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Computer Science
Medicine

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A Proposed Methodology for a Risk Assessment-Based Liposome Development Process

The requirements of a liposomal formulation vary depending on the pharmaceutical indication, the target patient population, and the corresponding route of administration. Different preparation methods require various material attributes (MAs) (properties and characteristics of the components) and process parameters (PPs) (settings of the preparation method). The identification of the quality target product profile for a liposome-based formulation, the critical quality attributes of the liposomes, and the possible MAs and PPs that may influence the key characteristics of the vesicles facilitates pharmaceutical research. Researchers can systematise their knowledge by using the quality by design (QbD) approach. The potential factors that influence the quality of the product can be collected and studied through a risk assessment process. In this paper, the requirements of a liposome formulation prepared via the thin-film hydration preparation technique are presented; furthermore, the possible factors that have an impact on the quality of the final product and have to be considered and specified during the development of a liposomal formulation are herein identified and collected. The understanding and the application of these elements of QbD in the pharmaceutical developments help to influence the quality, the achievements, and the success of the formulated product.

Introduction
Liposomes are described as artificially prepared vesicles composed of one or more concentric lipid bilayers that are enclosing one or more aqueous compartments by the European Medicine Agency [1]. Liposomes as drug carrier systems have several advantages [2]. These formulations can be used, among others, to protect active pharmaceutical agents (API), incorporate both lipophilic and hydrophilic drug molecules, and maintain targeted drug delivery [3]. From the beginning until the present day, four different generations of liposomes have been distinguished. The first-generation liposomes (conventional liposomes) are made up of neutral and/or negatively charged phospholipids and cholesterol [4]. These vesicles are taken up by the reticuloendothelial system (RES) (phagocytes) in cases of intravenous administration; thus, their circulation time is short [5]. The second generation consists of long-circulating liposomes, while the third generation is made from surface-modified liposomes that can avoid the defence mechanism of the immune system. The fourth generation is built up from polyethylene glycol (PEG)ylated or the so-called "stealth" liposomes [3,4]. The surface of these vesicles is coated with a hydrophilic polymer, such as polyethylene glycol (PEG), that increases the repulsive forces between the liposomes and thus avoids the protein adsorption and opsonisation associated with the drug substance, the excipients, the intermediates (in-process materials), and the drug product [33]. A CPP is a process parameter that variability has an impact on the CQAs and therefore should be monitored or controlled to ensure that the process produces the targeted quality [33].
(3) Risk assessment (RA): RA is a valuable science-based process that is used to identify and rank the parameters on the basis of their impact on the CQAs of the product. Risk assessment is typically performed as the first step during an early phase of the pharmaceutical development processes and is evaluated again when more information becomes available and higher knowledge is obtained [32,33]. The current experimental knowledge obtained from the former practical studies have to be aligned with information from the relevant literature. To perform a successful RA, first, the research team has to define the precise target product (QTPP) and then has to select the critical factors and estimate the interdependence of the critical factors, ranking them by the severity of their impact. The team members estimate the level of the interactions between the parameters occurring during the formulation process (production settings, materials, etc.). All the elements applied in the RA (QTPP elements, CQAs, CMAs, and CPPs) are defined and selected by the research group; therefore, their knowledge strongly impacts this selection process. Risk is defined as the combination of the probability of the occurrence of harm and the severity of that damage. The RA is a systematic process to evaluate the necessary information for the support of the risk-defining step within the risk management process. It means the identification of hazards and the analysis of risks [31]. The quality risk management tools provide systemic and reproducible methods based on up-to-date knowledge to rate the probability, severity, and sometimes detectability of the risk. These methods can be qualitative or quantitative. Once the risk is expressed quantitatively, a numerical scale is assigned for evaluation [33]. The numeric score of the evaluated risks could arise from the multiplications of the severity and occurrence (or probability) values, or sometimes from the severity, occurrence, and detectability if the same scale was used for the estimation of all of these parameters. The RA software can help in this process, but even during the software-supported assessments, the identification of the risks and the estimations of the severity and the occurrence are the task and responsibility of the research group. The software only makes the calculations and provides the data assessment and visualisation of the final results. These results are the basis of the design of experiments (DoE).
(4) Design space (DS) development: DS is a multidimensional combination and interaction of the input variables (e.g., material attributes) and the process parameters that have been demonstrated to assure quality.
(5) Definition of the control strategy. (6) Life cycle management. For better understanding, the schematic structure of the QbD approach is presented in Figure 1. This paper aimed to collect and evaluate the parameters that influence the manufacturing process of a liposomal pharmaceutical product in order to help the researchers and the professionals in the pharmaceutical industry in the QbD-based new liposome design and development. The authors aim to present a wide range of potential QTPP and CQA elements and their characteristics to highlight the potential decision and target points. It was also intended to give an example of how to use RA to rank the influencing parameters. For this illustration, the thin-film hydration method [35], the most common liposome production process (Table 1), was chosen, as the authors have practical experience and knowledge about this technique from their previous studies [27]. This method was described for the first time and used to prepare the first liposomes by Alec Douglas Bangham and his colleagues in 1965 [35]. Several modified versions of the original technique exist (Table 1), however, the basic steps of the process are mutual [36]: (1) preparation of the lipid film from phospholipids and cholesterol, (2) hydration of the thin film with a hydration medium, and (3) modification of the numbers of layers and the size of vesicles. This paper aimed to collect and evaluate the parameters that influence the manufacturing process of a liposomal pharmaceutical product in order to help the researchers and the professionals in the pharmaceutical industry in the QbD-based new liposome design and development. The authors aim to present a wide range of potential QTPP and CQA elements and their characteristics to highlight the potential decision and target points. It was also intended to give an example of how to use RA to rank the influencing parameters. For this illustration, the thin-film hydration method [35], the most common liposome production process (Table 1), was chosen, as the authors have practical experience and knowledge about this technique from their previous studies [27]. This method was described for the first time and used to prepare the first liposomes by Alec Douglas Bangham and his colleagues in 1965 [35]. Several modified versions of the original technique exist (Table 1), however, the basic steps of the process are mutual [36]: (1) preparation of the lipid film from phospholipids and cholesterol, (2) hydration of the thin film with a hydration medium, and (3) modification of the numbers of layers and the size of vesicles.

