Editorial Board

A Refereed Monthly International Journal of Management

The Mediating Role of Purchasing Duration in the Effect of Social Media Dependency on Consumer Purchasing Behavior: Comparison of India and Turkey

 

Yashwant Singh Rawal

Professor,

Amity School of Hospitality,

 Amity University, Rajasthan

yashwantr84@gmail.com,

https://orcid.org/0000-0001-5367-7443

 

Miraç Yücel Başer

Hasan Kalyoncu University,

Economics, Business Administration,

Gaziantep, Turkey,

mrcbaser201711@outlook.com,

https://orcid.org/0000-0002-9394-8815

 

Debasish Batabyal

Amity Institute of Travel and Tourism,

Amity University, Kolkata, India,

debasishbatabyal@gmail.com,

https://orcid.org/0000-0003-0191-9804

 

Yakup Durmaz

Hasan Kalyoncu University,

Economics, Administrative and Social Sciences,

Business Administration, Gaziantep, Turkey,

yakup.durmaz@hku.edu.tr ,

https://orcid.org/0000-0003-0332-4185

Corresponding author

 

Abstract

This study aims to investigate the mediating role of purchase duration in the effect of social media use on online consumer purchasing behavior. As social media has been gaining increasing importance worldwide, becoming, it has also been changing shopping habits in almost all countries. With so many consumers spending more time online than ever before, it is important to understand how long it takes them to make a purchase and which factors are involved in their decision-making process. More specifically, this study examines the relationship between social media dependency and consumer purchasing behavior, focusing on the mediating effect of purchase duration. A study involving 329 individuals from India and 204 from Turkey was conducted, and the data was analyzed using PLS structural equation modeling. The results indicated that social media dependency positively influences consumer purchasing behavior. Also, the study exhibits that purchase duration mediates the relationship between social media dependency and consumer purchasing behavior.

Keywords: Consumer Purchasing Behaviour (COPB), Purchase decision, Social Media Dependency, SEM-PLS, Mediating role

 

Introduction

In today’s world, understanding consumer intention and behaviour, maintaining the closest relationships with them, and how they behave on social media, are gaining more importance than ever before (Akar and Dalgic, 2018; Kunja and Gvrk, 2020) before buying items or services, shoppers seek out and gather the right data and then compare the seller using this data to make the best purchase decision. Search effort is an indispensable part of search behavior and is more complicated for service products. Consumers in third world countries are increasingly using the internet to search for product and service information, while first world countries are seeking its effective and efficient uses and implications. Though digital marketing literature addresses customer creation  (Noor et al., 2022), customer retention (Faisal and Hamdan,2021),  digital transformation (Bist et al., 2022), AI-mediated communication (Goldenthal et al., 2021), measurement of purchase intention and behaviour (Alwan and Alshurideh, 2022) with all their moderating and mediating effects, yet assessing the mediating role of purchase duration in the effect of social media dependency (SOMD) on consumer buying behavior hasn’t got attention. People are increasingly connected to the digital landscape and expect to spend more time on the internet. Offline buyers are also surfing information before purchasing. Therefore, understanding purchase duration with online and semi-online implications with related behaviour of consumers is not only essential but imperative as well. Also, a comparison of Internet marketing techniques specific to various businesses is recommended by many scholars (Bala and Verma, 2018;) with various moderating and mediating issues.

Increasing social media dependency (SOMD) in today’s scenario is more prevalent with a wider dimension of various stakeholders viz. manufacturers, suppliers, wholes sellers, distributors and finally the consumers. Present social media is pervasive and ubiquitous even omnipotent and has extended the ecosystem with its bidirectional trends in an increasingly “omni-social” world (Appel et al., 2020).This dependency is found critical in many researches and revealed a positive effect on consumer consideration set (Sreedevi, 2020; Beyari and Garamoun,2022). Assessment of the mediating effects of purchase duration in the impact ofthis increasing social media landscape and dependencies of various parties (Sreedevi, 2020; Appel et al., 2020) on the buying process and specifically on buying behaviour of consumers is a relatively unexplored area in consumer behaviour. Based on this gap, the current research is examining how social media dependency affects consumer purchasing behavior, specifically by looking at how purchase duration plays a role in this relationship. Moreover, this research adds to the literature on cross-national consumer behavior, using samples from India and Turkey.

