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Article

The Whole Is More than Its Parts: A Multidimensional Construct of Values in Consumer Information Search Behavior on SNS

by
Ofrit Kol
1,* and
Shalom Levy
2
1
School of Communication, Ariel University, Ariel 40700, Israel
2
Department of Economics and Business Administration, Ariel University, Ariel 40700, Israel
*
Author to whom correspondence should be addressed.
J. Theor. Appl. Electron. Commer. Res. 2022, 17(4), 1685-1695; https://doi.org/10.3390/jtaer17040085
Submission received: 25 October 2022 / Revised: 20 November 2022 / Accepted: 30 November 2022 / Published: 2 December 2022
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Abstract

:
The notion of consumer values has been a key factor in understanding consumer behavior and has attracted the attention of various scholars in e-commerce. The aims of this study are to conceptualize a multidimensional construct of consumer information value (CIV) on social network sites (SNS), to examine the construct empirically, and to investigate the nature of its relationships with consumer information search behavior, i.e., the use of information channels on SNS. Quantitative research was conducted using a representative sample of 612 Facebook users. The use of a higher-order construct and structural equation modeling procedures reveals that the following five value constructs—Economic, Psychological, Social, Functional and Hedonic values—are significant facets of the CIV. It suggests that the CIV model is a powerful tool that directly explains consumer information search behavior and has better explanatory power than the sum of its parts.

1. Introduction

Information search behavior has traditionally been considered a critical component of the consumer’s decision-making process [1,2] and is an essential part of e-commerce [3,4]. Engel et al. [5] identify external information search as the motivated acquisition of information from the environment, implying that there are reasons behind this motivation of search behavior.
Social network sites (SNS) are evolving into important sources of information, providing consumers with easily available resources for product information search [6,7]. Consumers seek advice from other users and follow brands [8]. According to a 2016 PwC survey carried out in 25 countries, 78% of consumers are influenced by social media when shopping online. 45% of SNS users read reviews and comments of other consumers, 44% receive promotional offers, 30% view advertisements and 20% are associated with particular brands or retailers [9]. Thus, it is claimed that SNS offers consumers two types of information sources in one place, commercial (market generated) and non-commercial (word of mouth) [10,11].
Consumers who search for information via SNS, are motivated in their information search behavior by some expected values or benefits which will help them make better purchase decisions [8]. Previous research demonstrated that SNS offer their users a flexible space to gratify various consumer information needs such as social needs, psychological needs, hedonic needs and utilitarian, functional or economic needs [10,12]. However, research regarding the impact of these motivational values on consumer information search behavior is scarce, mainly on consumers choice of the different information channels available on SNS. Furthermore, there is no comprehensive framework to capture the varied values of information expressed in consumer search behavior on SNS. Previous research suggested that the choice to adopt a multifaceted construct as the construct of theoretical convenience seems to reflect the assumption that the underlying construct is assessed indirectly by measures of its various manifestations, which has been called the latent variable approach [13,14,15]. By measuring several manifestations, it is more likely to gain a complete grasp of the underlying construct of interest [14]. In addition, it can also be addressed from the synergistic approach, stating that the construct is more than the sum of its component parts such that each component gains something from its association with the others [13,14].
Therefore, the purpose of the present study is to enhance our understanding of what motivates consumer information search behavior via SNS. It develops a multidimensional scale of consumer information value, tests its validity and reliability and demonstrates the explanation power of this multifaceted construct over the explaining sum of its parts. This paper begins with a comprehensive review of potential dimensions of information value to be included as items in the multidimensional scale. Next, two alternative path models are utilized via Structural Equation Modeling (SEM), one examines separately the information values, and the other examines the CIV construct to determine the preferable one. The study assesses the construct’s external validity as a valuable instrument, by empirically testing the impact of the measured general construct on the consumer’s behavior. Finally, the last section discusses the CIV as a preferable framework. Theoretical and practical implications are also provided.