Preparation Methods Subtypes Comments
Mechanical dispersion methods probe or bath sonication − the critical parameters vary on the basis of the selected preparation method; therefore, the definition of the production technique has to be the first step of every liposome formulation process − the properties of the liposomes (e.g., number of lamellas, size, and distribution of vesicles) French pressure cells-extrusion freeze-thawed liposomes membrane extrusion lipid film hydration techniques hydration of proliposomes micro emulsification, coalescence of small vesicles dual asymmetric centrifugation heating method, Mozafari method electro-formation Solvent dispersion methods ether injection ethanol injection

Methods
The LeanQbD software (QbD Works LLC, Fremont, CA, USA) was used for the RA procedure. The first element of this procedure was the interdependence rating between the QTPPs and the CQAs, and the CQAs and the CPPs. A three-level scale was used to describe the relation between the parameters: "high" (H), "medium" (M), or "low" (L). In the software, the qualitative three-level scale, used for the estimation, is linked to a selectable numeric scale (0-10, or 0-100), which gives, at the end, the severity scores of the evaluated risk factors on the basis of mathematical calculations. In this study, the 0-10 scale was used. After the categorisation of the interdependence, a risk occurrence rating of the CPPs (or probability rating step) was made, applying the same three-grade scale (H/M/L) for the analysis. As the output of the initial RA evaluation, Pareto diagrams [37] were generated by the software, presenting the numeric data and the ranking of the CQAs and the CPPs according to their potential impact on the aimed final product (QTPP). The Pareto charts not only show the differences of the CMAs and the CPPs by their effect but also help to select the factors of a potential experimental design. Table 2 summarises the potential QTPP elements collected by the authors. Potential CQAs are collected and presented in Table 3.  As the preparation method (Table 1) defines the CPPs of the liposome formulation process, a production technique that provides the target CQAs need to be selected prior to the investigation of CMAs and CPPs. The API can be added to the formulation via passive or active loading techniques [3]. Mechanical dispersion [3,19,38,39], solvent dispersion [3,38,39], and detergent removal [3,38,39] methods belong to the passive loading techniques, in which methods the lipid films are prepared via Pharmaceutics 2020, 12, 1164 7 of 13 different techniques, hydrated to obtain liposomes, and the drug is captured during the manufacturing process [3,39]. In case of active loading, the API is incorporated into the already prepared liposomes via gradient loading techniques using buffers or ammonium sulphate gradients [39]. Besides the conventional preparation methods, there are also numerous approaches that have been recently developed to produce liposomes [39,40]. In this paper, the thin-film hydration method-related factors are presented. The potential CMAs and CPPs of the technique are systemised in a flow chart in Figure 2. The steps of the thin-film hydration method [36] are shown in the middle of the figure, while the related material attributes (MAs) and process parameters (PPs) are presented on the two sides of the chart. The general criticality of the presented factors was investigated in a RA, and the rankings of the elements of CQAs, illustrated with Pareto charts for better understanding, are shown in Figure 3, while CMAs and CPPs are shown in Figure 4. Figure 2. Collection of the properties of the liposome components (material attributes (MAs)) and the preparation method (process parameters (PPs)) that affect the result of the thin-film hydration liposome manufacturing technique.

