in the Mobile Shopping Scale and its sub-dimensions when considering the Age group variable.
The study used ANOVA test to determine if there were any differences between the Mobile Shopping Scale and its sub-dimensions based on Education Status variable. The results indicated that there were no statistically significant differences in the Mobile Shopping Scale and its sub-dimensions when considering the Education status variable.
The study used ANOVA test to determine if there were any differences between the Mobile Shopping Scale and its sub-dimensions based on Monthly Income Status variable. The results indicated that there were statistically significant differences in the Mobile Shopping Scale and its sub-dimensions when considering the Monthly Income status variable. The groups that are the source of the difference are determined using the Tukey test and recorded alongside the relevant scale.
The study used ANOVA test to determine if there were any differences between the Mobile Shopping Scale and its sub-dimensions based on Profession Status variable. The results indicated that there were no statistically significant differences in the Mobile Shopping Scale and its sub-dimensions when considering the Profession status variable.
The study used a t-test to determine if there was a difference in the Shopping Attitude Scale and its sub-dimensions based on gender variable. The results revealed a statistically significant difference in the Shopping Attitude Scale and its sub-dimensions when considering the gender variable. The source of the difference is that the behavioral shopping attitude perception is higher in female participants than in male participants.
The study used a t-test to determine if there was a difference in the Shopping Attitude Scale and its sub-dimensions based on Marital Status variable. The results indicated that there were no statistically significant differences in the Shopping Attitude Scale and its sub-dimensions when considering the Marital Status variable.
The study used ANOVA test to determine if there were any differences between the Shopping Attitude Scale and its sub-dimensions based on Age group variable. The results indicated that there were no statistically significant differences in the Shopping Attitude Scale and its sub-dimensions when considering the Age group variable.
The study used ANOVA test to determine if there were any differences between the Shopping Attitude Scale and its sub-dimensions based on Education status variable. The results indicated that there were no statistically significant differences in the Shopping Attitude Scale and its sub-dimensions when considering the Education status variable.
The study used ANOVA test to determine if there were any differences between the Shopping Attitude Scale and its sub-dimensions based on Monthly Income status variable. The results indicated that there were no statistically significant differences in the Shopping Attitude Scale and its sub-dimensions when considering the Monthly Income status variable.
The study used ANOVA test to determine if there were any differences between the Shopping Attitude Scale and its sub-dimensions based on Profession status variable. The results indicated that there were no statistically significant differences in the Shopping Attitude Scale and its sub-dimensions when considering the Profession status variable.
When analyzing the correlation analysis results of the sub-dimensions of the Mobile Shopping Scale in relation to each other, it was found that the correlation coefficients between the sub-dimensions were deemed statistically significant. All sub-dimensions exhibit a positive relationship, meaning that if one variable increases, it is expected that the other variable will also increase.
The correlation analysis revealed that the correlation coefficients between the sub-dimensions of the Shopping Attitude Scale were statistically significant. This indicates a positive relationship between all sub-dimensions, meaning that an increase in one variable is expected to result in an increase in the other.
The correlation analysis results indicate that there is a statistically significant relationship between the sub-dimensions of the Mobile Shopping Scale and the Shopping Attitude Scale. It is evident that there is a positive correlation between these variables. Therefore, if one variable increases, it is expected that the other variable will also increase.
The sentiment for the explanatory variable (independent variable) Mobile Shopping variable is provided in Table 7 of the linear regression model, which uses the dependent variable Shopping Attitude. The ANOVA test revealed that the model was statistically significant. In addition, the determination coefficient of the model (corrected) was calculated as 0.131. Accordingly, 13.1% of the variability in the Shopping Attitude variable is explained by the Mobile Shopping variable through the linear regression model. According to the student-t test performed for the significance of the coefficients of the regression model, both coefficients were found to be statistically significant. Based on these results, the
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Table 7. Linear regression model results for shopping attitude variable and mobile shopping variable.
estimation of the regression line
(Shopping Attitude) = 0.586+ 0.298* (Mobile Shopping)
Based on the standard regression coefficient, if the Mobile Shopping variable increases by 1 unit, it is predicted that the Shopping Attitude variable will increase by 0.365 units.
