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The effect of prior knowledge on online purchase decision-making process

Sakharova Alexandra Sergeevna, Krasnova Daria Alexandrovna

Abstract

Purpose - is to identify how does the level of customers' expertise in knowledge of the category and the product itself affect the online purchase decision-making process.

Methodology - the paper is based on observations with the use of the additional strategy, as interviewing, collected from 50 respondents. It was conducted on the basis of two product categories: restaurants and pharmacy. The database is analyzed with the coding system, where dependent variable is presented, as stages of decision-making process, excluding post-purchase behavior, and independent variables, as the level of prior knowledge, time spent, participant`s age, use of feedbacks and search for benefits. The data is analyzed via qualitative and quantitative approach. The object of observation is a person who books a table in restaurant and purchase pills for stomachache pain online.

Findings - research revealed that different levels of primary knowledge affect the decision-making process about online purchases in different product categories. The level of prior knowledge for industries influences on online purchase decision making process in both spheres. By the way, decision making process for gastronomy sphere also is affected by spend time, where loyalty loopers make a decision more quickly than people who make an active evaluation of alternatives or do not go through all the steps of decision-making process. However, the pharmaceutic industry is influenced by the level of prior knowledge for the stomachache pills, where people who evaluate the alternatives have higher prior knowledge for a product, than those who are loyal or indifferent.

Research limitations - sample size of 50 respondents is not enough for such tool of quantitative analysis. The multinomial regression model does not provide the authors with the stable and significant results what could be explained as the limits of the sample. Another type of data collection could illustrate some other trends on online consumer behavior which were not obvious from the analysis of this research.

Practical implication - The results could help companies to analyze their positioning and promoting strategies in order to evaluate their competitiveness and full in the gaps in their ways of communications with potential customers. Moreover, findings could be implemented in brands' marketing strategies, if they do specialize or have a division on e-commerce market.

Key words: decision-making process, stages of decision-making process, online purchases, e-commerce, consumer behavior, online consumer behavior, prior knowledge, loyalty loop, evaluators, evaluation of alternatives, product experience, product perception.

Introduction

Nowadays Internet users tend to be overloaded with information which is broadcasted by brands, companies and users itself. PricewaterhouseCoopers conducted a research concerning digital advertisement marker in the timeframe of 2017-2020. The online trading market in Russia is constantly growing: in 2018, sales reached 1,150 billion rubles, and the number of online users reached 75 million. In 2018, the number of Russian buyers making purchases on the Internet every month reached 69%. (PwC 2019) Such impressive indicators stimulate brands to capture their target audience through online channels. At the end of 2018, leading Russian online stores recorded 881 million visits to their websites (from mobile and stationary devices).

The high importance of exclusive content to attract and retain an audience in the face of increasing competition forces OTT platforms to actively invest in its acquisition or production. Online advertising continues to grow at a rapid pace, as major media players and advertisers are increasingly focusing on online promotion channels, and consumers are choosing “screens in their pockets”. In addition, with the advent of new types of electronic devices (such as smart speakers with digital assistant support and smart TVs) that are changing the way consumers interact with digital multimedia services and content, new niches for competition are emerging.

By the end of 2018, the volume of the online advertising market in Russia exceeded the indicators of the television advertising market and amounted to $ 3.1 billion. According to the forecast, in the period until 2023 the segment will continue to grow at an average annual rate of 12% and reach a volume of $ 5.5 billion The main driver of market growth is the development of the mobile video advertising segment, which is becoming more accessible due to the penetration of high-speed Internet, the growth in the number of smartphones, the deployment of LTE 2.0 and 5G networks, as well as connecting an older audience (55+ years).