2. Literature Review

Social media’s popularity has led to the development of social commerce, where information search about products and services is a key aspect of consumer behavior [16]. Consumers searching for information on SNS confront various types of information channels in one place, classified by the source of information, into commercial and non-commercial channels [4,11,17]. Commercial channels are based on marketer-generated content, which is “branded content on social media that can take many forms, among them content shared directly by the brand from its brand page and social-marketing tools such as Facebook’s “Sponsored Stories” advertising unit” [18]. Non-commercial channels are based on user-generated content, supported through social media, which is “a mixture of fact and opinion, impression and sentiment, founded and unfounded tidbits and experiences” [19]. This information is actively created and distributed by consumers for consumers, with the intent to inform, enrich and enlighten one another about products, brands, services and more [20,21].
As evidenced by the literature, consumers expect to receive some benefits or values from their search for information that will help them make better purchase decisions [10,22]. The vast literature on values in advertising and word of mouth advocates that value for consumers is a subjective evaluation assessment of the worth or utility of the communication they receive [23]. Therefore, the concept of information value can be defined as the utility or worth of the information. Moreover, research suggests that information value positively affects consumers’ behavior [23,24] and the amount of effort they are willing to devote to information search [2].
Studies grounded on the consumption-value theory [25,26] and subsequent studies [27,28] suggest that SNS can offer users a flexible space to gratify various consumer information needs, such as social needs, psychological needs, hedonic needs and utilitarian, functional or economic needs [10,12]. Based on that, the current study proposes a multidimensional comprehensive construct of consumer information value (CIV)—a higher-order construct, composed of the aforementioned established consumer values received from information on SNS: Economic value, Psychological value, Social value, Functional value and Hedonic value. The CIV indicates the degree of the general value of information for the consumer. In the following section, each of the values is presented and their inclusion as distinct facets of the CIV is justified.

2.1. Distinct Facets of the CIV

The economic value (EV) leans on the economics of information theory [29,30] and refers to the benefits of saving time, effort and money [12,31]. The interactive nature of the Internet in general and the SNS, in particular, improves the access to information while reducing the cost of the search [32,33]. Additionally, saving money comes from the special offers, coupons and discounts consumers receive from marketers on SNS, which help them increase the economic benefit of the buying decision [34,35].
Psychological value (PV) refers to the emotional benefits gained from the reduced uncertainty involved in purchase decisions and the emotional affective state [36,37]. Information received on SNS, mainly from other users’ experiences, has been shown to be more trustworthy and can reduce uncertainty, thereby mitigating perceived risks [36,38].
Social value (SV) refers to the social benefits users receive when connecting to others via SNS [39]. This value is located in the feeling of belonging and receiving recognition from those who share the same norms, values and interests [12,40]. Information search on SNS facilitates a type of dialogue with people who have the same interests, developing a sense of social bonding [41]. Additionally, consumers often seek congruence with the norms of their friends and associates and pursue social approval for their consumption behavior [8].
Functional value (FV) suggests varied utilitarian benefits derived from the perceived quality of information that is tailored specifically to the needs of the consumer and his ability to accomplish tasks successfully [12,27]. Through non-commercial channels, consumers receive reliable recommendations from friends and acquaintances tailored precisely to their specific needs [42]. Through commercial channels, consumers gain relevant qualitative information about products and can learn about product specifications, and terms of service, and consult with the company representative.
Hedonic value (HV) relates to consumers’ feelings of fun or pleasure derived from using a technology [43] and the aesthetic value created by information consumption [44]. It refers to consumers’ experiential values such as good feelings, self-exploration and enjoyment derived from information searches on SNS, which can influence the decision-making process [12,45]. In summary, the study specifies five value facets identified in previous studies [12,25,26,27] and proposes that these facets are distinct dimensions of CIV. These CIV facets are assumed to relate comprehensively to the same theoretical construct (see Figure 1). Thus, the following hypothesis is formulated:
Hypothesis 1 (H1). 
The five constructs: EV, PV, SV, FV and HV are distinctive facets of consumer information value (CIV).

2.2. CIV and Its Explanatory Power

According to Carver [13], the adoption of a multifaceted construct seems to occur when the construct, serves as a convenient summary for several subsidiary tendencies that contribute to it. It is adopted when the whole seems more meaningful than any specific part and perhaps even more than the sum of the parts. Appealing to any one component of such a construct fails to capture something of its overall essence [14,15]. Thus, this synergetic approach assumes that the whole is greater than or different from the sum of its parts [13]. Following this approach, we assume that the CIV, as a multidimensional construct, will better explain consumer active information search behavior on SNS than all the separate values taken together. This approach leads to our research question:
Research question (RQ). 
Does the proposed construct CIV has the ability to explain information search behavior on SNS, and will this explanatory power be greater than the sum of its parts?