 

Literature Review

 

Consumer purchasing behaviour (COPB) and social media dependency (SOMD)

 

Social media has significantly increased and shared a platform for e-commercial activities around the world (Kunja and Gvrk, 2020; Beyari and Garamoun, 2022). Research interest in the effect of online digital platforms' on purchasing decisions has been growing, as several means and ways of e-commerce and social commerce are coming to light with innovations in information communication technologies. Contemporary research has exhibited that social media aids business decision-making and customer retention strategies (Beyari and Garamoun, 2022). Social media, as it is accessed by a wide number of consumers through such platforms as Instagram, Twitter, and Facebook, is a natural application for promotional efforts (Torres et al., 2018).The latest social media platforms incorporate diverse strategies ranging from personalized business messaging to cutting-edge artificial intelligence. In contemporary times, social media has supplanted traditional word-of-mouth, ushering in a novel phenomenon, as noted by (Yones and Muthaiyah2023).Business firms are moving from e-commerce to social commerce. (Addo,2021) highlighted the positive impacts of social cues like likes, chats, visits, and time exposure towards increasing social commerce by selling both transactional (purchase) and non-transactional (followership) benefits. In the context of social media, influencer marketing proves to be a significant in-breaking influence that influences the purchasing decisions of their consumers (Javed et al., 2022). Finally, this paper introduces a fresh perspective on evaluating customer engagement in live-streaming digital marketing. The study advocates for additional research in this evolving realm of social commerce, particularly in navigating the challenges imposed by the constraints of the COVID-19 pandemic.

One of the most important digital marketing tools that have come out today is social media influencer marketing (Saima and Khan,2020). In social media, reliability, information quality, and entertainment value are the most notable influences on the trustworthiness of the influencers, and as well as they pose indirect effects on purchase intention (Saima and Khan, 2020).Until recently, while investigating the future capabilities of artificial intelligence-based software for social media marketing, researchers identified different causal configurations that can boost potential users’ intention to test and use the software based on afsQCA approach (Capatina et al., 2020). In another study of millennials and Generation Z, it was noticed that music, humor, and social media influencers have a very positive impact on users and their intentions (Munsch, 2021). (Beyari and Garamoun,2022) also revealed that SOMD has a positive but insignificant effect on consumer consideration sets. In another significant study, Bag et al. (2021) found that customer knowledge creation, user knowledge creation, and external market knowledge creation have significant effects on B2B marketing-rational decision-making. Many scholars opined that SOMD is critical (Kujur and Singh,2020)in influencing information, product delivery, and ultimate purchase decisions. This dependency is a factor that is mainly exercised outside the e-commerce systems since third-party companies host the platforms (Beyari and Garamoun, 2022). Therefore, the hypotheses undertaken in this study are:

 

H1: Social media dependency has a significant positive impact on purchase duration both in India and Turkey.

H2:Social media dependency has a significant positive impact on consumer purchasing behavior in both India and Turkey.

 

Consumer purchasing behaviour(COPB) and digital marketing

 

Consumer purchasing behaviour (COPB) is the learning of inter-relationships between customers, clusters or organizations selection, buying, usage, and disposal of ideas, goods, and services to satisfy their needs (Khan et al., 2022).COPB is no doubt a vital measure of the progress of every business organization but it follows a sequence starting from purchasing attitude to intention and then actual COPB (Ajzen, 1985). Consumers' purchase intentions are fundamentally influenced by intrinsic factors, specifically the attitudes of customers, which play a pivotal role in shaping consumer behavior within the marketing domain. These behaviors are discernible through various channels, encompassing both traditional and social media marketing. This influence extends across diverse market segments, including business-to-business (B2B), business-to-customers (B2C), and even customer-to-customers (C2C) markets. (Islam et al., 2018; Jibril et al., 2019).While utilizing digital platform platforms, according to (Bala and Verma, 2018) numerous methods of introduction were provided by the World Wide Web including search engine optimization (SEO), search engine marketing (SEM), content marketing, marketing, and content automation, e-commerce marketing, campaign marketing, and social media marketing which are some examples of the most successful ones among them these courses are available online. Digital marketing is the marketing of a product or service through digital means such as the internet or mobile phones as we know. The most common means of digital marketing are email marketing, social media, internet marketing, mobile devices, and search engine optimization. The promotion of goods/services or even ideas through either websites or emails is called online marketing.