Table 7 presents the results of the linear regression model for the effect of Mobile Shopping sub-dimensions on Shopping Attitude. ANOVA test was used to analyze the significance of the linear regression model between Shopping Attitude and Mobile Shopping dimensions. According to the ANOVA test, the model was found to be statistically significant. In addition, the corrected R-squared coefficient for the model was calculated as 0.131. This value shows that 13.1% of Shopping Attitude variability is explained by the Mobile Shopping dimensions through a linear regression model. The coefficients in the Mobile Shopping Scale Pleasure Mean and Mobile Shopping Scale Privacy Mean sub-dimensions were found to be statistically significant and the coefficients in the other dimensions were found to be statistically insignificant.
The results of a linear regression model are the effect of Mobile Shopping sub-dimensions on the sub-dimension of Shopping Attitude Scale Cognitive Mean. The ANOVA test was used to examine the significance of the linear regression model between the Cognitive Mean sub-dimension of the Shopping Attitude Scale and the Mobile Shopping dimensions. According to ANOVA test, the model was found to be statistically significant. Furthermore, the model’s adjusted R-square coefficient was determined to be 0.062, indicating that 6.2% of the variance in the sub-dimension of the Shopping Attitude Scale Cognitive Mean can be accounted for by the linear regression model involving the Mobile Shopping dimensions. The coefficient of the Mobile Shopping Scale Comfort Mean sub-dimension was found to be statistically significant.
The results of a linear regression model show that the Mobile Shopping sub-dimensions have an effect on the Emotional Mean sub-dimension of the Shopping Attitude Scale. The ANOVA test was used to examine the significance of the linear regression model between the sub-dimension of Emotional Mean in the Shopping Attitude Scale and the dimensions of Mobile Shopping. According to ANOVA test, the model was found to be statistically significant. In addition, the adjusted R-square coefficient for the model was calculated as 0.122. This value demonstrates that 12.2% of the variability in the sub-dimension of Emotional Mean on the Shopping Attitude Scale is accounted for by the Mobile Shopping dimensions using a linear regression model. The coefficients for the Location and Pleasure dimensions, which are sub-dimensions of the Mobile Shopping Scale, were determined to be statistically significant.
The results of a linear regression model show that the Mobile Shopping sub-dimensions have an effect on the Behavioral Mean sub-dimension of the Shopping Attitude Scale. The ANOVA test was used to examine the significance of the linear regression model between the sub-dimension of Behavioral Mean in the Shopping Attitude Scale and the dimensions of Mobile Shopping. According to ANOVA test, the model was found to be statistically significant. In addition, the adjusted R-square coefficient for the model was calculated as 0.075. This value demonstrates that 7.5% of the variability in the sub-dimension of Behavioral Mean on the Shopping Attitude Scale is accounted for by the Mobile Shopping dimensions using a linear regression model. The six sub-dimensions of the mobile shopping attitude scale are Privacy, Pleasure, Location, Comfort, Product and Price. The coefficients for the Price and Privacy sub-dimensions, of the Mobile Shopping Scale were determined to be statistically significant.
