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Journal of Intelligent & Fuzzy Systems 35 (2018) 2827–2836 DOI:10.3233/JIFS-169636 IOS Press

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Research on the collective efficacy of social networks with multi factor analysis

Peng Fan∗ Business School, Central South University, Changsha, China

Abstract. With the rapid development of Internet technology, social networks have been widely used in the world, and some of them are really active and some people are just browsing. Based on this, the collective efficacy was proposed and the three element interaction determinism was studied. The similarity between social network users was calculated and integrated, and the collective efficacy was studied. 60 members of the four network groups were interviewed, and 15 influencing factors of network group efficacy were coded. 230 college students were investigated by questionnaire and the multi factor analysis method was integrated. The influence of community members’ efficacy on their community involvement was studied through the initiative, focusing on sharing information and other dependent variables, attitudes, interests, values, personality and other independent variables. The research results showed that increased awareness of the degree of interpersonal similarity will increase the degree of involvement of social network group members, and psychological involvement played an intermediary role in perceived interpersonal similarity and the increases of perceived interpersonal similarity will enhance the formation of the sense of group efficacy.

Keywords: Multi factor analysis, social network, collective efficacy

1. Introduction

The emergence and popularity of the Internet has brought great changes to people’s life. The social net- work with a wide variety of functions has become an integral part of the life style and daily life of the majority of Internet users gradually. Micro-blog, social networking sites and other social networks have become the third major Internet applications of instant messaging and online banking. Social net- work transferred the right to master the resources to the majority of Internet users. The interaction of the website and the user, the participation of the users, the integration of the resources and the externality of the network achieved a huge leap [1] It can be seen throughout the social networking market that micro-blog Sina, QQ space, all networks, the world

∗Corresponding author. Peng Fan, Business School, Central South University, Changsha, China. E-mail: fanpeng2011 @outlook.com.

community, campus BBS and other social network operators were very successful and achieved good performance [2].

The successful operation of social networks depended on the understanding of the needs and preferences of the community members in the vir- tual community. It was necessary to know that what factors allow community members to use the com- munity and stay in the social network for a long time. That is to say, what are the factors that enhance the cohesion of social networks and how to enhance the community’s collective efficacy, which was very important for the development of social networks [3]. Social network was a new life style of human beings. Members of a social network can communicate and participate in community related activities for a long time. Social network generated attraction and affin- ity to community members and the intimacy as well as affinity between members have deepened grad- ually [4]. Thus it can be seen that social networks included many characteristics including cohesion

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among the members in the traditional community [5]. The production of social network cohesion was inevitable and it was the inevitable result of the devel- opment of interpersonal relationships [6]. The level of social network cohesion had an important impact on the collective efficacy of social networks to the social network service, so it is very important to under- stand and study the cohesion and collective efficacy of social networks [7].

There were users who used big V in micro-blog detonated traffic and integrated its own fan base, which let customers repast from the community to the line of the line to the real store dining from the online community to the physical stores line down [8]. Finding the catalyzer of members of the network community involved in the psychological involve- ment of the community into the behavior of the community will be a way of marketing for social economic times [9]. With the enhancement of the network self-efficacy, people’s attitude towards the corresponding network subject will be increased, which made people can’t help but think of Bandura will be extended to the level of self-efficacy of a con- cept - group efficacy. Whether it can play a role in the process of community members involved in the net- work community. What factors can contribute to the formation of group efficacy of network community members [10].

2. Collective efficacy

2.1. Three element interaction determinism

Psychologists had to answer what determined the human mind and behavior in the creation of the the- ory. Bandura has made three-yuan-decide answer and its unique lied that he put the person’s subjective fac- tors into the psychological and behavioral function of the causal decision model. The three element interac- tion determinism hypothesized that the environment, the internal factors and the behavior of the three fac- tors were independent of each other, and they were interactive with interactive decision [11]. The inter- active decision model of the three was shown in the following figure:

B, P, E represented the behavior, the main body and the environment respectively, and the two-way arrow indicated that the relationship between the two fac- tors was interactive in the graph. Both the model and the intensity of the model changed with the change of B, P and E. For example, E, as the object and

Fig. 1. Bandura’s reciprocal determinism.

the external conditions of the B part, determined the mode and intensity of B, and B can also change the E so that it can adapt to the needs of people. P and B were also determined by each other. The internal fac- tors such as motivation, intention, goal, emotion and so on affected the behavior pattern and intensity of P, in the same way, the internal characteristics and exter- nal results of B affected or partially determined the P’s intention to beliefs and emotional responses [12]. In the relationship between P and E, although per- sonal characteristics, emotional and cognitive ability of P were the product of the specific environment and subject to the constraints of environmental con- ditions, on the other hand, E also depended on the cognitive grasp of P. E can affect P only when it was grasped by P.