More than 4 million posts on personal blogs are published on the Internet daily: a third of them are published on sites, and the rest on social networks. (GfK 2019). This illustrates that Internet users receives a huge amount of information on a daily basis that leads to the fact, that all of consumers are familiar with great number of products, even in there was previous experience in testing products. It means that every consumer has prior knowledge concerning the majority of spheres. The majority of authors determine the concept of prior knowledge as the amount, type or organization of what an individual actually has stored in memory. ( Kanwar, Olson & Sims 1981)

A strong tendency of e-commerce market development drives market players to be active and increase their level of competitiveness by uploading digital advertisements in order to receive customers' attention and persuade them to make a purchase. This amount of advertisement content leads to enlarging customers' level of prior knowledge and affect their purchase decision-making process.

The aim of this research paper is to identify how does the level of customers' expertise in knowledge of the category and the product itself affect the online purchase decision-making process. There is a complexity of marketing and psychological spheres, which are updating quite quickly. During the analysis of existing literature, it was found out that many of the papers are not relevant due to the time frames, as some changes even in e-commerce could be seen, that is why there is a lack of relevant up-to-dated information, what illustrates us the gap in chosen sphere. Analyzed literature illustrates that the decision-making behavior is quite unique for each person. If it is specified on online process, there are not many researches which were conducted in order to evaluate customers' behavior on each step on decision-making path. The majority of them examines the way how do prior know knowledge affect decision-making, but there in an empty niche which explains the effect of prior knowledge on the exact decision-making step and other variable factors which also could play a role. That is why the key difference of this research paper is that there is deep analysis of consumer behavior which shows the impact of existing behavioral drivers on each step of decision-making process.

Several research questions were formulated to identify the direction the research development. The key research question is “How do prior knowledge influence on the online purchase decision making process”. Then there are other sub-questions, which specify the problem and divide the research on stages. They are following:

- What is the exact definition of “prior knowledge”? And how could it be measured.

- What are the stages of purchase decision-making process?

- Is there any difference between online and online purchase decision making process?

- What other factors could be crucial in online decision-making process?

The hypotheses of the research are going to be the following:

H1: Knowledge of product has an effect on purchase decision-making process outcomes

H1a: The more extensive level of the prior knowledge customer has, the less time he or she spends on the decision-making process.

H1b: The more extensive level of the prior knowledge consumer has, the less he or she is engaged in decision making processes with a smaller number of cycles.

H1c: Consumers with the limited level of prior knowledge engage in an online purchase decision-making process with a higher number of alternatives, than consumers with the extensive level of prior knowledge.

H2d: Consumers with a limited level of prior knowledge engage in online purchase decision-making processes with a higher number of evaluation or measuring criteria, compared to consumers with the extensive level of prior knowledge of product.

The research purpose of this paper is to identify what exact stages of online purchase decision-making process are affected by the level of consumers' prior knowledge and what other variables could have a crucial influence on e-commerce consumers behavior and find out are there any differences in consumers' online purchase decision-making process concerning the category of the product . The key idea of the research is to conduct a deep analysis of consumers' online behavior, which undoubtably could be useful for companies which are represented on e-commerce market. The results could help companies to analyze their positioning and promoting strategies in order to evaluate their competitiveness and full in the gaps in their ways of communications with potential customers. The findings of the research could be also implemented in brands' marketing strategies, if they do specialize or have a division on e-commerce market. The received results will illustrate brands how do their potential customers make a choice of purchase and what exactly pushes them to make an action. That is why companies could specify and distribute their resources in another way for capturing their target audience on a specific step of customers' decision-making path. This could be also useful for those companies which are going to become market players. As there is an analysis of their target audience behavior, what means that it is not needed for brands to become a pioneer on several marketing strategies or test a pilot version of advertisement and positioning strategies, because they are already provided with their customers decision-making path. So, it is just needed for companies to implement the results of the research and create such promoting strategy that will include and affect each step of consumers' online decision-making process with the stress on the particular variables which have an impact on the specific group of customers.

