With advances in information (digital) technologies and people’s increasing proficiency with the internet, organizations are creating online communities for customers to engage with them and with each other [1
]. An online community serves as a way of connecting a brand to customers. When a brand is the focal point of an online community, the community is labeled an online brand community (OBC), where consumer groups gather and communicate to achieve personal and group goals [3
]. OBCs have been growing exponentially since 2004, and almost half of the top 100 global brands have established their own virtual communities [6
]. This popularity is unsurprising given the benefits of OBCs for both marketers and consumers. For marketers, an OBC can work as a versatile brand-building tool for creating, customizing, and distributing persuasive advertising messages [7
]. At the same time, an OBC provides consumers a platform to communicate, share meaningful consumption experiences with each other, and develop a sense of belonging [8
Member participation has been identified as a key driver of brand-community performance [10
]. Some studies have been conducted from the perspective of consumer motives. However, once people participate in OBCs, the question is then what information technology could provide to ensure a good experience. Climate traditionally refers to weather statistics over long periods of time, but the concept is widely used in the field of organizational behavior. Therefore, community climate in our study is regarded as how a community communication environment is perceived and interpreted by participants. Inspired by management research, we distinguish two types of community climate: controlling and supportive [11
]. Although research has revealed that organizations with a supportive climate encourage members to participate, exchange their information, and practice constructive conflict resolutions openly and freely [12
], participants may perceive the community to be unsafe without proper control [13
]. However, little research has examined the effect of a controlling climate in an OBC context. Therefore, a theoretical question worth pondering is whether a controlling climate is necessary in OBCs and how controlling and supportive climates influence community relationships.
We draw on and further extend social capital, organization climate and organization inertia theories to develop a conceptual framework that seeks to understand the affective influence of controlling and supportive climates on community relationships in OBCs. To advance this line of research, we propose that participants’ relationship with OBCs is determined not only by a supportive climate but also by controlling climate. Specifically, we expect that the impact of both types of climate on community identification is mediated by social capital and that community age plays a moderating role. Social capital draws on more physical forms of capital metaphorically to show the value of networks of relationships and trust. Given that OBCs can be viewed as networked connections among community members [4
], a reasonable step is to propose that social capital is the underlying mechanism behind the relationship between community climate and community identification.
The contributions of our research are fourfold. First, we provide a conceptual framework which postulates the outcomes of both supportive and controlling climates in an OBC context. Second, this study highlights the positive role of a controlling climate, which management theories view as negative for organizations as it may limit participants’ ways of participating, interacting and sharing [14
]. Third, we contribute to OBC theories by taking a network view on the formation of community identification. Specifically, we investigate social capital as a mediator linking the OBC constructs. Fourth, this study contributes to organizational inertia theory by exploring the moderating role of community age. Both controlling climate and supportive climate are found to be more effective in facilitating community identification in older communities characterized by inertia than in younger communities.
4. Research Method
Data were collected from So-jump (www.sojump.com
acccessed on 1 July 2015), a professional online survey network consisting of 2,600,000 members in China [62
]. China was selected as the setting of this research because it has a huge e-commerce sector and large number of OBCs and participants. Before the core variables were measured, the subjects were asked to write down the names of the brands and brand communities that they followed and to describe their association with these brand communities. The subjects of this study included only members of brand communities. A total of 946 members submitted their responses, but 465 respondents provided invalid answers. A filtering question was used to identify non-members of OBCs, who were not included in the data analysis. Those giving answers in less than 3 min were not included, as using less than 3 min to answer pages of questions usually indicates careless [64
], skimming details [65
], and insufficient time spent [66
]. Finally, 481 questionnaires were selected for analyses. Industries involved in the test included the mobile phone industry (for example, Apple, Samsung, and Nokia), the automobile industry (for example, Audi, BMW, and Honda), and the clothing industry (for example, Nike, Lining, and ONLY). Table 1
provides the demographic characteristics of the valid sample.
The measures and their validity assessments are shown in Table 2
. Chinese participants often select the neutral point on a five- or seven-point Likert scale [35
]; thus, we used a six-point Likert scale ranging from 1 (completely disagree) to 6 (completely agree) [67
We adopted existing measures to capture trust, norms of reciprocity, and brand community identification. A scale was developed for measuring the new construct, controlling climate
, which indicates that members should develop a powerful sense of functional self-control in OBCs, as Churchill [69
] suggested. First, two items based on the literature related to controlling climate [70
] and one item based on observations of several brand communities were used to form the scale. Ten marketing professors were invited to provide comments on these items, which provided guidelines for the revision of the scale. After the first author completed the Chinese language translation of the measurements, back translation helped ensure scale accuracy. Items were dropped with factor loading below 0.5 or if there was an item-to-total correlation below 0.4.
was measured by adapting Rogg et al.’s [71
] measurement scale with seven items. Three items adopted from the literature were used to measure trust
]. Norms of reciprocity
imply actions contingent upon rewarding reactions from other community members and cease when the expected rewarding reactions are not forthcoming, and it was measured by a two-item scale [34
]. Brand community identification
was measured by six items [35
]. Community age
was measured by asking respondents to describe their perception of the online brand community’s length of history, from very short (coded as 1) to very long (coded as 5).