Results
The general criticality of the presented factors was investigated in a RA, and the rankings of the elements of CQAs, illustrated with Pareto charts for better understanding, are shown in Figure 3, while CMAs and CPPs are shown in Figure 4.   The general criticality of the presented factors was investigated in a RA, and the rankings of the elements of CQAs, illustrated with Pareto charts for better understanding, are shown in Figure 3, while CMAs and CPPs are shown in Figure 4.

Discussion
The QTPP (Table 2) depends mainly on the therapeutic/clinical aims and requirements, as well as the characteristics of the drug substance, and it is always unique. For instance, QTPP may be a nano-sized liposome-containing injection for cancer therapy with a proper dose of drug and drug Pharmaceutics 2020, 12, 1164 9 of 13 release dedicated to the therapeutic needs. Those quality attributes that are critically related to the QTPP are the CQAs. That is the reason why the CQAs are also always unique and depend on the QTPP. The potential CQAs (Table 3) are, e.g., the type of the liposome, its lamellar structure, vesicle size, size distribution, sterility, viscosity, and stability, or the dissolution profile of the formulation. The API encapsulation efficiency is also a critical attribute for the liposomes, in addition to the zeta potential, which refers to the stability of the vesicles. PdI is one further potential CQA for lipid-based nanocarrier systems such as liposomes.
The application of a quality management visualisation tool, such as a fishbone diagram, process mapping, or a flow chart, is always useful for the identification of the CMAs and the CPPs of the aimed liposomal product. In this case, to show the systemic collection and presentation of the potential CMAs and CPPs, we built a flow chart (Figure 2). In the middle of the figure, the steps of the production process, which in this case was the thin-film hydration liposome preparation method, are presented. The left side of the flow chart contains the material attributes (MAs), and the right side shows the process parameters (PPs). These MAs and PPs can affect the result of the thin-film hydration-based liposome manufacturing process. The critical ones have to be selected and labelled as CMAs and CPPs. To make this figure and the tables of QTPP and CQAs, prior knowledge, previous experimental experience, and a thorough literature background survey of the field [31,[41][42][43][44][45][46][47][48] were necessary. Although, the main points of the tables and figures are shreds of evidence from the literature mixed with practical experiences, the systemic collection of all the relevant factors and data in one paper is the novelty of the work. The demonstration of the CMAs and the CPPs parallelly enhances the transparency of their relationships. In the following step, RA can be performed among the elements of the QTPP, the CQAs, and the CMAs and the CPPs. Several tools are suitable for an RA, e.g., the support of an RA software can help to achieve proper and quick implementation. In the presented case, the LeanQbD (QbD Works LLC, Fremont, CA, USA) RA software was applied. The interdependence rating among the elements was made on a three-grade scale, as the interaction is low (L), medium (M), or high (H). This process was made step by step for each pair of factors on the basis of the prior experimental and literary knowledge. The results of the RA are presented in Pareto charts generated by the software (Figures 3 and 4). Figure 3 shows the theoretical ranking of the CQAs of the liposomes according to the initial general RA made by the authors. It may also vary in other cases on the basis of the QTPP. Figure 4 presents the general ranking of the CMAs and the CPPs depending on their severity for the liposomal product. It may vary on the basis of the QTPP and the CQAs. According to the RA, the most influential CMAs, organised in descending order, are the phospholipids, the API content [27], the surface modifiers, the cholesterol content, the ratio between the phospholipids and the cholesterol, the phase transition temperature of the lipids, and the quality of the hydration media and the cryoprotectant, while the CPPs are the working temperature, the duration of the sonication, and the number of filtrations. The effect of the CMAs/CPPs can be accurately investigated if some of the values are set on the same level, while the ones under the scope of the study are changed according to the DoE.
Xu et al. performed a risk analysis study on liposomes prepared using the thin-film hydration technique and loaded with superoxide dismutase via a freeze-thaw cycling technique. They analysed those factors that affect the size, the encapsulation efficiency, and the stability of the liposomes. For this evaluation, they checked the properties of the formulation, the process, the analytical method, and the instrumentation reliability. They found that the "analytical method" and the "instrument reliability" categories can be well-controlled; therefore, the factors of these two categories are not critical. However, the factors of the "analytical method" and the "instrument reliability" are non-negligible for the selection and settings of the characterisation methods. Their findings, namely, the influencing role of the lipid concentration, the cholesterol ratio, and the quality of the phospholipids are consistent with our results [49]. Porfire et al. provided a general overview of the QbD approach for liposomes without defining a production process and described methodologies for liposome characterisation as a control strategy in detail. Their reasonable considerations were built into the tables of this paper with our additions. The facts above draw attention to the low number of studies following the steps of the QbD recommended by the regulatory authorities [31]. Our presented work fits well into this scientific research area; it extends the previous knowledge and gives a detailed overview of the QbD application. The systemised and structured form of the facts and information may help researchers in designing and planning their future studies of liposomes.