. Recent literature has firmly established the correlation between digital marketing and AI, contributing insights to areas such as market forecasting, process automation, and decision-making, while enhancing the efficiency of tasks traditionally performed by humans (Hassan, 2021).In recent years, the impact of AI on digital marketing has surged, empowering marketers to personalize sales and digital marketing efforts beyond conventional expectations. The integration of artificial intelligence technologies in digital marketing, coupled with human-generated data, enables companies to cultivate trust on digital platforms and enhance positive, tailored client experiences through a comprehensive approach (Rabby et al., 2021; Bag et al., 2021). The implications of this cutting-edge intersection of digital marketing and AI extend to various sectors, including financial services (Mogaji et al., 2020), education, healthcare (Hassan, 2021), and others. However, the presence of inconsistent findings poses a challenge in developing a robust model for Consumer Online Purchase Behavior (COPB), especially in the context of social media dependency and associated purchase duration.

Again, many scholars already found in their research that consumers’ attitudes and satisfaction as strong mediators for the links between the quality of website design and purchase intentions (Wen, 2012) that vary widely from country to country. While internet knowledge has a significant role in buying decisions (Neger, M., & Uddin, B., 2020), the role of online and offline purchase duration is so far unexplored. Service orientation has been growing worldwide, maybe for tangible products and many scholars empirically proved that e-shopping for services attenuates the effects of spatial attributes on distance and duration ( Mallapragadaet al., 2016; Shi et al., 2020; Li et al., 2021).

The most modern sphere of digital marketing as a mediating issue is expected to introduce a new insight as many researchers indicated limitations that may have privacy, bias, and ethics and are pervasive in AI-based technologies therefore empirical lack of trust is the primary concern for the B2C scenario and power inequality plays a significant role if considering the B2B scenario (Anayat and Rasool, 2022).This consumer attitudes, satisfaction, perceived behavioral control, purchase intention, and above all purchase decision draws empirical attention for investigating relationship between shopping time and duration (Jacoby et al., 1976; Li et al., 2021).

 

 Therefore, it is hypothesized that:

H3: There is a positive relationship between consumers' purchase duration and their purchasing behavior both in India and Turkey.

 

Purchase duration

Since time immemorial, purchase begets sales that create economic value and contribute to society. The time elapsed between the first user-product interaction and the final purchase is known as purchase duration. It is a well-established construct that has a dramatic effect on the consumer’s buying course. Purchase duration stands as a pivotal factor that is deployed by AI mechanism is e-commerce for user behavior estimation indicated by (Allal-Chérif et al.,2021.). (Lee et al. 2021) opine that we may treat it as an artifact of AI systems performance as well.

In the realm of online buying and selling, transactions occur without direct physical meetings or negotiations, facilitated through smartphones and computers (Maghfuroh, 2020). The popularity of online transactions stems from their accessibility and flexibility, allowing individuals to engage in buying and selling activities anywhere and anytime, often through online platforms or dedicated e-commerce sites (Wahyuni, 2022).

It's crucial to note that purchase duration and shopping time are distinct concepts, recommended as independent variables associated with machine learning, purchase duration, and product recommendation (Beyari and Garamoun, 2022). Purchase duration refers to the specific period determined by producers or sellers, while shopping time represents the available period for customers with a specific interest in making a purchase. Market research on shopping time is emerging as a significant trend in understanding Consumer Online Purchase Behavior (COPB). Additionally, shopping time tends to be pro-cyclical, with purchasing effort increasing with income, as higher income levels reduce the opportunity cost of searching for goods to buy(Petrosky-Nadeau et al., 2016). (Cachero-Martínezand andVázquez-Casielles2018) investigated and found how shopping experiences stimulate consumers’ engagement and their predisposition to spend more time at the retail store which may be extended to many other sectors. Their research contributes to increasing shopping time with the mediating role of visit frequency that enhances purchase duration. Research on purchase duration is relatively less and draws attention nowadays with a wider perspective. Product information, booking or actual purchase, delivery, and actual consumption of products and services vary. Products and services with a significant time lag have direct effects on sales and actual purchases. Many researchers have explored the connection between factors influencing trust in online shopping and their impact on consumers' purchase intentions. They used the following interchangeably: perceived service quality, perceived website quality, perceived reputation, and the mediating and moderating roles of these variables.(Qalati et al., 2021), flash sales, perceived risk, perceived informativeness, perceived ease of use, and perceived trust (Habib and yum, 2018) but not the purchase duration. The most established assertion on buying decisions is the longer time one takes to buy, the more the possibility to acquire (Givan et al., 2021; Lee et al., 2021).Figure 1 displays the proposed research model. It is hypothesized that:

H4a: There is a mediating effect of purchase duration in the relationship between social media dependency and consumer purchasing behavior in India.