Four demographic variables were examined and controlled. Respondents were asked to indicate gender
with two options (male, scored 0; female, scored 1). Age
was measured by using the age range from 1 (≤20) to 4 (>40). Education
was assessed by asking education levels from 1 (high school or below) to 3 (postgraduate or above). Income
was captured by collecting monthly income (before tax) information from respondents (see Table 1
5. Data Analysis
The conceptual framework was assessed using the partial least squares techniques with SmartPLS 3.0 and bootstrapping with 5000 samples. PLS is robust against non-normality [73
], and can hence maximize the explained variance. Furthermore, SmartPLS 3.0 includes additional analyses such as HTMT [74
We checked the constructs’ reliability and validity (see Table 2
). The Cronbach’s α levels of these items were all above 0.70 (α > 0.70). The composite reliabilities (CR) of all four constructs exceeded 0.86 (CR > 0.70), so measurement items had sufficient reliability. Overall, the model fit indices (χ2 = 945.837, SRMR = 0.058; dULS = 0.637; dG = 0.302; NFI = 0.825) were satisfactory. All average variance extracted (AVE) values were above 0.60 (AVE > 0.50), and the square root of the AVE of each construct exceeded the correlation coefficients between it and the other constructs [75
The data were self-reported. Thus, the issue of common method bias may exist. First, the results of the Harmon one-factor test [76
] indicated that the four extracted factors explained 74.29% of the total variance, and the largest variance explained by an individual factor was 23.15% (EV < 50%). In line with Henseler et al. [74
], the heterotrait-monotrait (HTMT) ratio was evaluated. Table 3
shows that the HTMT ratio was less than 0.90. Therefore, common method variance was not a problem in our data [77
The structural model predicted 50 percent of the variance in trust (TR), 52 percent of that in norms of reciprocity (NR), and 58 percent of that in community identification (CI). Given that the proportion of variance explained exceeded 10 percent, the model has sufficient predictive power. In addition, none of the control variables exerted a significant effect on the model’s endogenous constructs.
shows correlation coefficients among the constructs in our conceptual framework. All coefficients were below 0.75. Model 1 is the baseline model including main effects (Table 5
). The path analysis showed that the controlling climate (CC) had a significantly positive effect on TR (β = 0.105, p
< 0.05) and NR (β = 0.189, p
< 0.001), supporting H1 and H4, respectively. Supportive climate (SC) had a significantly positive effect on TR (β = 0.638, p
< 0.001), NR (β = 0.290, p
< 0.001) and CI (β = 0.253, p
< 0.001), supporting H2, H5 and H9, respectively. TR was positively related to NR (β = 0.373, p
< 0.001), supporting H6. TR (β = 0.376, p
< 0.001) and NR (β = 0.193, p
< 0.001) also had a significantly positive effect on CI, supporting H3 and H7, respectively. The effect of CC on CI was not significant, so H8 was not supported.
Moderating effects were tested with models 2 and 3 (Table 5
). The interaction between community age (CA) and CC had a positive and significant effect on TR (β = 0.237, p
< 0.05), supporting H10a. The interaction between CA and SC also had a positive and significant effect on TR (β = 0.233, p
< 0.05), supporting H10b. However, the interaction between CA and CC did not have a significant effect on NR. The interaction between CA and SC had a negative effect on NR, as hypothesized, and the t-value was close to the 1.96 threshold (β = −0.165, p
< 0.10). Therefore, H11a was not supported, and H11b was partially supported.
The mediating effects were assessed using Sobel’s test. As shown in Table 6
, significant partial mediation effects of TR on the CC-CI, SC-CI, CC-NR and SC-NR relationships existed. NR partially mediated the CC-CI, SC-CI and TR-CI relationships. Therefore, social capital (trust and norms of reciprocity) exerted a partial mediating effect on the relationship between community climate (controlling and supportive) and community identification.
By drawing from social capital, organizational climate and organizational inertia theories, we proposed a new conceptual framework to obtain greater insights into the OBC climate. Overall, our data support the conceptual framework which depicts the critical role of controlling and supportive climates for promoting community identification in OBCs.
As shown in Figure 1
, controlling and supportive climates, trust, norms of reciprocity, and community identification are significantly and positively related. Controlling and supportive climates act as external stimuli that affect members’ perceived trust and norms of reciprocity and then drive members to identify with OBCs. Without proper control, a risk exists for members in OBCs [18
]. The results provide further evidence that a controlling climate may promote community relationships. The findings suggest that controlling and supportive climates not only contribute to perceived community trust and perceived norms of reciprocity, but also can lead to community identification. Furthermore, social capital (trust and norms of reciprocity) exerts a partial mediating effect on the relationship between the community climate and community identification in OBCs.