7. Discussion and Conclusion
Mobile technology is generating a wide range of exceptional opportunities and novel sources of income for businesses across various industries, and this is achieved by offering paid mobile products and services and implementing innovative value-added solutions that leverage the advantages of mobility. In order to keep up with the rapid pace of change, companies are increasingly interested in comprehending the behavioral attitude of the contemporary mobile consumer. The goal of this article is to explore how users respond to various factors that impact their inclination to use mobile devices for online purchases, with a specific focus on the present circumstances. The current literature focuses on understanding the factors that may influence consumers’ intention to adopt m-shopping; these studies are mostly based on theories of behavioral intention such as the Technology Acceptance Model, Diffusion of Innovation, and Unified Acceptance and Technology Use Theory. This study combines new theoretical constructs with existing evidence in order to expand the Technology Acceptance Model, which was initially established by Davis and later enhanced by other researchers. The proposed model includes behavioral intent, mobile skill, pleasure, anxiety, perceived benefit, perceived ease of use, confidence, relationship drivers, and innovation. This study combines new theoretical constructs with existing evidence in order to expand the Technology Acceptance Model, which was initially established by Davis and later enhanced by other researchers. The proposed model includes behavioral intent, mobile skill, pleasure, anxiety, perceived benefit, perceived ease of use, confidence, relationship drivers, and innovation. This study combines new theoretical constructs with existing evidence in order to expand the Technology Acceptance Model, which was initially established by Davis and later enhanced by other researchers. The proposed model includes behavioral intent, mobile skill, pleasure, anxiety, perceived benefit, perceived ease of use, confidence, relationship drivers, and innovation. Mobile phones have long been talked about as the next great communication channel for marketers, and for good reason. The vast majority of today’s consumers always have their mobile devices close at hand. This positions the smartphone as the consumer’s most reliable and important device, while making it the marketer’s most direct and trustworthy means of interaction. Traditional marketing and sales strategies and techniques certainly don’t work for mobile consumers. However, as consumers embrace new mobile technologies and shopping behaviors, retailers must commit to continuous business and IT transformation at the same pace. Customers must enhance their relationships with consumers to the extent of becoming trusted partners, which entails offering value in return for their personal profiles and preference data, such as rewards, special treats, and discounts. Companies need to enhance their data collection and analytics systems in order to align their skills and strategies with data-driven approaches that consumers consider acceptable. In addition to everything mentioned above, the ongoing survey is being conducted in a country where there have been limited previous studies conducted. Furthermore, the conceptual model employed in this study utilizes marketing perspectives to address the issue of technology acceptance, specifically by exploring potential marketing strategies related to mobility characteristics, such as relationship drivers, pleasure, and anxiety structures. Relationship drivers have been found to have a positive effect on behavioral intention and a strong positive effect on perceived benefit. It is important to mention that companies providing mobile products and services, as well as organizations offering mobile services and solutions, can gain advantages from the connections established through m-shopping activities with their users. This is achieved by offering personalized services that are voluntary and can be accessed from any location at any given time. M-shopping solutions should have features that prioritize the importance of customers’ attitudes towards benefit and pleasure. Similarly, m-providers should focus on the benefits of m-shopping through advertising campaigns that leverage location-based activities. In order to enhance M-shopping intent, it is necessary for them to demonstrate to their customers that they can save both money and time through simplifying the purchasing procedure. M-coupons can also attract m-shoppers by simplifying their exchange, such as directly reducing the price instead of providing a discount for their next purchase, and this will contribute to m-shopping with evident positive attributes. Also, pleasure has a positive effect on behavioral intention and perceived benefit. Cronbach’s Alpha coefficient for the scales and sub-dimensions included in the research was obtained as “high reliability” ratio of the scales and sub-dimensions within the scope of the research except for one sub-dimension. The reliability of the “Shopping Attitude Behavioral” sub-dimension of the Shopping Attitude scale was obtained as “fairly reliable”. The study used a t-test to determine if there was a difference in the Shopping Attitude Scale and its sub-dimensions based on gender variable. The results revealed a statistically significant difference in the Shopping Attitude Scale and its sub-dimensions when considering the gender variable. The source of the difference is that the behavioral shopping attitude perception is higher in female participants than in male participants. The study used ANOVA test to determine if there were any differences between the Mobile Shopping Scale and its sub-dimensions based on Profession Status variable. The results indicated that there were no statistically significant differences in the Mobile Shopping Scale and its sub-dimensions when