H4b: There is a mediating effect of purchase duration in the relationship between social media dependency and consumer purchasing behavior in Turkey

 

Consumer Purchasing behaviour (COPB), Social Media Dependency, and Purchase Duration Model in Cross-country Perspective

The world is digitally divided. This division is prevalent in e-governance, health, education, automobiles, and FMCG products, etc. which influence consumers significantly While urban users are increasingly using online platforms for purchasing goods and availing of services, rural customers are more inquisitive about it (Ma, 2022; Hamza et al., 2019). Regarding this online and offline choice of annual purchases (of a widely used product e.g. clothing) and involvement of transport, Hischier (2018) focused on worthwhile sustainability issues only and can be readdressed through purchase duration also.  Many studies on the urban and rural divide, and residents’ attitudes dealt with relevant enabling factors and inhibiting factors that are required for the adoption of development planning and policy recommendations (Zhang and Zhu, 2021). For the adoption of digital platforms and their extended uses, many scholars focused on models that are based on perceived usefulness, perceived ease of use, perceived behavioral control, perceived facilitating conditions, perceived services qualities, perceived security, and resistance to change (Zhang and Zhu,2021) but through the mediating role of purchase duration is a novel approach in this study.

The Theory of Planned Behavior by Ajzen (1991) is considered one among the best established for understanding attitudes and consumer behavior. In the context of COPB, this framework has been highly applied. According to the TPB, the central role is played primarily by the construction of the behavioral intention that directly influences the behavior. This behavioral intention is determined by three essential elements that include attitude toward the behavior, subjective social norms, and perceived behavioral control in that order. (Le‐Anh and Nguyen‐To, 2020). Though this is a very simple model yet many other models have been developed on its basis, presented by different researchers e.g. alphabet theory presented by (Zepeda and Deal,2009), TPB for studying organic food purchase intention by (Yilmaz and Ilter,2017), attachment theory by VanMeter (2018) social network theory by (Akar and Dalgic,2018) etc. Most recently, marketers have linked consumers' multiple purchasing approaches to the higher use of social media and this trend has been increasing day after day with various new dimensions. Moreover, this social network theory also confirms that behavioral attitude, social norms, and perceived behavioral control have a positive impact on consumers' purchase intentions. Therefore, this study is a novel attempt to understand COPB in the backdrop of increasing and varied media dependencies and purchase duration. Furthermore, this particular research incorporates the social network theory in integration with the theory of planned behavior through social network analysis. These social networking services contribute to network structure variables and personal motivation or COPB as a whole (Lee & Cha, 2022). Again, the attachment theory provides a valuable lens for delving into the underlying processes through which social network ties facilitate community purchases. This theory posits that individuals form attachments to specific objects, fostering a willingness to invest more emotional, cognitive, and behavioral resources into these objects. In the context of community purchases, attachment theory can be employed to understand how the emotional and psychological connections individuals develop within their social networks influence their inclination to allocate resources, both in terms of time and commitment, towards community-related acquisitions. (VanMeter etal., 2018).

The need for dealing with the purchase duration, and management of shopping duration with a unique experience amid this digital world is of immense importance and drawing immediate attention. In service sectors e.g., aviation, health, insurance, etc. this booking management directly impacts on demand and supply of a product or service (Tanand Sundarakani, 2021).Though the mediating role of lack of transparency, perceived distrust and efficiency, trust, business innovativeness, sustainable development, personalization (Kang et al., 2023; Lopez and Garza, 2023; Ranjan et al., 2022; Upadhyay et al., 2023; Dixit et al., 2022; Ifekanandu et al., 2023) are widely used constructs in the literature of consumer buying behaviour, the implications of purchase duration still remains uncalled for.

Using social media with its most modern uses for unique shopping experiences through purchase duration is a new trend in consumer research that draws the attention of future cross-country investigation also for wider implementations, specifically in cross-country perspective with a population of diverse uses of social media, its dependencies, expertise, and platforms.