According to the literature review, the most appropriate way to get closer to the result observation, that could illustrate the respondents' behavior and each step of their online decision-making process. And then there is a need to conduct interviews with the respondents in order to indemnify the details of their decision-making process and discus why did they follow the exact path. It is also important to identify what drivers of the respondents to make a choice: what factors are crucial for them and why. This direction of the research could be useful in order to conduct a deep analysis of consumer online behavior, which could be implemented in a real-life case of brands and companies. Such method as observation with post-interviewing was chosen, because the most up-to-dated research articles were implemented it and support their choice with strong results and full description of customers' decision-making process and the supporting variables.

The objectives of the research are following:

- Identifying the most common definition and the measurements of prior knowledge.

- Finding out and compare models of decision-making process.

- Specifying differences between online and offline decision-making process.

- Creating database of respondents' behavior.

- Evaluating the results and comparing behavioral models of participants.

The paper is based on participant observations with the use of the additional strategy, as interviewing, to make the data more validity. The data is analyzed via qualitative and quantitative approach. The object of observation is a person who books a table in restaurant and purchase pills for stomachache pain online. The subject of observation is an online purchase decision making path. The interviewing allows determining the participants` level of prior knowledge. The methodology is divided into three parts: interview with responders about their level of prior knowledge in both industries - gastronomy and pharmaceutics, observation of participants` path with the coding key-decisions to a database, then interview with the responders to understand the factors, that may affect his decision-making path. The reason of choosing distinctive industries is that the gastronomy sphere, where a person should book a table in a restaurant for a family dinner is a part of people`s daily life, while pharmaceutics needs a specific knowledge and experience. Two different industries allow to see are there any differences in the impact of prior knowledge in decision making process, as it is supposing that for responders will be easier to choose a restaurant for dinner, than pills for stomachache.

An object of the research is considered to be the behavior of respondents. It also could be specified as online purchase consumers' behavior, as there are differences between online and offline decision-making path. Weather the subject of the research is the effect of prior knowledge on online purchase decision-making process. As the key idea of the research to find out the consumers' online purchase path and identify is the level of prior knowledge plays a role.

The research paper is divided into five parts. The first part is literature review that examines the existing models of purchase decision making process and all definitions which are needed and how they could be measured and this issues which were investigated previously concerning consumer online purchase behavior and level of prior knowledge of customers. The second part is dedicated to methodology, where the sample size, data sources and appropriate methods and analysis will be determined, then anticipated results and, finally, conclusions with justification of the research. Scientific contribution of the research paper is going to be following: presented paper is going to be concentrated on online purchase consumer's behavior.

That means that the results of the paper could be useful for the consumer behavior analysis sphere.

The details of the research id going to provide further researchers with the models that illustrates how does the level of prior knowledge affect the online purchase decision-making process.

This will be suitable for the more detailed research concerning consumers' purchasing behavior and factors which are crucial in customer's decision-making process. Moreover, the contribution to the consumers' behavior field is going to be explained in such variables which also affect еру decision-making process as the category of the product. Further researches could use this information in order to specify their research on the chosen sphere in case of including the models and the variables of present research in their models. They could also develop the idea of this research paper by specifying the cluster of the respondents or concerning the product's category. Received models could be implemented in the further research as the starting point of discussion or the basic analysis type for receiving the necessary information which could be developed.

Literature review

In order to identify the effect of prior knowledge on online purchase decision-making process there is a need to analyze previous experience of researchers. The structure of literature review is going to be the following: firstly, there is a part connected with the purchase decision-making process. The idea is to analyze the existing models of consumers' purchase behavior, then to identify the differences between online and offline one and find out the most appropriate model for the research. Secondly, there is a part devoted to the concept of prior knowledge, which describes its definition and evaluation metrics. This part is also devoted to analyzing previous experience of the researchers in order to formulate the methodology of the paper.

1.Decision-making process

According to Kaplan, A. M., & Haenlein, M. (2009) Marketing becomes an essential part of any business or organization due to Information Age, where business environment changes rapidly. Hawkins, Mothersbaugh & Best (2007) believe that assumptions and consumers` behavior knowledge are components of all marketing decisions, as knowing the way of consumer` acting in a specific situation allows companies to improve marketing strategies and be more competitive.