2.2. The concept and nature of collective efficacy

Bandura defined collective efficacy as: “a common belief in the ability of team members to combine their teams to achieve a certain level of performance in a situation”, which was the definition that was accepted by most researchers commonly. Collective efficacy was defined as a collective belief in the success of a specific task, which reflected the expectation of a group to accomplish a specific task. Collective effi- cacy referred to the ability of the members to perceive and evaluate the ability of the group, not the ability of the group itself.

Collective efficacy strength of individuals with dif- ferent status or role was different in the collective. At the same time, collective efficacy also changed with the field of collective activities. The collective may appear to be very confident in an activity, but the col- lective perception of the efficacy of the belief was very low in another area. Thus, collective efficacy was not a static group feature, which was fluctuating

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with the constant change of the relationship between the members and the external pressure of the exter- nal reality. However, as a collective property, we can use the consistency within the group rather than the differences among groups as the main indicators of the common beliefs of the group [13]. Bandura believed that the factors affecting self-efficacy, such as acquired experience, social persuasion and so on will also affect the collective efficacy in a wide sense. In Bandura’s view, collective efficacy beliefs can predict the level of group behavior. The study and regulation of collective efficacy of groups can predict and control the behavior of these groups.

3. Social networking collective efficacy

3.1. Social network user relations

A social network was the network service that user can establish an open or semi open account and each other connected through the link. Social net- works in China have become Web2.0 business with the most extensive coverage of users, the most influ- ential spread of and the highest commercial value [14].

The complex and special relationship among the users in the social network can be studied by the social network. The formation of social network was based on the form of user network. Different users were different nodes in the network, and you can use G (V, E, W ) to express a social network, V was a collection of users, E represented the edge of the col- lection. If two users Vi and Vj had a relationship, there was a side e(Vi, Vj ), and W represented the weight.

Fig. 2. Design the network.

The weight of the size can be different set according to the need [15].

There were three different forms of social net- working data according to the current situation of the development of social networks. The first was the two friends confirm the relationship and this kind of net- work friends need mutual recognition. Otherwise it can’t successfully to set up friend relationship, which was represented by Facebook, all networks and so on. The second category was one way concern type. Users concerned about other users and did not need each other must pay attention to their own, which can be chosen according to their own interests and was represented by the Twitter, micro-blog and Sina. The Third types were community groups. There was no clear relationship between the user, but the same community had some similar characteristics [16]. Traditional personalized recommendation method assumed that users were independent and identically distributed, and it ignored the trust between users based on social relations and the collective efficacy.

3.2. Current situation of social network and the similarity between users

The data showed that China’s total population was nearly 1 billion 340 million and the proportion of urban and rural population counted 50 percent in the statistics on Chinese Internet users. Internet users were 485 million and the Internet penetration rate reached 36%. Nearly 920 million were mobile phone users and mobile phone penetration rate was 67%. Chinese Internet users believed that the Internet was the most attractive media, which was more than TV. Chinese Internet users spent 41% of the time on

Fig. 3. Chinese Internet users and the present situation of social networks.

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Fig. 4. Social networking activities, items to promote collective efficacy.

social networking, and 77% of Chinese Internet users believed that the brand will be more attractive to par- ticipate in social networking.

The similarity between users can be reflected by their strong association operation and weak corre- lation operation. The strong correlation operation referred to the direct interaction between the users, such as forwarding, comments, sharing and other operations. Weak correlation operation referred to the user was not obvious interaction between rela- tions, such as attention to the same common page, which often reached the same geographic location or common used of the same site applications and so on. And the formula Sim (s, v) = asvn∑

k−1 ask+1

· h1 +

bsv n∑

k−1 bsk+1

· h2 + csvn∑ k−1

csk+1 .h3 was used to calculate the

similarity of two users. Among them, represented user v and user s similarity, and a(s, v) represented user v comments on the number of user s. bsv indi- cated the number of times the user v to the user s, and csv represented the share number of user v to users s. A, B, C, respectively, to comment, forward, share the rights of these 3 operations. A, B, C, respec- tively, to comment, forward, share the rights of these 3 operations. h1, h2, h3 represented respectively the rights of comment, forward, and share. Usually the similarity was proportional to the collective efficacy.