Conclusions
This work aimed to collect and systemise all the relevant factors of the liposome formulation development via the QbD technique. The application of the QbD approach is a regulatory requirement in the pharmaceutical submissions, and in these applications, RA is the key step. In this study, the theoretical method was presented, the potential QTPP elements of the liposome-based formulations were determined, and the potential CQAs of the liposomes were also collected. The potential critical material attributes and process parameters that need to be considered during the formulation design of the thin-film hydration liposome preparation method were listed and evaluated. The method of screening was also presented to identify the most critical factors. The phospholipids, the API content, the surface modifiers, the cholesterol content, the ratio between the phospholipids and the cholesterol, the phase transition temperature of the lipophilic phase, and the quality of the hydration media and the cryoprotectant were found to be the CMAs of highest influence. Furthermore, the working temperature, the duration of the sonication, and the number of filtrations were identified to be essential CPPs. The authors believe that the presented concept may help researchers to establish and perform studies on liposomes with less effort and more success.

Conflicts of Interest:
The authors declare no conflict of interest.


The Effect of Boredom Proneness on Smartphone Addiction and Impulse Purchasing: A Field Study with Young Consumers in Turkey

The study seeks to understand the effects of boredom proneness on impulse purchasing and smartphone addiction of young consumers. Moreover, the possible mediating role of smartphone addiction is tested for the effect of boredom proneness on impulse purchasing. Nowadays, the effect of emotions on human behavior is generally accepted, and boredom is one of the important and common problematic feelings or moods at various levels of life due to factors like unemployment, not being able to work in a suitable job, not getting appropriate education matching individual abilities, monotony of tasks, and feeling life is meaningless. Investigating the effect of boredom on specific consumer behavior would increase our knowledge about consumer behavior. For the research, a survey was conducted 313 students from Kirikkale University, Keskin Vocational High School; the data were collected by convenience sampling method. The data were processed through statistical tools like exploratory factor analysis, coefficient alphas, and regression analysis. The results of the study reveal that boredom proneness affects impulse purchasing and smartphone addiction. In addition, it is understood that smartphone addiction plays a mediating role in the effect of boredom proneness on impulse purchasing. These results indicate that boredom can be an important factor affecting certain negative consumer behaviors.