3. Materials and Methods

3.1. Sample and Procedures

Data were collected through a web-based survey using an online access panel in exchange for payment. A representative sample of Facebook users was obtained using quotas based on Facebook audience insights [46]. Although the panel was composed of pre-screened respondents who expressed a desire to participate in surveys, subjects were told that participation in the study was subject to their personal consent and that they could terminate their participation at any time. A preliminary requirement for participating in this study was having an active Facebook account. Confidentiality and anonymity were assured.
Respondents with missing values were eliminated from the sample. Overall, 612 active Facebook users were analyzed in this study, with an average number of 463 friends. Participants were 53% females and 47% males with an average age of 36 (between 18 and 80). Most of the participants possess above high school education level (74%), with an average or above-average income (62%).

3.2. Measurement

The questionnaire scale items were based on previous studies. The items for information value scales (FV, PV, EV, SV and HV) were taken from Gvili et al. [12]. The two information channels (Commercial and Non-commercial) scales were taken from Kol et al. [10]. All scale items were modified to suit product information search on SNS. Respondents were asked to indicate their level of agreement with different statements. A seven-point Likert scale was used, ranging from 1 = strongly disagree, to 7 = strongly agree. Additionally, two new multidimensional constructs were created. First, Consumer Information Value (CIV) was created as a higher-order construct Hair et al. [47] comprised of the five information values; economic value, psychological value, functional value, social value and hedonic value. Second, a new independent reflective construct—Channel of Information—was created as a higher-order construct [47] constituted from the non-commercial and commercial channels. Demographic data were also collected.

4. Results

First, items were subjected to exploratory factor analyses (EFA) with Varimax rotation. Eight factors were produced, explaining 76.32 percent of the cumulative variance, and all items’ loadings were above 0.5. The commercial information channel was divided into two dimensions (Ad and brand page) that were integrated to form a single measure as supported by the following confirmatory factor analysis. Additionally, a confirmatory factor analysis (CFA) was conducted to confirm construct validity. The results confirm the constructs (χ2 value (448) = 1284.26, p < 0.05 (χ2/df < 3); Comparative Fit Index (CFI) = 0.95; Normed Fit Index (NFI) = 0.92; and Root Mean Square Error of Approximation (RMSEA) = 0.055). The CFA shows that scale items loaded satisfactorily on the relevant latent variables; all loadings were above 0.5. Convergent validity, discriminant validity, and internal consistency were examined using Average Variance Extracted (AVE), Composite Reliability (CR) and Cronbach’s alpha (see Table 1). In AVE measurements, constructs were above 0.5, indicating that there is acceptable convergent validity of the constructs. Furthermore, comparing the square of the correlation estimate between any couple of these constructs with the AVE values reveals greater values for AVE in all cases, which verifies the discriminant validity of the constructs (Table 2 shows the correlations pattern between variables and the Maximum Shared Squared Variance (MSV)). CR measurements were all above 0.85 and Cronbach’s alphas were above 0.85, displaying acceptable reliability of the measurements.
Second, an additional CFA was conducted in order to assess whether the five CIV facets pertain to the same latent construct and whether the two-channel facets pertain to the same latent construct, in which both, CIV and Use Behavior (Channels of information) were treated as higher-order constructs. The tested CFA model is a second-order factor, namely a structure that contains two layers of latent constructs. The results confirm that both latent constructs pertain to CIV and Use Behavior (Channels), and the facets (all five CIV and two channels, respectively) are loaded acceptably (χ2 value (458) = 1060.70, p < 0.05 (χ2/df < 3); Comparative Fit Index (CFI) = 0.962; Normed Fit Index (NFI) = 0.936; Root Mean Square Error of Approximation (RMSEA) = 0.046). Additionally, the CIV higher-order construct’s AVE is 0.67 and the CR is 0.91, and the Use behavior (channels) higher-order construct’s AVE is 0.76 and the CR is 0.86, showing that the latent constructs which emerged from the CIV facets, and the channel facets are robust. Moreover, in AVE measurements, constructs were above 0.5, indicating that there is acceptable convergent validity of the constructs and CR measurements were all above 0.86 displaying acceptable reliability of the measurements (see Table 3). Therefore, we conclude that the five constructs: EV, SV, FV, PV and HV are distinctive facets of consumer information value (CIV), which was proven to be a robust, reliable and valid construct.
Next, a correlation test was conducted to examine the relationship between the values and the higher-order construct—Use Behavior (channels of information). Results (see Table 4) show a significant correlation between each one of the values and Use Behavior (channels of information). Hence, there is a correlation between all five values; consumers may seek to receive from the information, and the Use Behavior (channels of information).
Model testing: To answer the research question of whether the multidimensional construct, CIV, has better explanatory power of consumer information search behavior on SNS, two path models were conducted using Structural Equation Modeling (SEM). The first model examines the values separately. The overall fit statistics (goodness of fit measures) exhibit an acceptable level of fit (χ2 value (454) = 1336.19, p < 0.05 (χ2/df less than 3); Comparative Fit Index (CFI) = 0.945; Normed Fit Index (NFI) = 0.919; Root Mean Square Error of Approximation (RMSEA) = 0.056), indicating that the path model is valid. The second model examines the CIV construct. The overall fit statistics for this CIV model indicate that the path model is more robust (χ2 value (458) = 1060.70, p < 0.05 (χ2/df less than 3); Comparative Fit Index (CFI) = 0.962; Normed Fit Index (NFI) = 0.936; Root Mean Square Error of Approximation (RMSEA) = 0.046).
In the first model (see Figure 2), 3 values were associated with consumer use behavior (the channels); economic value (β = 0.53, p < 0.01), psychology value (β = 0.16, p < 0.05) and hedonic value (β = 0.11, p < 0.05), which accounted for 58 percent of the total variance of consumer use behavior (R2 = 0.58). Furthermore, the two additional values, social value (β = 0.05, p > 0.05) and functional value (β = 0.01, p > 0.05) were not significant and are not associated with consumer use behavior (the channels). Although in the correlation test these two values have a significant relationship with the channels (see Table 4), in the regression equation they are not significant due to multicollinearity, their association with the other values.
In the second model (see Figure 3), CIV—as a latent construct that emerged from the five facets—was associated with consumer use behavior (the channels) (β = 0.83, p < 0.01) and accounted for 69 percent of the total variance of consumer use behavior (R2 = 0.69). Parameter estimates and structural relationships are displayed in Table 5. Hence, the second model has higher explanatory power, and the CIV as a latent construct that emerged from the five facets better explains consumer behavior than each one of the values.