3.3. Recommendations based on group efficacy

Group performance recommendation behavior was a group of two or more than two groups of users to initiate projects and to recommend. For example, the

Fig. 5. Aggregation method of individual results.

originator of a social network launched an activity, such as the launch of tourism activities, the pur- chase of products or services and so on. Collective efficacy produced influence to willingness or behav- ior of participate in other group members. Group recommendation was based on the individual rec- ommendation, but it was not a simple addition of individual recommendation [17].

It can be used to study the collective efficacy of social networks through the establishment of the aggregated individual model. The main idea of this method was to aggregate the individual model into a group model, then it was suitable for the group and it was easy to produce the model of collective efficacy. However, the combination mode of this sin- gle may produce a reasonable group recommendation

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Fig. 6. Aggregating groups model method.

because each user group may be preferred to different, which twisted group user preference profile, thereby reduced the degree of satisfaction with the group of users for the recommended results and can’t play the effect of group efficacy.

When studying the group recommendation system, regardless of any of the two strategies men- tioned above, appropriate aggregation strategies are required. Different aggregation strategies can be applied to different scenarios, depending on the system and group requirements.

Computational social similarity: S ∈ Rnxn is used to show the user’s social network,

and n is the number of users. If the user U is concerned about the user V , then Suv = 1, otherwise Suv = o,. For the relationship matrix S ∈ Rnxn, the propagation attenuation coefficient is β. The propagation attenu- ation coefficient is the closeness between the users, and the attenuation ratio of each layer is increased. Assuming that the information of the user U needs g(u, v) times to be sent to the user V , and the user’s new social relationship matrix K can be expressed as:

Suv = βg(u, v) • U(g(u, v) − k) (1) In formula (1) U(x), when x ≤ 0, U(x) = 1; when

x > 0, U(x) = 0. After obtaining new social relation matrix of users through the formula (1), we can use cosine similarity to calculate the social similarity of user U and use V , and the formula is as follows.

Simsocial(u,v) = ⇀ u · ⇀v |⇀u||⇀v|

· (2)

Fusion social network similarity: The Pearson coefficient is used to calculate the sim-

ilarity between the two users, which can be expressed as follows.

Simrating(u, v) = ∑

i∈I (rui−r̄)(rvi−̄r)√∑ ieI

(rui−r̄)2 ∑

iel(rvi−r̄)2 (3)

In formula (3), Simrating(u, v) is the score similar- ity of user U and V , rui is the score of user U for the item I, and r̄u is the average value of the item scores by user U. The simplest linear relationship can be used to fusion score similarity and social similarity.

Sim(u, v) = α · Simrating(u, v) + (1 − α) · Simsocial(u, v) (4)

Whereas, 0 ≤ α ≤ 1. When α < 0.5, the social similarity plays a major role. When α > 0.5, the score similarity plays a major role. And when α = 0.5, the degree of similarity is equal to the social similarity. Integration of social networks and social similarity score similarity is made, and the fused similarity is applied in the group recommendation system based on collective, so as to improve the quality of group recommendation system, and enhance the sense of collective efficacy.

4. Study on the influencing factors of the group efficacy

4.1. The actual influence factors of the group efficacy

In reality, there are many factors that affect the efficacy of the group, which are summarized in the following. The first is the past achievements. The school’s average academic achievement is an effec- tive predictor of teacher group efficacy. The second is the sense of self-efficacy. Self-efficacy is an effec- tive predictor of group efficacy. The third is the key member. Group members often take into account the performance of key members of the group when they judge the effectiveness of the group. The fourth is the task scenario. Group interdependence may be another important predictor of group efficacy. But the com- munity in the real situation is very different with the network community. For example, the network com- munity can create different mechanisms for collective action, collective action network including the rela- tive indirect behaviors such as browsing information about local problems, and some direct actions such as

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government officials contacted by mail [18]. There- fore, these factors have also changed. In this research, the interview method was used to get the text that affects the network group efficacy, and then a series of analysis was carried on in order to explore the new influence factors in the network context.