Orji at al. (2017) define “consumer behavior”, as a process of studying of society`s needs and motivation in the way of how people choose one product from another.

Sassatelli (2007) claims that consumer behavior can be associated with:

- purchasing activities: purchasing goods or services; how products and services are purchased by customers, as well as all activities related to making a purchase decision, including searching for information, evaluating of goods and services, and methods of payment;

- consumption activities: focusing on who, where, when, and how can distribute consumption of goods and services within families or consumption units;

- disposal activities: refers to the way consumers dispose of products and packaging; might also include resale activities such as eBay and secondary markets.

According to Khan (2006, 4) consumer behavior can be also determined as the decision-making process and operation engaged in acquiring, evaluating , using and disposing of goods and services.

Dumler and Skinner (2007) specify “decision making process”, as “a choice among competing alternatives and the implementation of the chosen alternative”. There are some fundamental models illustrated customer decision-making process and which are presented in Table 1.

Table 1 Models illustrated decision-making process*

*Source: Data for Simon model from Simon H. (1960), Nicosia model from Nicosia F.M. (1966), Engel, Kollat & Blackwell model from Engel J.F., Kollat D.T., and Blackwell R.D. (1968), Theory of buyer behavior from Sheth J. & Howard J.A. (1969), An alternative conceptualization for consumer behaviour and product performance from Narayana C.L. & Markin R.J. (1975), Keeney's fourstage decisionmaking modelfrom Keeney R.L. (1982), Rassuli & Harrell model from Rassuli K.M. & Harrell G.D. (1990) Sheth, Newman & Gross model from Sheth J.N., Newman B.I. & Gross B.L. (1991), Smith & Rupp's model from Smith A. & Rupp W. (2003) The Marketing Spiral from Armano D. (2007), McKinsey's dynamic model of the consumer decision journey from Court D., Elzinga D., Mulder S. & Vetnik O.J. (2009).

Summarizing the information from the table above, most of the models have similar steps that describe the purchase decision making process. For example, problem recognition, input, structuring the decision making problem or need recognition are the step where consumer understand his need and create associations with the brands; active evaluation, variation of alternative actions or information processing, where consumer evaluates and select the best and most suitable product; purchase or process of researching potential purchases, when a consumer makes the final decision and buys a product. Despite the fact that many models are similar in its decision making steps, each model focuses on one of these steps, which describes the decision making process in more detailed way. For example, the Smith & Rupp's model and Rassuli & Harrell model claim that there are external factors that can influence the process of purchase, as feedbacks and such values like emotions, environment.

McKinsey founded the last model, which shows the process of decision-making. It was found that due to the rapid changing of communication from one side, for example, marketer to customer and vice versa, and a two side relationship, for instance, between marketers and customers and vice versa, marketers should have a systematic approach to satisfy consumer needs and use word-of-mouth (Court et al., 2009). McKinsey developed Consumer decision journey, which represents a circular model of decision making process and to find out pivots or touch points. (Figure.1)

Figure 1. McKinsey's dynamic model of the consumer decision journey*

*Based on the source. Reprinted from Court D., Elzinga D., Mulder S. & Vetnik O.J. (2009)

The consumer decision journey consists of four steps:

- Firstly, the customer examines an initial set of brands according to points of contact and associations with the brand;

- Secondly, the customer might evaluate various alternatives actively, where the individual may insert or remove brands on their own;

- Thirdly, the purchase stage assumes that the customer choose a specific brand;

- Fourthly, the consumer uses a product, by which the experience appear, and individual compare acquired knowledge with expectations about the product and notify the next customer journey.

- Lastly, if the post purchase experience is positive then the brand has done its job and has potential for the consumer to enter the loyalty loop.