5. Discussion

The current study suggests a comprehensive framework of the values of information consumers obtain from information searches on SNS, as part of the buying process in e-commerce. A CIV framework was designed to capture the different values of consumer product information. It conceptualizes measures and validates the CIV, as a multidimensional construct consisting of five value dimensions that can benefit consumers: Economic value, Psychological value, Social value, Functional value and Hedonic value. First, the findings support the conceptualization of CIV as a higher-order latent construct, and therefore that the essence of the value of information can be conveyed through the five facets of CIV. Hence, this higher-order construct provides us with a comprehensive measure of the value of information on SNS. Second, the following perceptual model shows that the CIV can significantly better explain the consumer’s cognitive behavior, expressed by the consumer’s search behavior for product information on SNS.
It seems that CIV, the new multidimensional construct, has better explanatory power than the values put separately, explaining 11% more of consumers’ active information search (AIS) on SNS. The greater explanatory power of the CIV may be understood by the synergistic approach, which assumes that the whole is greater than or different from the sum of its parts [13]. That is, the multidimensional construct is more than the sum of its component parts, with each component gaining something from its association with the others [13,14]. Therefore, it can be concluded that CIV as a whole seems more meaningful than any specific part and perhaps even more than the sum of the parts.
The current study provides a significant contribution to theory and practice. From a theoretical perspective, the study enhances our understanding of consumer information search on SNS and the motivation for such a search in the context of social commerce. It suggests a new framework for consumer information value (CIV) via SNS, which was empirically validated and has a significant effect on consumer behavior. Furthermore, though the study focused on a specific social medium, Facebook, the design of the CIV can be adapted to other forms of traditional and digital social media. Second, the CIV is found to be a powerful predicting device. The CIV is powerful enough to directly explain behavioral aspects even better than the values separately. Third, the study reinforces and extends the consumption values perspective [25] on consumer information search via SNS.
From a practical perspective, marketing communication practitioners should note that in order to motivate potential consumers to be engaged in product information search via SNS they must provide some value: economic, psychological, social, functional or hedonic value. Moreover, this study provides practitioners with a valid and reliable instrument to evaluate the effectiveness of consumers’ engagement with the product information offered.
Finally, limitations should be noted. First, the framework and the empirical study were limited to the cognitive behavior of consumer information search on SNS. Future research could expand the framework to further include and explain constructs such as attitude, satisfaction and buying intention. Second, the current study focuses on the values of consumer information search behavior on SNS. Future studies should observe the information values from the sender’s perspective and examine what motivates the consumer to share information [48]. Third, based on previous literature [25,27], the CIV framework was formed on five distinct value facets. Future research could also look for additional potential values or benefits consumers seek during information engagement on SNS to enhance the dimensionality of the CIV concept. Fourth, this study concentrated on Facebook as a typical SNS channel. Nevertheless, previous research implied that SNS channels vary with respect to their perceived characteristics [49]. Therefore, for more generalization future research should examine the applicability of the proposed CIV model on other types of SNS channels such as Instagram, TikTok, and the like.