Therefore, the proposed research model is given in Figure 1 exhibiting asserted interrelationships with respective hypotheses in the study.

 

Figure 1.Suggested Research Framework

 

 

 

 

Research Approach

 

Sample and Data Gathering Process

In this study, data collection was conducted online using the Qualtrics service. Following the elimination of incomplete responses during data collection processes carried out separately for the samples from India and Turkey, analysis was performed with the responses of 329 participants from India and 204 participants from Turkey. In the Indian sample, 59.6% of participants were male, and 67.5% were within the age range of 18 to 25. Among the participants, 75.1% were single, and 51.1% held a bachelor's degree. 46.8% of participants made online purchases of products and services once a month. In the Turkish sample, participants were predominantly composed of females, accounting for 54.4%, and 39.2% of participants were within the age range of 18 to 25. Among the participants, 51% were single, and 42.2% had a bachelor's degree. 44.1% of participants made online purchases of products and services once a month.

 

Measurements

In the current study, the authors have developed measurements in line with the context of the research by examining previous studies related to social media dependency and purchase duration in consumer purchasing behavior (Beyari and Garamoun, 2022; Yin and Qiu, 2021; Gupta et al., 2020; Cherukur, 2020). For developing the scale for the current study researchers reviewed the research articles related to consumer purchasing behavior, social media dependency on digital marketing, consumer decision /purchase intention, social media, and purchase intention and closely observed the variables studied and scale used or developed by the researchers. Based on this the new questionnaire was developed by the researchers and run through reliability and validity checks and then the factor analysis and pilot study were conducted.

To ensure the reliability and validity of the questionnaire, it underwent rigorous checks. Reliability refers to the consistency and stability of the questionnaire's measurement, while validity concerns the extent to which the questionnaire accurately captures the constructs being studied. These checks will help ensure that the questionnaire produces reliable and valid results. Following the reliability and validity assessment, a factor analysis was conducted. Factor analysis is a statistical technique that aims to identify the underlying dimensions or factors within a set of variables. Additionally, a pilot study was conducted. The pilot study involves administering the questionnaire to a small sample of participants. This helps in assessing the feasibility of the questionnaire, identifying any potential issues or ambiguities in the wording of the questions, and determining the time required to complete the questionnaire. The feedback (Ameen et al., 2021) and insights gained from the pilot study will allow the researchers to make necessary refinements to the questionnaire before implementing it on a larger scale.

The developed measurements were initially evaluated through a pilot study involving 57 participants from India and 62 participants from Turkey. Thanks to the pilot study, measurements were refined for reliability and validity, and these refined measurements were used in the main research process. The measurements for the variable’s social media dependency and purchase duration in the research consist of eight items, while the measurement for consumer purchasing behavior consists of five items. Each measurement was prepared in a 5-point Likert scale format (1=strongly disagree, 5=strongly agree).

 

Data analysis

We employed SmartPLS4.0.9 (partial least squares) to test the proposed theoretical model and hypotheses. SmartPLS stands out due to its ability to make robust predictions with weak measurements compared to covariance-based Structural Equation Modeling, and its lack of requirement for normal distribution assumptions (Hair et al., 2017; Reinartz et al., 2009). Furthermore, for the analysis of participants' demographic information, frequency analysis was conducted using the SPSS 22.0.

Data Analysis and Findings

 

Measurement model

A few statistics were used in calculating the reliability and validating the measurement models. In estimating the reliability, Cronbach's alpha, composite reliability that is (rho_a), and (rho_a) of the measurements were assessed. From Table 1 and Table 2, it can be seen that in both Indian and Turkish samples, the various statistics measures for reliability result as being above the threshold value of 0.7 (Hair et al., 2021). This therefore implies that the measurements are reliable.