4.2. Research design

As an exploratory research, the interview method was mainly used in this part to explore the influence factors of the network group efficacy. In this study, 60 members from four Internet groups were selected and interviewed. Each group had 10 members to accept the network interview, and in order to eliminate the interference of gender factors, especially 5 male and 5 female were selected. The whole process of the inter- view was recorded. After the interview, the recording data were collected under the guidance of experts, and the text information was extracted according to the root theory to analyze the data from the beginning to the end. After many discussions, the influence factors of the network group efficacy were summarized.

4.3. Structured interviews

The network groups for the structured interview include: flash mob; game teams in online games; group tour pal; some members of the tour pal group launched in micro-blog. Interview questions are as follows.

(1) The basic information of population statistics (gender, age, education, etc.)

(2) Are you in a network group with clear group goals? What is the name or type of the group? The number of members in a group? Group time and the time you join (for example, A: less than 6 months, B: 6∼12 months, C: 1∼2 years, D: 2 years or more)

(3) Would you please briefly describe the group goals of an action or task in your network group? Think about your current group, and select the appropriate number based on your feelings. (1 - not very agree; 2 - not consent; 3-disagree a little uncertain; 4- uncertainty; 5 - somewhat agree; 6- consent; 7 - quite agree)

Group efficacy: I believe that my group will be able to do a good job. (1–7 points); By work- ing hard, I am can complete the arduous task in the group. (1–7 points); I believe that my group can effectively deal with the problem of

sudden. (1–7 points); My group has the ability to complete the task. (1–7 points).

(4) Please specify the reason you feel in the prob- lem (3) (influence factors)

In order to guarantee the reliability of the open coding, the materials obtained from the interviews are coded and sorted out by the two codes. If there is inconsistent with the situation, they can discuss in accordance with the literature and related research. If it still cannot be resolved, the relevant experts will give advice. In the end, the primary encoding library with 15 entries is obtained.

A1: The perception of the characteristics of the promoter; A2: Perception of self-ability; A3: Cooper- ation with the members of the situation; A4: Member relationship closeness; A5: Leader’s personal ability; A6: The ability of a good team player; A7: Com- munication with others; A8: Similar identities and experiences; A9: The situation in the past; A10: Rela- tionship with other members; A11: The number of people involved; A12: Difficulty level of perceived task; A13: The perception of others and their own similar situation; A14: The time of group existence; A15: Judgment of team hardware condition.

In order to obtain the accurate and effective conver- gence model, 14 graduate students were found from the Business College of Tsinghua University, and they were randomly divided into two groups for the interview on issue. For example: members of the first group reached a consensus, that was, the “leader’s personal ability” and “outstanding members of the ability” was summarized as “key members”. Accord- ing to induction results of the two groups and experts’ guidance, the factors that affected the efficiency of network group were obtained finally, namely: past achievements; key members; self-efficacy; group properties (including interdependence scale, pop- ulation etc.); task; participate in communication; perceived similarity [19].

4.4. Research results

Combined with the literature review and research results, it is not difficult to find that the factors men- tioned above have been studied and verified in the previous several factors. The degree of participation in communication and perceived similarity are the unique factors in the network context. It is because of the changes in the field that makes the two new factors also contribute to the formation of group efficacy. In order to ensure the quality of reliability and validity,

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reliability test found some graduate and PhD who did not participate in the encoding to encode the text, and it was found the encoding similarity was 80%, so as to ensure the credibility of qualitative research.

5. Collective efficacy of social network based on multi factor analysis

5.1. Research assumptions

Interpersonal similarity can be defined as a lot of different dimensions. The operation of homogeneity (a term that means “the same hobby”, and sometimes with the perception of similarity) includes two dimen- sions, the background and the homogeneity of the attitude. In general, the homogeneity or the similarity of the attitude is more predictive than the evaluation of the source by homogeneity of the demographic characteristics [20]. However, social proximity (such as demographic data) can predict the similarity in another area (such as attitude) in a certain area. For this reason, the perception of similarity has a more significant predictive effect than that of the actual or objective similarity. The concept of interpersonal similarity actually used the way of expression of the attitude similarity, but it illustrated the potential com- monality, which was not only the attitude, or enduring belief. The following assumptions were put forward.