6. Conclusions

This paper aimed to investigate the magnitude of consumer information values on active use behavior on SNS. It offers a comprehensive framework that integrates varied established perceived values into a second-order construct to better explain consumer search behavior on SNS. Using a survey among active Facebook users, the study explores the effects of varied perceived values on active use behavior and compares them with the effects of the CIV multifaceted construct. The findings of this work reveal that the whole is more than its parts which means that the composed CIV model has better explanatory power than its component values. CIV as a whole seems more meaningful than any specific part and perhaps even more than the sum of the parts.

Author Contributions

Conceptualization, O.K. and S.L.; methodology, O.K. and S.L.; software, S.L.; validation, O.K. and S.L.; formal analysis, S.L.; investigation, O.K.; resources, O.K.; data curation, S.L.; writing—original draft preparation, O.K.; writing—review and editing, S.L.; visualization, O.K.; supervision, O.K and S.L; project administration, O.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data availability is upon request and consent of the authors.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. CIV—A multidimensional construct.
Figure 1. CIV—A multidimensional construct.
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Figure 2. Structural Model of separate effect of values—Path Analysis Results. Notes: Path parameters are standardized parameter estimates. R2 are in the right corner; ** p < 0.01, * p < 0.05.
Figure 2. Structural Model of separate effect of values—Path Analysis Results. Notes: Path parameters are standardized parameter estimates. R2 are in the right corner; ** p < 0.01, * p < 0.05.
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Figure 3. Structural Model of the CIV effect—Path Analysis Results. Notes: Path parameters are standardized parameter estimates. R2 are in the right corner; ** p < 0.01.
Figure 3. Structural Model of the CIV effect—Path Analysis Results. Notes: Path parameters are standardized parameter estimates. R2 are in the right corner; ** p < 0.01.
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Table
Table 1. CFA—Items’ Factor Loading and Variables’ Reliability and Validity Measures.
Table 1. CFA—Items’ Factor Loading and Variables’ Reliability and Validity Measures.
Construct Variables and ItemsStd. Coef.AVECRCronbach’s Alpha
Economic Value (EV) 0.720.910.92
The information on products/services I receive on Facebook helps me save money.0.79 **
The information on products/services I receive on Facebook helps me get greater value for the money I pay.0.86 **
The information on products/services I receive on Facebook reduces my search time on Facebook.0.84 **
The information on products/services I receive on Facebook helps me find better offers and solutions.0.89 **
Social Value (SV) 0.700.900.91
The information on products/services, I search for on Facebook lets me make a good impression on other people.0.87 **
The information on products/services, I search for on Facebook gives me a sense of belonging to a group or to other people.0.91 **
The information on products/services, I search for on Facebook helps me to feel accepted by other people.0.81 **
The information on products/services, I search for on Facebook improves the way I am perceived by others and tells people who I am.0.75 **
Functional Value (FV) 0.660.900.93
I trust the information I get on Facebook about products/services.0.77 **
The information on products/services that I receive on Facebook is usually reliable.0.80 **
Information about products/services I receive on Facebook is generally credible.0.75 **
The information I receive on Facebook allows me to get products/services in accordance with my needs0.88 **
I get quality information about products/services on Facebook.0.87 **
Psychological Value (PV) 0.630.890.91
The information I receive on Facebook makes it easy for me to try new products and services.0.82 **
The information I receive on Facebook reduces the concerns I have when making a purchase decision.0.81 **
The information I receive on Facebook allows me to make purchase decisions in accordance with societal expectations or what is acceptable.0.71 **
The information I receive on Facebook reduces the concerns I have when using the products or services.0.80 **