In research written by Belch G. & Belch M. (2009) the difference between high and low engagement in a process of decision making is discussed. The researches try to find behavior in the way of cognitive orientation. The model has five steps of processes of decision making, where the consumer is consider being a problem solver and information processor. Information processor involved in a mental process in order to evaluate different alternatives and identify an extent, in which information processor might satisfy needs. There are other points of view according to how customers gain experience and knowledge, which they apply in a process of purchase. In accordance with Boyd et al. (2002) different approaches of learning and its impact on ads should be analyzed to understand the way how consumers get knowledge. Consumer knowledge is defined, as a process because of which customers gain post purchase experience, which they may use in future behavior. There are two methods of learning: behavioral and cognitive approach. The authors assume that the difference between the purchase with low and high involvement is that the process of purchase might less or more illustrate the five step model due to risks, which customer may face with. For example, products, which are purchased for the first time, require more involvement, than repeatedly purchased goods. Thus, the more consumers involved in a process of decision making, the less he know about the product that customer want to buy.

Kotler & Keller (2012) go further and analyze model of consumer decision-making process, which includes five stages, and add a new stage - recycling stage. In addition, the authors discuss mitigating effects on consumer decision-making, for instant, consumer engagement.

To sum up, the consumer decision-making path has been changed over a long period of time. During this time, the number of steps of decision-making process increased from three to five, with the help of which the consumer behavior become more detailed. There can be seen external factors, that affect the decision making process. Moreover, the visual part of the decision-making process has changed from linear to spiral shape and then to cyclical shape, where the loyalty loop is occurred. The level of consumer engagement plays a great role in decision making process too, where the engagement is about the way how consumer gets the information and experience and uses it in their purchase decision making process.

Awareness of relationship between online shopping and online consumer`s behavior is essential in order to compete on a rapidly growing and changing market. Many researches do not divide offline and online shopping. By the way, to understand the similarities and differences of offline and online purchase decision-making the comparative analysis (Table 2) should be done based on literature review:

Table 2. Comparative analysis of offline and online purchase decision-making models*

*Source: Information adopted from Lee (2002), Liebermann and Stashevsky (2002), McKnightet al., (2002), Suh and Han (2002), Liang and Lai (2002), Cheung et al. (2003), Constantinides (2002), Cho, N. and Park, S. (2001) Scott Morton, Zettelmeyer, and Silva-Risso (2001), Papamichail & Robertson (2008).

hus, according to the comparative analysis it can be seen that the main difference between online and offline shopping is that purchasing via the Internet add additional elements that influence on online decision making and that cannot be analyzed with the help of traditional marketing tools.

The research, which is written by Senecal et al. (2005), investigate the impact of different online decision making processes on online shopping behavior. Research design is an experiment. Online consumers were requested to complete an online shopping task, where participants should purchase a product by following the recommendations of the online shopping assistant. Also participants complete a questionnaire before and after the experiment in order to make researches to reduce individuals that understand the purpose of the experiment to make data more accurate. Decision making path was analyzed by clickstream, which can be defined, as a path, which is made by the online customer, through different web-sites. Such elements, as time, number of pages visited and web-site information are included in a clickstream. As a result, participants, who did not use recommendations about the product, have more linear decision making path than those who has a consultation about the product they want to purchase.

Fitzsimons and Lehmann (2001) conduct the similar experiment and find that those online customers that ignore product recommendations have a drop in satisfaction at the post purchase stage of decision making process.

Senecal and Nantel (2002) decided to distribute online recommendations into several categories: other consumers (for example, relatives and friends), experts (for instance, sales managers, independent professionals) and special expert aggregators and consumer decision support systems, for example, chat bots or intelligent based systems. Urban, Sultan and Qualls (1999) find out that around 88 per cent of customers who use the recommendation tools agreed that the system allows new alternatives, which was not met by the consumer.

Harris (2009) creates a survey from online consumer behavior in Great Britain. During the third summary questionnaire of online consumer behavior the importance of experience and knowledge of the consumer was found. The questionnaire represents the elements, which determined the focus on online relationship with customers via increasing consumer power through social media experience.