Introduction
Today, emotions encountered in life can affect human attitudes and behaviors (Weiss & Cropanzano, 1996, pp. 10-12) and consumers' behavior is affected by individual characteristics (Kim & Yang, 2020), contextual factors (Phuong & Dat, 2017), and especially by emotions (Yağcı & Çabuk, 2018, pp. 419-455) are both accepted notions. Boredom, which is especially common in people, means a lack of motivation, low physiological arousal (Biolcati et al., 2018), and an insufficient attention capacity. It also causes social, professional and personal problems that must be overcome (Martin et al., 2012). In this research, it is suggested that boredom may be related to smartphone addiction and impulse consumption, which are among common problems of today.
Although attempts to reduce boredom through consumption behaviors are evident, research on the relationship between boredom and certain consumer behaviors in marketing science are limited. Within the scope of this research and testing of the theory with primary data, whether impulse purchasing and smartphone addiction are the main consumer behaviors associated with boredom is explored.

The Concept of Boredom
Boredom is explained as an unpleasant and temporary mood in which an individual feels indifferent and has difficulty focusing on a task. Boredom may arise from several situations such as over-focusing on oneself, being concerned about individual rights such as the right to be happy, not finding anything to do, not receiving any external stimuli, not being able to connect with stimulants (knowing the lesson already or finding it meaningless, etc.), experiencing repetitive tasks and a lack of variety, etc. In addition, mood, fatigue, previous experiences and personal interests also affect boredom in a particular situation. Behaviors such as stretching, bending fingers, imagining and scribbling appear as clues to boredom (Conrad, 1997).
Boredom can be expressed as a disturbing and dissatisfying feeling resulting from repetitive, monotonous and prolonged behavior. In particular, there are social processes, activities or situations that causes an individual to experience boredom. This feeling indicates that the individual's current activity or situation is not appealing, is meaningless, and some interesting or meaningful things need to be undertaken. Avoiding boredom is related to social behaviors such as risk-taking and conflict between groups. Therefore, boredom interacts with behavior (Barbalet, 1999). Definitions of boredom emphasized that boredom is related to time, that time loses its value according to the activities performed, and that there is a conscious mental state in the form of being aware of the lack of interest in participating in an activity (Greenson, 1953).
Measurement tools have been developed by scientists to determine boredom in a valid and reliable way. Among these, the Boredom Proneness Scale developed by Farmer and Sundberg (1986) and Short Form of Boredom Proneness Scale developed by Vodanovich et al. (2005) are used in many studies. According to them, boredom proneness consists of insufficient internal and external arousal. While the internal factor is the individual's low ability to identify and process his own feelings and thoughts (Harris, 2000), the external factor is the failure of the individual to satisfy the need for excitement, change and challenge, or their attempts in this direction does not generate satisfaction (Vodanovich & Kass, 1990). Research has shown that boredom is higher in women in terms of lack of internal stimulants and less in terms of lack of external stimuli (Seib & Vodanovich, 1998;Studak & Workman, 2004). Struk et al. (2017), created an 8-item internally consistent and structurally valid short form of scale by adjusting the style of expression and ensuring consistency between items due to idea that reverse questions in survey forms may cause misinterpretations. Accordingly, boredom consists of components such as finding oneself often in incomplete tasks, difficulty in being entertained, evaluating work as monotonous, not feeling motivated at work, thinking that they should be encouraged to take action and just sitting around not doing anything.