The information I receive on Facebook helps me decide what is best for me.0.81 **
Hedonic Value (HV) 0.800.920.92
Searching for information about products/services on Facebook is fun for me0.94 **
I enjoy searching for information about products/services on Facebook0.98 **
Finding information about products/services on Facebook is entertaining and amusing0.75 **
Non-Commercial Channels (Personal Profile &Groups of Interest) 0.520.860.85
I post a question in my personal profile requesting a recommendation or an opinion about a product from family/friends. 0.74 **
I request a certain person opinion about a product in a private message on Facebook’s Messenger.0.72 **
I read reviews and recommendation written in the past by friends/family on my personal profile0.68 **
I post a question on a relevant Facebook group (e.g., cooking moms, defective dads, etc.) in order to receive a recommendation or an opinion about a product. 0.77 **
I read opinions and recommendation about a product that were written in the past by the participants in a relevant Facebook group (e.g., cooking moms, defective dads, etc.)0.74 **
I “like” or write a response about information on a product that interests me in a relevant Facebook group0.66 **
Commercial Channels (Brand Page & Advertisement) 0.510.860.86
I go to a brand page to get information about a product or request the information by sending a message on the brand page Facebook messenger. 0.72 **
I go to a brand page to find coupons or promotions or marketing offers.0.77 **
I am interested in coupons and marketing offers I get on my personal profile from brand pages I “liked.0.67 **
I read relevant ads that appear on my profile.0.79 **
I actively “like” relevant ads that appear on my profile.0.70 **
If it is relevant to me, I click on the ad to get more information about the product.0.60 **
Notes: ** p < 0.01.
Table
Table 2. Correlations and the Maximum Shared Squared Variance (MSV).
Table 2. Correlations and the Maximum Shared Squared Variance (MSV).
NonCommCommEVSVFVPVHV
NonComm.0.520.60 **0.60 **0.39 **0.48 **0.53 **0.45 **
Comm.0.360.510.60**0.39 **0.47 **0.54 **0.44 **
EV0.360.360.720.49 **0.61 **0.70 **0.51 **
SV0.150.150.240.70.41 **0.57 **0.41 **
FV0.230.220.370.170.660.68 **0.50 **
PV0.280.290.290.320.460.630.51 **
HV0.20.190.260.170.250.260.8
Notes: ** < 0.01 (2-tailed); correlations are in the upper right side while the MSV values are in the lower left side; AVE are in bold diagonal.
Table
Table 3. Second Order CFA—Items’ Factor Loading and Variables’ Reliability and Validity Measures.
Table 3. Second Order CFA—Items’ Factor Loading and Variables’ Reliability and Validity Measures.
Construct Variables and ItemsStd. Coef.AVECR
CIV 0.670.91
FV0.83 **
PV0.93 **
EV0.89 **
SV0.64 **
HV0.65 **
Use Behavior—Channel 0.760.86
Non-Commercial0.87 **
Commercial0.87 **
Notes: ** p < 0.01.
Table
Table 4. Correlations between variables.
Table 4. Correlations between variables.
EVSVFVPVHV
Use Behavior0.672 **0.436 **0.534 **0.596 **0.497 **
Notes: ** p < 0.01.
Table
Table 5. Parameter Estimates and Structural Relationships.
Table 5. Parameter Estimates and Structural Relationships.
Relationship Standardized EffectRegression Weights
R2Total-DirectEstimateS.E.C.R.p
Model 1—Separately0.58
EV → Use Behavior 0.5320.3830.05660.810<0.01
SV → Use Behavior 0.0480.3700.400.9090.364
FV → Use Behavior 0.0980.8400.5210.6100.107
PV → Use Behavior 0.1550.1150.5820.001<0.05
HV → Use Behavior 0.1120.8000.3320.408<0.05
Model 2—CIV0.69
CIV → Use Behavior 0.8320.9390.77120.208<0.01
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Kol, O.; Levy, S. The Whole Is More than Its Parts: A Multidimensional Construct of Values in Consumer Information Search Behavior on SNS. J. Theor. Appl. Electron. Commer. Res. 2022, 17, 1685-1695. https://doi.org/10.3390/jtaer17040085

AMA Style

Kol O, Levy S. The Whole Is More than Its Parts: A Multidimensional Construct of Values in Consumer Information Search Behavior on SNS. Journal of Theoretical and Applied Electronic Commerce Research. 2022; 17(4):1685-1695. https://doi.org/10.3390/jtaer17040085

Chicago/Turabian Style

Kol, Ofrit, and Shalom Levy. 2022. "The Whole Is More than Its Parts: A Multidimensional Construct of Values in Consumer Information Search Behavior on SNS" Journal of Theoretical and Applied Electronic Commerce Research 17, no. 4: 1685-1695. https://doi.org/10.3390/jtaer17040085

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