Thus, overall the shopping behavior of consumers who consult but do not follow a product recommendation should be more complex than those who do not consult a product recommendation since the former base their decision on at least one more piece of information, which is the recommendation. In addition, the shopping behavior of consumers who consult but do not follow a product recommendation should be more extensive and complex than those who do consult and follow a product recommendation since there is a mismatch between the recommendation and the preferred alternative, which leads to more deliberation. Previous researches do not study on what stage of decision making process the prior knowledge influence on.

1.1 The concept of Prior Knowledge

In order to identify the connection between prior knowledge and stages of online purchase decision-making process, firstly, there is a need to be provided with the definition of prior knowledge. According to existing literature, the concept of prior knowledge does not have the equal meaning which is widely used in research papers. (Wood and Lynch 2002)

The table 3 provides definitions on the concept of prior knowledge:

Table 3. Definitions on the concept of prior knowledge*

*Source: Wood, & Lynch, 2002; Hong & Sternthal, 2010; Karimi, Papamichaila & Hollanda, 2015; Gardner, 1984; Kanwar, Olson & Sims ,1981; Marks and Olson, 1981.

The table 3 combines key definitions of the concept of prior knowledge, that were implemented in the research papers, which great number of researchers relied on. It could be seen, that the majority of the authors explains the concept of prior knowledge as the level of expertise and the amount of information that consumer has concerning particular product or category. It shows that the amount of knowledge is not an objective variable, that is why there is a need to identify how it could be evaluated.

Consumers' knowledge of the product has a strong influence on purchasing process and on stages of information search. (Brucks 1985). Two primary aspects of the customer choices environment are the following premises: there is an issue with the available product information and that consumers tend to have prior knowledge with the category or product itself. (Bettman and Park 1986). The effect of primary knowledge and previous experience on consumers' choice process has been an area for the research for a long period of time.

Some researchers suggested that there is a negative relationship between the level of consumer's knowledge and the amount of analyzed external data, other papers do not support this result. Malhora in 1983 explained this equivocal result by claiming that consumers which are provided with the high level of prior knowledge tend to have the lack of motivation to search for extra information during the decision-making process while those ones with the lower level of prior knowledge are not capable enough to conduct the research and evaluate existing information. However, this could be explained in a more detailed way through the experimental researches with main aim is to examine the influence of product knowledge on the combination of purchase decision-making variables and also according to the personal decision-making style of the respondents.

Consumers with extensive amount of prior knowledge and more stable with their preferences and have a tendency to begin with the small set of alternatives. (Cowley and Mitchell 2003) Such consumers tend to have a reconvinced solution for the alternative's evaluation criteria. With the high level of knowledge, they behave more confident during the information searching tasks. These people are more capable to assess information, what leads to reducing their time and number of decision-making steps. Consumers with the lower level of prior knowledge are limited in their ability to collect and process relevant information for evaluating alternatives. This leads to wasting more time on cycles pathing. As these consumers are not provided with the suitable options and types of evaluation criteria. That is why they consider a larger set of actions. ( Malhotra 1983)

The authors which were conducted researches concerning the effect of prior knowledge of product on the decision-making process found out that there is a strong influence of the products' expertise on the consumers' purchase behavior. They identified that such factors as previous experience and amount of knowledge make consumers spend less time on making a purchase. They supported this result by the fact, that consumers with the limited level of prior knowledge tend to avoid spending his or her time on evaluating alternatives as they are stable with their preferences. However, no factors were examined which could also affect the purchase decision-making process.