H1: Perceived interpersonal similarity of members of the network community has a significant positive effect on their behavior.

H2: Perceived interpersonal similarity among members of the network community plays a role in the psychological involvement and behavior of the community.

H3: The perceived similarity of the members of the network community has a significant positive effect on the formation of the group efficacy.

H4: The community members’ sense of com- munity efficacy plays a regulatory role in the psychological involvement and behavior of the mem- bers of the community.

5.2. Research design

The multi factor analysis method was fused in this study to find the measurement of each vari- able, and the dependent variable was the behavior involved. The corresponding independent variable was perceived interpersonal similarity. The depen- dent variables were initiative and focusing on sharing

information, and the corresponding independent variables were attitude, values and personality char- acteristics. Using the 7 point scale as the tool, after the adaptation, the pre-test and the re adaptation, the formal questionnaire was obtained. The subjects were 230 students from different schools. Questionnaires were distributed and recovered, and 160 valid ques- tionnaires were obtained. Among them, 78 were male and 82 were female. In order to ensure the credibility of the answer, participants used the anonymous way to answer questions.

5.3. Data analysis

We first used SPSS 20.0 to measure the various dimensions of the item for the exploratory factor anal- ysis, and calculated the weighted average number. Then the mean and variance analysis and Pearson correlation test were carried out on each dimension of each variable, and the correlation between the vari- ables was found to be more significant. The specific situation is shown in Table 3. Among them, the corre- lation of perceived similarity, behavioral involvement and psychological involvement was higher than 0.29, which fully explained the mediating role of psycho- logical involvement in the two.

In order to verify the hypothesis of H1, a linear regression analysis of perceptual similarity to behav- ior involvement was made in this paper. It was found that the correlation between the various dimensions was significant, and it was positively correlated with each other. The specific situation is shown in Table 2.

In order to verify the hypothesis H2, the results of an intermediary analysis are shown in Table 3. Because each variable had two or more dimensions, one of them was only one example. In general, psychological involvement played the intermediary role partly in mediating the relationship between perceived interpersonal similarity and behavioral involvement.

The same as hypothesis H1, hypothesis H3 was also a regression analysis of the causal relationship. Finally, it was found that the causal relationship between perceived interpersonal similarity and group efficacy was significant. Hypothesis H4 tested the moderating effect of group efficacy on psychologi- cal involvement and behavior involvement. We also took a dimension as an example, and made the data as in Table 4. It was found that the group efficacy only played a moderating role in the psychologi- cal involvement and the sharing of the information dimension.

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Table 1 Statistical properties of each variable and its correlation coefficient

mean standard attitude values personality attribution associated Initiative Share collective value deviation information efficacy

attitude 5.44 1.153 1 values 5.35 1.235 0.727∗∗ 1 personality 4.81 1.275 0.587∗∗ 0.609∗∗ 1 attribution 5.08 1.331 0.534∗∗ 0.541∗∗ 0.431∗∗ 1 associated 4.94 1.311 0.522∗∗ 0.456∗∗ 0.444∗∗ 0.835∗∗ 1 Initiative 5.24 1.310 0.522∗∗ 0.415∗∗ 0.290∗∗ 0.688∗∗ 0.686∗∗ 1 Share information 5.33 1.381 0.493∗∗ 0.474∗∗ 0.271∗∗ 0.755∗∗ 0.689∗∗ 0.794∗∗ 1 collective efficacy 4.97 1.203 0.499∗∗ 0.521∗∗ 0.427∗∗ 0.806∗∗ 0.778∗∗ 0.739∗∗ 0.770∗∗ 1

Table 2 Regression analysis of perceived interpersonal similarity on behavior

Dependent independent Non standardized value Coefficient R2 adjustment F variable variable coefficient significance R2

Behavior involvement Perceived interpersonal similarity Perceived interpersonal attitude 0.593 7.692 0.006 0.272 0.268 59.159∗∗∗

similarity Sense of worth 0.440 6.835 0.003 0.172 0.167 32.834∗∗∗ personality characteristics 0.299 3.815 0.000 0.084 0.079 14.553∗∗∗