Besides, convergent validity and discriminant validity were used for the measurements. In this context, in respect to the verification of convergent validity, an examination of average variance extracted (AVE) and outer loadings of the measurements were carried out. From both samples of Indians and Turks, on counting AVE values that exceeded the 0.50 threshold, it is also reported that the outer loadings exceeded the 0.60 threshold as well (Hair et al., 2010). The measurements have convergent validity. For discriminant validity, both the Heterotrait-Monotrait Ratio (HTMT) and the Fornell-Larcker criteria were evaluated. According to the HTMT criterion, the correlation values of constructs must be less than 0.90 (Kline, 2015). Moreover, employing the Fornell-Larcker criterion, the square root of the AVE for each construct should be greater than its correlations with other constructs (Fornell and Larcker, 1981). As can be seen in Table 3, the constructs have demonstrated discriminant validity. Lastly, to test the multicollinearity of the constructs, VIF values were checked. The findings indicated that all the VIF values were way below 5 (Hair et al., 2019). Thus, it can be concluded that there is no issue of multicollinearity.

 

Structural model

In the research, bootstrapping with 5000 samples and cases was used to test the proposed hypotheses. Firstly, to assess the predictive capability of the exogenous variables on the endogenous variables in the proposed model, the R2 values of the endogenous variables were examined. Accordingly, in the Indian sample, consumer purchasing behavior had an R2 value of 47%, and purchase duration had an R2 value of 44%; in the Turkish sample, consumer purchasing behavior had an R2 value of 44% and purchase duration had an R2 value of 50%. It was observed that the R2 values of the endogenous variables exceeded the proposed threshold of 10% (Chin, 1998), indicating a satisfactory level of explanatory

 

Table 1.Model Assessment results  (India)

Construct		Loading	a Cronbach	Composite reliability (rho_a)	Composite reliability (rho_c)	AVE
Consumer Purchasing Behavior			0.717	0.722	0.825	0.541
COPB1		0.689
COPB3		0.782
COPB4		0.741
COPB5		0.726
Purchase Duration			0.843	0.844	0.884	0.561
PDRT1		0.677
PDRT2		0.759
PDRT3		0.760
PDRT4		0.735
PDRT5		0.812
PDRT6		0.747
Social Media Dependency			0.806	0.804	0.860	0.508
SOMD2		0.665
SOMD3		0.725
SOMD4		0.768
SOMD5		0.745
SOMD7		0.703
SOMD8		0.661
Note: COPB2, PDRT7, PDRT8, SOMD1 and SOMD6 were eliminated

 

Table 2. Model Assessment results (Turkey)

Construct		Loading	a Cronbach	Composite reliability (rho_a)	Composite reliability (rho_c)	AVE
Consumer Purchasing Behavior			0.780	0.793	0.857	0.601
COPB1		0.724
COPB3		0.837
COPB4		0.770
COPB5		0.764
Purchase Duration			0.880	0.883	0.908	0.586
PDRT1		0.814
PDRT2		0.798
PDRT3		0.776
PDRT5		0.843
PDRT6		0.680
PDRT7		0.674
PDRT8		0.755
Social Media Dependency			0.840	0.841	0.883	0.557
SOMD2		0.692
SOMD3		0.755
SOMD4		0.797
SOMD5		0.794
SOMD6		0.723
SOMD7		0.710
Note: COPB2, PDRT4, SOMD1 and SOMD8 were eliminated

 

Furthermore, the endogenous variables had medium predictive relevance with Q2 values greater than 0.25 and less than 0.50 for both India and Turkey samples (Hair et al., 2019). Furthermore, as seen in Table 4, the tested structural model in both the Indian and Turkish samples demonstrates acceptable fit indices with satisfactory SRMR and NFI values (Wang et al., 2022; Sarma et al., 2022).

 

Table 3. Discriminant validity

India
	Fornell-Larcker criterion					Heterotrait-Monotrait ratio (HTMT)
	COPB	PDRT	SOMD			COPB	PDRT	SOMD
COPB	0.735				COPB	1.000
PDRT	0.623	0.749			PDRT	0.791	1.000
SOMD	0.637	0.668	0.712		SOMD	0.829	0.795	1.000
Turkey
	Fornell-Larcker criterion					Heterotrait-Monotrait ratio (HTMT)
	COPB	PDRT	SOMD			COPB	PDRT	SOMD
COPB	0.775				COPB	1.000
PDRT	0.603	0.765			PDRT	0.711	1.000
SOMD	0.636	0.713	0.746		SOMD	0.777	0.829	1.000
Notes: COPB: Consumer purchasing behavior; PDRT: Purchase duration; SOMD: Social media dependency

 