In case of measuring the level of prior knowledge Karimi, Papamichaila & Hollanda in 2015 examined how an online purchase process is affected by the individual decision-making style and the level of respondents' prior knowledge. They classified the level of prior knowledge in two types: high and low. Product's knowledge was identified by the adaption of the Brucks model, who decided to apply two items to evaluate the subjective consumers' knowledge of a product. Two questions were used in order to measure product knowledge asked respondents to rate their knowledge of the exact product and familiarity with product using degree from low to high. This model of subjective consumers' knowledge evaluates the perception of individuals. This type of assessment identifies the amount of search and style of evaluation as it tends to reflect consumer's personal decisions about further actions in decision-making process. (Brucks 1985)

Table 4 The distribution of prior knowledge concerning people's architypes*

*Source: Karimi, Papamichaila & Hollanda 2015.

Combining a decision style with a level of knowledge in one in a specific decision environment, the authors presented four types of user archives, as shown in the table. As a result, four archetypes behave differently with respect to the four processes. Archetype 1 users (satisfactory, with little knowledge) will participate in less intense activities processes related to the number of cycles, duration, number of alternatives considered, and the number of criteria compared to maximizers 3 and 4 as your satisfaction behavior leads them to a more efficient process. Due to its limited ability to collect and assimilate relevant information to evaluate options. They were expected to do more look after this archetype 2. A combination of satisfactory behavior and a high degree Knowledge for users of Archetype 2 (satisfying a high level of knowledge) enhances the tendency of this archetype to efficiency. Its high level of knowledge combined with its successful search strategy naturally limits the number of searches performed. They expected Archetype 2 to be involved in less intense activities for all search efforts, compared to other archetypes. On the other hand, they expected Archetype 3 (Maximizer with little knowledge) to participate in intensive search activities for the entire decision-making process results. Archetype 4 (Maximum Knowledge) is expected to release less search effort than with Archetype 3. Maximum solution style means that they regret and ate afraid to lose sight of good alternatives or not to identify themselves high quality of existing options. Therefore, they expected this archetype to invest emotionally. To make decisions and participate in processes with a longer duration, number of cycles, alternatives and criteria are compared with those found in archetypes 1 and 2. ( Karimi, Papamichaila & Hollanda 2015)

Based on the extensive video analysis, Karimi, Papamichaila & Hollanda captured four decision-process dimensions, such as number of cycles, time, alternatives evaluation and amount if criteria. This was used in order to capture the entire process which occurs during the purchase behavior. Such method of data gaining provided authors with the direct insights into the decision-making process, a clear overview of the interaction flows and allowed to analyze behavioral phases and steps. In order to analyze the received data they applied the business process modeling techniques which are based on UML activity diagrams to evaluate the decision-making process. The result illustrated that the intensity of conducted processes in case of cycles number, consumed time, number of evaluated alternatives and criteria were dependent on both decision-making style and the level of product knowledge.

The literature examines the influence of previous knowledge, as well as everything that is necessary to achieve your goals, so they follow well-studied and procedural solutions (Spence and Brooks, 1997), election campaigns in their information search, and they emphasize that information can be obtained as a result. (Schraagen and Leijenhorst 2001) Insights about these processing proclivities in the literature examining the effects of prior knowledge indicate that consumers with extensive knowledge in a domain exhibit a sense of urgency about achieving their goals (Lewandowski and Kirsner 2000). On the contrary of those, who have limited knowledge of the subject. Mendel and Johnson 2002, and they give the impression that they are more knowledgeable colleagues (Sujan 1985). This characteristic of the preferred methods of information processing, suggesting that all these tasks are based on ample opportunities to achieve their goals.

The authors investigated the effect of prior knowledge on different brands, when information is processed using different methods and presented at different levels of interpretation. When there is a correspondence between prior knowledge and the way information is processed, they also expect positive subjective variability, which will lead to more favorable judgments about the brand. They call this prediction the "hypothesis of knowledge." Due to the sense of urgency of consumers to achieve a goal (Lewandowsky and Kirsner 2000), consumers with wide knowledge should prefer to process information in messages in such a way that, in their opinion, it is easier for them to move towards the goal.