Boredom and Consumption
There has been little research about boredom in the field of consumer behavior, and the relationship between boredom and consumption has mainly been examined as an emotion caused by a particular product or service. For example, while consumers receive services (banking, health, restaurant services, etc.), waiting in line is an inevitable situation (Conrad, 1997). Another example is that nurses often hear patients' complaints about boredom. The boredom experience of psychiatric patients causes the therapeutic potential of the hospital environment to decrease. Therefore, it is recommended that healthcare professionals and managers change the traditional environment of the hospital in a way to eliminate boredom (Binnema, 2004).
In addition to these issues, some of the products with which consumers experience boredom while using are communication tools (such as Facebook), and food and fashion products. Yazdanparast et al. (2015) examined the phenomenon of Facebook boredom among undergraduate students. It concluded that this boredom negatively affected the attitudes towards the social networking site and advertisements on this channel.
Another group of products to which boredom is related is food. Food boredom, which expresses the negative change in the level of liking, causes a decrease in the consumer's interest in the product. As a result, boredom may cause a consumer to pay more for the products they may have eaten long ago, and food variety is recommended in diet programs (Moskowitz, 2000). The feeling of boredom has also been examined in terms of clothing and fashion products. Kwon and Choo (2014) examined the effects of boredom on consumer behaviors such as disposing of products depending upon conditions such as the products becoming obsolescent, not fitting, and changing fashions. Accordingly, variables such as wardrobe management, changes in size, trend, preferences, age, status, design, and new acquisitions encourage the disposal of clothing products. Besides, the relevant research has indicated that clothing products are disposed of because consumers are not seeing them as beautiful, not enjoying them, and getting tired of their design.
One of the goals of marketers is to ensure that consumers do not experience discomfort in the consumption and purchasing of goods and services. Related to this, enjoyable store environments and product diversity that reduces consumers' boredom may influence consumer preferences when choosing among options. Besides this, it is possible to shape consumption behaviors depending on the general boredom that people experience in their lives.
People can see consumption as a method to get rid of the feeling of boredom. Due to the efforts expended to avoid negative emotions, consumption may be beneficial in order to get better conditions or avoid the causes of this situation. In this context, research has shown that consumers who are prone to boredom have more shopping wishes, search for change or diversity, shop to change their moods (Mano, 1999), and buy more fashion products online (Park, 2015). This study examined the possible effects of boredom proneness on impulse purchasing and smartphone addiction.

Hypothesis Development
Impulse-based consumer purchases are those that have not been planned completely or partially before entering a store (Berman & Evans, 2007, pp. 215-216). Impulse purchases are unplanned, occur suddenly, are desire-driven, impulsive and the result of an internal psychological condition to purchase a product, and are characterized by little attention being paid to the results of the purchase (Piron, 1991;, Rook & Fisher, 1995Rook & Gardner, 1993;Rook & Hoch, 1985).
A great deal of research has been conducted on impulse purchasing behavior. It is evident that impulse purchasing is effected by mood  as well as many other factors like low price, disposable income (Berman & Evans, 2007, pp. 215-216), self-perception, suppression of emotions, delay of instant satisfaction (Kacen & Lee, 2002, p. 163), shop environment or atmosphere, salespeople, product features (product category, product variety, etc.), promotion activities, age, low perceived risk, high materialism, individualistic culture, general impulsivity (Ünsalan, 2016), and social norms (Rook & Fisher, 1995).
Impulse consumption can especially be affected by strong emotions (Aruna & Santhi, 2015;Baun & Groeppel-Klein, 2003). For example, when people are in a positive mood (experiencing fun, etc.), they can be more generous and rewarding toward themselves (Beatty & Ferrell, 1998;Dittmar et al., 1996;Gardner & Rook, 1988). On the other hand, although the effects of negative emotions on consumers are not as clear as positive emotions, it is possible to think that impulse consumption can be engaged in to reduce negative emotions, such as depression, or to improve mood (Glen & DeMoss, 1990). For example, when people are stressed or get depressed, they go to the kitchen more often and buy products such as clothing and cosmetics more frequently. In brief, it is understood that consumption behaviors can be engaged in for therapeutic purposes (Luomala, 2002) or to manage negative moods (Luomala, 1998).
Research also exists in the marketing literature that supports the idea that boredom can directly affect impulse purchases (Dittmar & Drury, 2000;Gasiorowska, 2011;Geuens et al., 2004). In the in-depth interviews by Sundström et al. (2019), it was found that when young consumers were bored, they reacted more to marketing stimuli (price, easy access, free delivery, etc.) to break the monotony. In addition, in the experimental research by Moynihan et al. (2017), a relationship between boredom and impulsiveness was revealed. Based on the research findings and related discussions, the first hypothesis is as follows: H1: Boredom proneness affects impulse purchasing.
Technological improvements may lead new behavioral problems like social media/network (Choi, 2018;Choi et al. 2019) and smartphone addiction, which affect daily life negatively. In the use of smart phones, which is common today and consumes a significant part of the day, people experience problems related to control. As in other countries, research has shown that smartphone addiction among young people is quite common in Turkey. Accordingly, people feel deprived when they are not using their smartphones, have difficulty controlling their smartphone use, and have difficulties in their lives due to their smartphone use (Fidan, 2016;Kuyucu, 2017;Noyan et al., 2015).
Research has also shown that negative emotions like depression, anxiety (Kardefelt-Winther, 2014;Long et al., 2016) and loneliness (Aktaş & Yılmaz, 2017;Mert & Özdemir, 2018) are associated with increased problematic internet and smartphone use. In addition, besides the studies that have shown that boredom affects young people's addiction behaviors such as smoking, excessive alcohol use and gambling (Biolcati et al., 2016;Hunter & Csikszentmihalyi, 2003;Mercer & Eastwood, 2010;Ziervogel et al., 1997), there are studies that have shown that boredom affects problematic or excessive Internet use (Biolcati et al., 2018;Lin et al., 2009;Wegmann et al., 2018) and problematic smartphone usage (Elhai et al., 2018;Elhai et al., 2019;Matic et al., 2015;Wolniewicz et al., 2019). Therefore, the idea that boredom affects smartphone addiction is put forward within the scope of this research and the following hypothesis is formulated: H2: Boredom proneness affects smartphone addiction.
Additionally, this research questions whether smartphone addiction plays a mediating role in the effect of boredom on impulse consumption. It is possible that because smartphones consume a significant part of the day and mental resources, smartphone addiction is in conflict with the ability to realize impulse consumption, and it can decrease impulse