According to the results obtained through the bootstrapping method, in the Indian sample, social media dependency significantly and positively influenced both purchase duration (β = 0.668; p < 0.000) and consumer purchasing behavior (β = 0.399; p < 0.000). Thus, H1 and H2 were supported. Additionally, purchase duration was significantly related to consumer purchasing behavior (β = 0.356; p < 0.000), confirming the support for H3. Similarly, in the Turkish sample, social media dependency positively affected both purchase duration (β = 0.713; p < 0.000) and consumer purchasing behavior (β = 0.420; p < 0.000), supporting H1 and H2. Furthermore, purchase duration positively influenced consumer purchasing behavior (β = 0.303; p < 0.000), thus supporting H3. As a result, as seen in Table 5, Figure 2, and Figure 3, all direct effect hypotheses were supported in both the Indian and Turkish samples.

 

Table 4. Saturated model results

	India					Turkey
Construct	R2	Q2	SRMR	NFI	Construct	R2	Q2	SRMR	NFI
COPB	0.473	0.395	0.080	0.773	COPB	0.445	0.394	0.069	0.835
PDRT	0.444	0.434			PDRT	0.506	0.493
Notes: COPB: Consumer purchasing behavior; PDRT: Purchase duration; Q2: predictive relevance; SRMR: Standardized root mean square residual; NFI: Normed fit index

 

Table 5. Path coefficients

Hypotheses	India					Turkey
	Β	t	p	Results		Β	t	P	Results
H1 SOMD→PDRT	0.668	17.405	0.000	Accepted		0.713	19.239	0.000	Accepted
H2 SOMD→COPB	0.399	7.033	0.000	Accepted		0.420	5.115	0.000	Accepted
H3 PDRT→COPB	0.356	5.974	0.000	Accepted		0.303	3.772	0.000	Accepted
Notes: COPB: Consumer purchasing behavior; PDRT: Purchase duration; SOMD: Social media dependency

           

The mediating effect of purchase duration in the relationship between social media dependency and consumer purchasing behavior has been tested using the bootstrapping method. The mediating effect is interpreted based on the variance accounted for (VAF) value, which is obtained by dividing the indirect effect by the total effect. If the VAF value is between 20% and 80%, it indicates partial mediation; if it exceeds 80%, it indicates full mediation (Hair et al., 2014). As seen in Table 6, VAF values of 38% for the Indian sample and 34% for the Turkish sample were achieved. Based on this, a partial mediating effect of purchase duration in the influence of social media dependency on consumer purchasing behavior has been identified. Consequently, hypotheses H4a and H4b have been supported.

 

 

Table 6. Mediation results

India
Type of effect	Effect	Β	t		5% Lower	95% Upper	status
Total effect (TE)	SOMD→COPB	0.640	16.975***		0.551	0.700	sig
Indirecteffect (IE)	SOMD→PDRT→COPB	0.240	5.503***		0.154	0.323	sig
VAF	IE/TE	38%
Result				Partialmediationexist (H4a supported)
Turkey
Type of effect	Effect	Β	T		5% Lower	95% Upper	status
Total effect (TE)	SOMD→COPB	0.636	13.145***		0.522	0.719	sig
Indirecteffect (IE)	SOMD→PDRT→COPB	0.216	3.725***		0.103	0.330	sig
VAF	IE/TE	34%
Result				Partialmediationexist (H4b supported)
Notes: *p < 0.05; **p < 0.01; ***p < 0.001; VAF: Variance accounted for; COPB: Consumer purchasing behavior; PDRT: Purchase duration; SOMD: Social media dependency

 

Conclusıon

The study further exhibits that social media dependency has a significant positive impact on both purchase duration and COPB( Beyari and Garamoun, 2022; Yin and Qiu, 2021)  in both India and Turkey. This supports hypotheses H1 and H2, indicating that individuals who are more dependent on social media are likely to spend more time on purchase decisions and are more influenced by digital marketing promotions.

Additionally, there was a positive relationship observed between purchase duration and COPB. This means that consumers who take more time to make purchase decisions are more likely to exhibit specific purchasing behaviors influenced by digital marketing.

Therefore, this study is a novel approach with network structure variables(Lee & Cha, 2022), e.g. purchase duration, and degree of dependency on social media that are increasingly important and distinctive attempts for COPB. 

Furthermore, the mediating effect of purchase duration was examined in the relationship between social media dependency and consumer purchasing behavior. The results indicated that purchase duration partially mediates the influence of social media dependency on consumer purchasing behavior in both India and Turkey, supporting hypotheses H4a and H4b.