Focus on sharing information attitude 0.590 7.119 0.000 0.243 0.238 50.684∗∗∗ Sense of worth 0.530 6.759 0.000 0.224 0.219 45.691∗∗∗ personality characteristics 0.294 3.542 0.001 0.074 0.068 12.544∗∗∗

Table 3 Regression analysis of psychological involvement in perceived interpersonal similarity and behavioral involvement (for example)

Model Model 1 Initiative Model 2 Initiative Model 3 Initiative Coef. T sig. Coef. T sig. Coef. T sig.

attitude 0.593 7.692 0.006 0.246 3.263 0.001 Sense of belonging 0.667 11.922 0.000 0.564 8.643 0.000 R2 0.272 0.474 0.507

Table 4 Regression Analysis of group efficacy (for example)

Model Model 1 Model 2 Model 3 Focus on sharing Focus on sharing Focus on sharing

information information information Coef. T sig. Coef. T sig. Coef. T sig.

Sense of belonging 0.783 14.45 1.000 0.395 4.738 0.000 0.851 5.955 0.001 Group efficacy 0.532 5.716 015 0.930 6.831 0.009 Interaction term –0.099 –3.848 0.005 R2 0.569 0.644 0.675 adjustment R2 0.567 0.640 0.669

5.4. Research results

According to the results of the data analysis above, we can get the following conclusions. Hypothesis H1 is verified, that is, the higher the degree of per- ceived interpersonal similarity, the higher the degree of involvement of the network community mem- bers. Hypothesis H2 has been verified, psychological involvement does play a mediating role between perceived interpersonal similarity and behavioral involvement. Hypothesis H3 is verified, that is, the higher degree of perceived interpersonal similarity,

the greater the sense of group efficacy, which is con- sistent with the results of the first exploratory study in this paper. Hypothesis H4 has been partially veri- fied, that is, group efficacy can positively regulate the information sharing behavior role of the psychologi- cal involvement; and in the process of psychological involvement playing the initiative role, the sense of group efficacy does not play a regulatory role. The reason may be that when people have a higher sense of efficacy of their own network community, they will be very confident in the ability of the group, so that they become lazy, and then make their own

P. Fan / Research on the collective efficacy of social networks 2835

initiative reduced. Generally speaking, the H1, H2 and H3 hypothesis of this study have been verified, and the H4 hypothesis has been partly verified. The model of this study has a certain theoretical contri- bution to the involvement of the network community, and it can be better in line with the actual phenomenon of society.

5.5. The application of collective efficacy of social networks

In reality, there have been a number of group buy- ing sites using the role of group efficacy to play the role of marketing, such as the Love Joint purchase website in Taiwan. It is a website platform to help users to purchase together. Through the joint purchase to satisfy the discount fill threshold and save freight, it is easy to buy the most value-added goods. “Unity is strength” is the slogan of this site. The customers rec- ommend good and inexpensive goods, share a large amount of consumer information, and find the most economical way of consumption. That is the best interpretation of the spirit of the joint purchase. In addition, for the network community that does not require too obvious cooperation behavior, it can make some manipulation, such as looking for their product positioning and interpersonal similar spokesmen to attract consumers, to foster the formation of group efficacy of members, so that the consumer is not just a bystander in the group, but to become a true actor.

6. Conclusions

With the rapid development of information tech- nology, the Internet has greatly promoted the change of people’s way of life. For some groups that require high degree of cooperation in the network situation, group efficacy can better predict people’s behavior trend, which has a more important significance in management and marketing. The collective efficacy of social network is studied by the method of estab- lishing the aggregated individual model. The social similarity is calculated and the similarity of social network is fused. The model has been applied to the group recommendation system based on collec- tive efficacy. First of all, the 15 factors affecting the group efficacy under network environment are explored by interview. Then through the multi fac- tor analysis method, the influence of the network community member’s group efficacy on the commu- nity involvement is studied, and the moderating effect

of group efficacy on psychological involvement and behavior involvement is found. It is concluded that the perceived interpersonal similarity of members of the network community is involved in their behavior, and the formation of group efficacy has a significant pos- itive effect on their social psychological involvement and plays a role in the social psychological involve- ment. The deficiency of the study is that the data is too little and the research process may be subjective. Practice has proved that the research on the collec- tive efficacy of social networks with multiple factors can contribute to the development of social network marketing.

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