Factor and Reliability Analysis
Due to the fact that the research measurement tool is largely adapted, exploratory factor analysis was performed for structural validity of the survey instrument. In addition, coefficient alphas were calculated to determine the reliability of the item groups prepared for the purpose of measuring the research variables. According to the reliability analysis conducted for the statements prepared to determine the boredom proneness, the coefficient alpha was found to be 0.70 and sufficient. According to the exploratory factor analysis performed for this variable, the KMO coefficient was determined to be 0.704, and it was understood that the data were suitable for factor analysis. Five items with factor loads higher than 0.5 were included in the analysis and these explained 42.75% of the total variance (see Table 2).
According to the factor analysis conducted for the items prepared to measure impulse purchasing, it was determined that the KMO coefficient was 0.740, and the related 4 items explained 61.946% of the total variance. The reliability value of the question group was calculated as 0.740 (see Table 3).
According to the factor analysis conducted for the items prepared to determine smartphone addiction, the KMO coefficient was determined as 0.829. The prepared items were allocated to two factors explaining 55.502% of the total variance, and the factors were named "deprivation/ dominance" and "problematic use/results" respectively according to the purposes for the preparation of the questions. The reliability coefficients of the question groups that made up the addiction dimensions were determined to be sufficient (see Table 4).
consumption. However, there is no scientific evidence regarding this possible effect. Accordingly, the third hypothesis of the research is: H3: Smartphone addiction plays a mediating role in the effect of boredom proneness on impulse consumption.

Research Methods and Materials
The primary goal of this research is to test the possible effects of boredom on impulse purchasing and smartphone addiction on a sample in Turkey. In addition, whether smartphone addiction plays a mediating role in the effect of boredom proneness on internal consumption is also examined.
In the research, a survey was conducted with 313 students of Kirikkale University Keskin Vocational High School; the data were collected by convenience sampling method between the dates of 16.12.2019 and 27.12.2019. In the development of the research measurement tool, previouslyconducted scientific research was employed to measure the variables -to measure boredom proneness, Struk et al.'s (2017) study was used; to measure impulse purchasing, Sneath et al.'s (2019) and Rook & Hoch's (1985) studies were used; and to measure smartphone addiction, Lopez-Fernandez's (2017) and Kwon et al.'s (2013) studies were employed. The items for boredom proneness have been translated into Turkish, and the items for impulse purchasing and smartphone addiction have been adapted from the aforementioned studies.

General Features of the Participants
Three hundred and thirteen vocational high school students participated in the research. While 67.1% of the 313 participants were 20 year-old and older, 32.6% were under 20. In terms of gender, 86.9% of the participants were women. In general, the participants consisted of students, young people and people who have similar characteristics and are without a regular income (see Table 1).