While controlling climate has been viewed as a negative management option [14
], the findings of our study confirm the idea that providing support and executing control are both effective to build trust in long-established communities. However, these two approaches seem to have similar influence on norms of reciprocity for both long-established and new communities, as H9b and H10b were not supported in the findings. The reason could be that norms of reciprocity are not diluted over time, while trust may diminish when OBCs become aged and the platform fails to keep promoting interactions among members [54
In OBCs, members can join voluntarily, and they are free to change their experience. According to previous research, if an OBC can provide a supportive climate where members freely communicate their feelings and opinions, then members will have a more positive attitude toward the community and thus have a higher level of engagement [77
]. According to Chan et al. [79
], perceived community value and perceived system support have positive relationships with customer engagement in OBCs. Swear words, advertisements, or irrelevant information may appear without a proper controlling climate. Therefore, controlling OBCs is necessary; however, the type of control needed is different from simple, technological, or bureaucratic control. In OBCs, a powerful sense of self-control is developed among community members based on their common values, and a set of behavioral standards is established for self-management [21
]. Therefore, the controlling climate of an OBC is based on concertive control, and it prompts members to understand what is expected of them and what their duties are in the OBC [19
The results of the data analysis indicate that perceived community trust exerts strong positive impacts on perceived norms of reciprocity. This finding is consistent with the findings of extant studies [20
]. However, in contrast to our proposed hypothesis, some research focuses on the role of norms of reciprocity in building trust, which therefore is critical to social exchange relationship. Chiu et al. [20
] found that social interaction ties, norms of reciprocity, and identification indirectly influence knowledge quality through trust. However, in OBCs, members are anonymous, and transient exchange with strangers is often risky [80
]. In addition, risks and uncertainties may weaken members’ trust. Without perceived community trust, members will neither adopt other people’s advice nor share their opinions. Therefore, we proposed and tested the impact of trust on norms of reciprocity in OBCs.
7.1. Theoretical Implications
OBCs promote both customer-brand communication and inter-member online interactions. A controlling climate in OBCs not only affects members’ social capital but also influences their relationships within these communities. Thus, the exploration of how a controlling climate influences community relationships in OBCs is an important research issue.
First, the findings contribute to the online community climate literature. OBCs provide a platform for members to share ideas, information and experience. However, research has mainly focused on supportive climates and neglected the effect of controlling climates. Our study examines the impact of controlling climates on community relationships in OBCs. The findings suggest that building and maintaining a controlling climate is an important approach to boost community identification.
Second, we applied concertive control to OBCs, and the findings validate controlling climate as an important factor that influences community relationships. Unlike simple, technological, or bureaucratic control, concertive control is useful for self-managed teams. In OBCs, members join voluntarily, and they are free to change their experience. Therefore, OBCs are self-managed teams, and a powerful sense of self-control develops among community members [21
]. The current study thus provides important insights for understanding OBCs.
Thirdly, we identified the boundary conditions of the climate-trust association. Building supportive climate is not effective in boosting trust for all OBCs. Working with supportive climate is effective to make members trust each other particularly for long-established communities. While a recent study on online community leadership suggests that greater management efforts are needed to build inter-member trust [54
], our findings indicate that the efforts in controlling member behavior in OBCs are less effective for new communities than for long-established communities.
Finally, we extend the social capital literature in two aspects. First, studies indicate that a supportive climate may promote social capital [77
]. The empirical results of this study reveal that social capital (trust and norms of reciprocity) exerts a partial mediating effect on the relationship between community climate and community identification in OBCs. Second, studies show that norms of reciprocity have a positive effect on trust [20
]. Our findings indicate a different logic that suggests that trust can drive the norms of reciprocity in OBCs.
7.2. Managerial Implications
OBCs with many active brand enthusiasts are an efficient channel for providing effective and timely access to product and brand information, and they are valued by a large number of members. The results of our study show that a controlling climate can improve perceived community trust and norms of reciprocity among members, thereby leading to a high level of community identification. Therefore, building and maintaining a controlling climate are important approaches to boost community identification in OBCs.
However, the controlling climate of online communities is different from that of conventional organizations. In most conventional organizations, people are forced to work together, and simple, technological, or bureaucratic control is adopted. OBCs provide a social platform for users to share opinions, information, emotions and experiences [4
]. If members perceive too much control over the content and expression, then the level of participation and interaction among members might diminish [79
]. Therefore, the controlling climate of an OBC is based on the common values of members, and a set of behavioral standards should be established for self-management [21
]. In other words, concertive control should be adopted in OBCs.
Companies or community managers should pay specific attention to stimulating members’ emotion in promoting the community [39
]. Members would like to share their brand experiences and build close relationships with other members in OBCs. Perceived community trust and reciprocity may strengthen their sense of belonging and responsibility and improve the relationships between members and communities.
7.3. Limitations and Further Research
Several limitations pertaining to this study suggest directions for further research. First, the conceptualization of the online community climate is still in its infant stage and has been debated among researchers [20
]. Future research could explore or re-test the controlling-supportive climate typology by employing both types of climates at the same time. Second, to accurately capture the association between perceived community trust and norms of reciprocity, a better research design can comprise a time-series analysis across different periods. Future research can employ a longitudinal design to test the causality between trust and norms of reciprocity. Finally, the sample context, which is China, limits the generalizability of this study. Recently, the number of OBCs and members has been increasing around the world. Therefore, future studies can be conducted in other geographic settings to gain insights from cross-culture variations.