considering the Profession status variable. The study used ANOVA test to determine if there were any differences between the Mobile Shopping Scale and its sub-dimensions based on Monthly Income Status variable. The results indicated that there were statistically significant differences in the Mobile Shopping Scale and its sub-dimensions when considering the Monthly Income status variable. The groups that are the source of the difference are determined using the Tukey test and recorded alongside the relevant scale. The study used ANOVA test to determine if there were any differences between the Mobile Shopping Scale and its sub-dimensions based on Education Status variable. The results indicated that there were no statistically significant differences in the Mobile Shopping Scale and its sub-dimensions when considering the Education status variable. The study used ANOVA test to determine if there were any differences between the Mobile Shopping Scale and its sub-dimensions based on Age group variable. The results indicated that there were no statistically significant differences in the Mobile Shopping Scale and its sub-dimensions when considering the Age group variable. The study used a t-test to determine if there were any differences between the Mobile Shopping Scale and its sub-dimensions based on Marital Status. The results indicated that there were no statistically significant differences in the Mobile Shopping Scale and its sub-dimensions when considering the Marital Status variable. The study used a t-test to determine if there was a difference in the Mobile Shopping Scale and its sub-dimensions based on gender. The results indicated that there was no statistically significant difference between males and females in their use of the Mobile Shopping Scale and its sub-dimensions, as measured in the questionnaire. The ANOVA test was used to examine the significance of the linear regression model between the sub-dimension of Behavioral Mean in the Shopping Attitude Scale and the dimensions of Mobile Shopping. According to ANOVA test, the model was found to be statistically significant. In addition, the adjusted R-square coefficient for the model was calculated as 0.075. This value demonstrates that 7.5% of the variability in the sub-dimension of Behavioral Mean on the Shopping Attitude Scale is accounted for by the Mobile Shopping dimensions using a linear regression model. Among the six sub-variables of the Mobile Shopping Scale, the coefficients related to the Price and Privacy sub-dimensions were determined to be statistically significant. The sentiment for the explanatory variable (independent variable) Mobile Shopping variable is provided of the linear regression model, which uses the dependent variable Shopping Attitude. The ANOVA test revealed that the model was statistically significant. In addition, the determination coefficient of the model (corrected) was calculated as 0.131. Accordingly, 13.1% of the variability in the Shopping Attitude variable is explained by the Mobile Shopping variable through the linear regression model. According to the student-t test performed for the significance of the coefficients of the regression model, both coefficients were found to be statistically significant. Based on these results, the estimation of the regression line
(Shopping Attitude) = 0.586 + 0.298* (Mobile Shopping)
based on the standard regression coefficient, if the Mobile Shopping variable increases by 1 unit, it is predicted that the Shopping Attitude variable will increase by 0.365 units. The correlation analysis reveals the relationship between the sub-dimensions of the Mobile Shopping Scale and the Shopping Attitude Scale. The correlation coefficients of the sub-dimensions from both variables were determined to be statistically significant. It is observed that the variables have a positive correlation. In this case, when there is an increase in one of the variables, the other is expected to increase. The Shopping Attitude Scale is given with the results of correlation analysis for the relationship between the sub-dimensions. The correlation coefficients between the sub-dimensions were found to be statistically significant. There is a positive relationship between all sub-dimensions. In this case, when there is an increase in one of the variables, it is expected that the other one will also increase.
8. Recommendations
The concept of connecting the desire to adopt mobile services with the connections and bonds that can be formed through their usage, such as coupons, rewards, extra deals, and location-based marketing efforts, is exceptionally innovative and specifically tailored to mobile functionalities. Non-store forms of shopping can offer satisfaction to the profiles of internet-literate and tech-savvy shoppers. Such consumers can associate the shopping process with an innovative shopping channel, which gives them pleasure. Enjoyment has been discovered to be a factor that has a positive impact on the intention of online shopping, thus leading to the reason mentioned. In addition, the behavioral intent of the mobile shopping factor of the proposed model, as well as the results of our analyses, shows the relationship between enjoyment and perceived benefit and perceived ease of use. Non-store conversion styles will add excitement to the shopping of internet literate and technology enthusiasts. Such individuals can share the shopping process with a developed shopping channel, which shows that they can enjoy them. Therefore, since it has been determined that enjoyment is a factor that positively affects online shopping perspectives, businesses should give more importance to such behaviors in the future. In addition, in addition to determining the intention of the proposed model factor behavior in mobile shopping, regarding our analysis, the relationship between the usefulness that consumers feel pleasantly and the ease of use they perceive will create research areas for similar studies.