Testing the Hypotheses
Multiple regression analyses were performed to test the research hypotheses. First, according to the model it was found that boredom proneness affects impulse consumption (standard β= 0,258) significantly; H1, "Boredom proneness affects impulse purchasing", was thus supported (see Table 5).
Finally, regression analysis was carried out to test the third hypothesis. Boredom proneness and smartphone addiction dimensions were included in the same model as variables likely to affect impulse purchasing. In order to conclude that there is a mediating role besides the independent variable affecting the dependent variable and the mediating variable, the effect of the independent variable on the dependent variable should disappear or decrease when the independent variable and the mediating variable are combined (Baron & Kenny, 1986, p. 1176. According to the analysis carried out in this framework, when smartphone addiction occurred, the effect of boredom proneness on impulse purchasing disappears (Standard β decreased from 0,258 to 0,091). So, a mediating role of smartphone addiction was evidenced. Hence, H3, "Smartphone addiction plays a mediating role in the effect of boredom proneness on impulse purchasing", was supported (see Table 7).

Results and Discussion
As a result of the research, the conclusion that boredom proneness affects impulse consumption is similar to the results of studies in the marketing literature that show that consumers become involved in impulse-related consumption behaviors (snacking, eating, drinking, having fun, etc.) in order to correct negative emotions and moods (Abramson & Stinson, 1977;Biolcati et al., 2018;Crockett et al., 2015;Galloway, 2002;Koball et al., 2012;Luomala, 1998Luomala, , 2002Glen & DeMoss, 1990;Mitchell et al., 2007;Moynihan et al., 2015;Sit et al., 2006;Sundström et al., 2019;Walfish & Brown, 2009;Virvilaitė et al., 2011). In addition, this finding is similar to the results of research that concludes that boredom affects impulse purchase (Dittmar & Drury, 2000;Gasiorowska, 2011;Geuens et al., 2004). Therefore, it is understood that boredom proneness affects the impulse purchasing of the young consumers of our sample in the Kirikkale province of Turkey.
Moreover, the conclusion that boredom proneness affects smartphone addiction is also similar to that of studies in the related literature, which show that boredom is connected to problematic Internet use (Biolcati et al., 2018;Lin et al., 2009;Wegmann et al., 2018) and problematic smartphone use (Elhai et al., 2018;Elhai et al., 2019;Matic et al., 2015;Wolniewicz. 2019). Therefore, the idea that boredom proneness is a variable that affects smartphone addiction among young people is supported in this study.
In addition, when smartphone addiction and boredom proneness are combined in the same model, the mediating effect of smartphone addiction with respect to the effect of boredom proneness on impulse purchasing is the original result of this research. Therefore, it can be asserted that when boredom proneness is high, impulse consumption is lower due to a high smartphone addiction level. This shows that the use of smartphones at a higher problematic level causes a variation in the importance of factors affecting impulse purchasing. In other words, smartphone addiction may be thought of as a substitute for impulse consumption in terms of reducing the effect of boredom.

Conclusions
In this research dealing with the effects of boredom on a specific consumer behavior, it is evident that boredom proneness is related to an increase in smartphone addiction and impulse purchasing. This result supports the general idea that boredom, as a negative emotion or mood, may be a major contributor to certain consumer behaviors. In addition, the fact that smartphone addiction reduces the effect of boredom on impulse purchasing is another result of this research.
The results demonstrate the need to manage boredom, which is one of the most important problems facing young people today as it is one of the triggering variables for smartphone addiction. Therefore, it can be suggested that young people should be educated in areas appropriate to their abilities and interests, so that they can comprehend the meaning of the behaviors or tasks in which they are engaged and be focused on and motivated in their activities in order to reduce their problematic use of smartphones and increase their ability to make more conscious and fulfilling purchases. It is possible for parents and educational institutions, managers and employees to take precautions in reducing boredom.
As with any research, this research has its limitations. Conducting the research via a survey method necessitates that the participants' answers to the items in the survey be considered truthful. In addition, the realization of the research using vocational high school student participants, a young and narrow customer group, restricts the generalization of the research results. However, it is thought that the research findings are useful in order to test the claims put forth in this research on a specific group of customers. Conducting similar research with a larger sample will contribute to our understanding of the subject.