The study contributes to the understanding of how social media dependency impacts COPB through the mediating role of purchase duration. The findings provide insights for marketers and businesses to tailor their strategies and interventions, ultimately enhancing their digital marketing efforts and overall consumer engagement. This study advances theoretical knowledge by exploring the nuanced relationships between these factors, paving the way for further research in the field of online consumer behavior.

 

Practical Relevance:

These findings have several practical implications for marketers and businesses operating in the digital age. Firstly, recognizing the role of social media in influencing both purchase duration and COPB(Beyari and Garamoun, 2022) can inform marketing strategies. Businesses can tailor their digital marketing efforts to capture the attention of users who are more dependent on social media(Yin and Qiu, 2021), thereby potentially extending their purchase durations and encouraging specific behaviors.

Moreover, understanding the mediating role of purchase duration highlights the importance of personalized and targeted marketing campaigns. Implementing strategies that create a sense of urgency or appeal to impulsive buying tendencies can potentially shorten purchase durations and boost overall sales.

Theoretical Relevance:

The current body of literature is supplemented with an empirical link between social media dependency, duration of purchases, and COPB (Beyari and Garamoun,2022). The evidence is a point well-made that variables are connected. The very complex and intricate web of online consumer behavior is tied to the factors revealed in it.
The study, however, introduces the concept of purchase duration as a mediator between social media dependency and consumer purchasing behavior (Yin and Qiu, 2021). This further extends our understanding of mechanisms through which social media influences consumer behavior and provides a novel perspective for future research.

Future Research Directions

Although the findings this study offers are valuable, there are many avenues for future research. One of them is how the differences in culture might affect the relationships discussed here. Because this study focused only on India and Turkey, looking at these same kinds of relationships in other cultures and nations could provide a full understanding.
Other factors that may moderate or mediate the relations will add more depth to our understanding. For instance, individual characteristics such as personality traits or decision-making styles might explain what's going on here.

This study essentially operates within the online context. Future studies can ask how these relations appear in consumer behavior in the offline marketplace or hybrid online-offline shopping experiences.

 

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Appendix

 

Variables	Statements	Reference
Consumer Purchasing Behavior	·         I frequently make purchases based on digital marketing promotions. ·         I compare prices and features online before making a purchase decision. ·         I am more likely to buy a product or service after seeing it advertised digitally. ·         Digital marketing influences my brand choices. ·         I have made impulsive purchases due to digital marketing.	Cherukur(2020); Gupta et al. (2020); Yin and Qiu (2021)
Social media dependency	·         Occasionally, social media shows or suggests contain marketing messages from online agencies. ·         At times, I click on other links from my social media. ·         I keep track of various brands from my social media. ·         You have numerous products on your wish list for e-commerce websites ·          Every time you log into these sites you encounter something that catches your attention to the extent that you are most likely to purchase it in the near future ·         Recommendations for products you encounter are based on products that you have previously purchased. ·          You, at one point, purchased part of, if not all of, the recommendations presented to you. ·         You have shown repetition in purchases as you interact with e-commerce websites	Beyari and Garamoun (2022)
Purchase duration	·         Frequency of  your engagement  in online shopping ·         On average, what time do you spend browsing online shopping websites or apps before making a purchase ·         Very Little Time Little Time Moderate Time Significant Time Extensive Time ·         I have noticed that websites or apps employing artificial intelligence in their product recommendations or personalized shopping experiences lead to a shorter purchase duration. ·         Personalized recommendations or product suggestions generated by artificial intelligence have a significant influence on my purchase decision-making process. ·         The use of artificial intelligence in online shopping platforms has made the purchasing process more efficient for me. ·         I have made impulse purchases online due to targeted advertising or personalized recommendations from artificial intelligence algorithms. ·         Overall, the use of artificial intelligence enhances my online shopping experience in terms of purchase duration. ·         When presented with personalized recommendations generated by artificial intelligence algorithms, I am more likely to explore additional products or spend more time browsing. ·         The presence of real-time chat support or virtual shopping assistants powered by artificial intelligence would make me more inclined to make a purchase. ·         Overall, the role of artificial intelligence has had a positive impact on reducing my purchase duration in online shopping.	Beyari and Garamoun (2022).