This prediction is consistent with the observation that, compared to those with limited knowledge, consumers with extensive prior knowledge are more likely to make decisions quickly and are more selective in the information that they consider to be the basis for decision-making. (Schraagen and Leijenhorst 2001) This result is to the extent that they sometimes lose useful information or stop learning new information prematurely (Wood and Lynch 2002). Conversely, consumers with limited prior knowledge are likely to prefer an assessment mode that allows them to apply processing strategies that facilitate their comprehensive and complete review of information to compensate for their lack of prior knowledge and receive information. Consistent with this reasoning, consumers with limited knowledge are more likely to compare alternatives than consumers with more knowledge (Mitchell and Dacin 1996). They also exhibit a strong propensity for data processing, which makes it easier to obtain and record all information, including information related to the solution (Alba and Hutchinson 1987; Thunholm 2005). These results show that the evaluation processing mode, characterized by a detailed comparison of alternatives, should coincide with the propensities for processing consumers with limited prior knowledge.

Another researches Wood and Lynch in 2002 examines the role of primary knowledge in the process of receiving new product information. They claimed that prior knowledge is an individual factor. In order to measure its' level among the respondent they did following. Firstly, they tested their hypotheses by comparing consumers with experimentally induced levels of prior knowledge to avoid confounding with correlated constructs of involvement or self-perception of goals. Then they replicated these results when real prior experience is measured, allowing them to tie their findings back to experience-based definitions in the expertise literature (Alba and Hutchinson 1987).

In order to check the influence of PK about allergy medications on the learning of information about a new allergy remedy, they chose a product category about which our respondents would have low prior knowledge and administered a training exercise to the high PK group prior to receipt of new product information. They divided respondents into two groups and provided only one group with the detailed product information, then they made a product change in order to manipulate on the factor of newness. The idea was to test whether this factor affect the more careful processing in group of high PK or not. They chose allergy medications because there is a clear relationship between proper use and efficacy with pharmaceutical products. (Wood and Lynch 2002)

James, Bettman and Park (1986) examines effects of the prior knowledge and experience on phases on decision-making processes in a most common way. They used a coding protocol. In order to evaluate the level of prior knowledge of the respondents they used one-way analysis of variance on the response to the question which identifies the familiarity of a person with the exact category of subjects. They asked to evaluate their prior knowledge and then divided the results into 3 categories.

One of the best ways to follow the respondents'' decision-making process is to track it as it unfolds so that the knowledge of the final outcome does not affect the perception (Langley, Mintzberg, Pitcher, Posada, Saint-Macary 1995). In addition, observation of consumers as they follow the process facilitates the identification of different behavioral patterns. (Ranaweera, McDougall, Bansal 2005) Therefore, a method that allows for the recording of the process, in real time, is quite suitable way to capture the process and identify decision-making patterns.

The existing literature illustrates that the concept of consumers prior knowledge is a latent variable, as the level of consumer's knowledge and expertise could be only measured by dividing the whole concept on several levels. In order to identify the type of consumers' knowledge, there is a need to crate a personal scale and conduct an interview with the respondent. It is better to divide the concept itself on product's prior knowledge and the category's for more stable and reliable results. Prior knowledge of product should be identified as a level of expertise or experience with the particular type of product. However, the prior knowledge of category should be explained as amount of information that respondents operates concerning the specific field, category of products or services.

2.Methodology

The empirical aim of the research paper is to find the impact of prior knowledge on online decision-making process, find out at what stage customer journey the prior knowledge influence a lot and understand are there any difference of decision making path due to industries. The research question of the paper is “How do prior knowledge influence on the online purchase decision making process?”. The paper is based on participant observations with the use of the additional strategy, as interviewing, to make the data more validity. The data is analyzed via qualitative and quantitative approach. The object of observation is a person who books a table in restaurant and purchase pills for stomachache pain online. The subject of observation is an online purchase decision making path. The interviewing allows determining the participants` level of prior knowledge. The observations with interviewing were carried out from the middle of April to the middle of May in 2020.