In social structure literature, social capital has been proposed as one of the most useful concepts that provide theoretical and procedural perspectives to examine important features in the social context of the firm-consumer relationship [3] [20] . The firm cannot be studied within an independent context because its inception was built on social relations where every economic action undertaking and its corresponding outcome were rooted in the social structure. Drawing on this view, it is clearly understood that every economic activity is ingrained in social relations and it’s greatly affected by actors’ relations and the overall structure of the network of relations.

The conceptual foundation to stem up the definition of social capital seems blur due to the absence of unanimity in the different view-point of great thinkers such as [5] [21] [22] [25] [26] [27] [28] [29] , [30] who inspired most of the current works in social capital. However, based on the work of the aforementioned authors [4] definition of social capital gives an explicit picture of social capital as a “joint asset” that upsurges the efficiency of a community in the resources exchange that occur in it [25] . Furthermore, from a community point of view, it increases cooperation, adjustment and the productive value of social connection for the mutual benefit of its members relevant to the objective of the social organization [31] [32] [33] .

Conventionally, firms were assumed to be linked to its consumers by an arms-length relationship and pursue self-interested and profit-seeking agenda without any explicit consideration for firm-consumer relations. Any ongoing relationship existing between them was believed to be profit-seeking motivated [16] . [29] , couldn’t have put it better by stating that firms are not just made up of many discrete units that make self-centered decisions constrained by scarce resources anymore, but rather, how embedded they are in a network of relations determine their strategic conduct.

Examining the relationship of social capital on the creation of intellectual capital by [4] , clarified the dimensions of social capital as: structural, relational and cognitive. The author’s discussion on the structural and relational dimensions of social capital was mostly based on the argument of [3] [16] . Where the structural dimension described the tie architecture and exchange pattern between and among actors within a network which is mostly characterized by the size of the network, density, centrality, frequent communication. The relational dimension emphasized personal relationships developed by actors through a history of interactions with important features such as trustworthiness and trust [34] [35] . The last dimension of social capital, cognitive, focuses on the system of meaning-shared representations and interpretations with focal features of shared code and language, and shared narratives [4] [36] [37] .

Based on the production-oriented perspectives, the idea of value creation can be traced back to the early 20^th century industry principle, where the value chain concept introduced by Michael Porter in the 1980s gave the firm an integrated framework to identify, manage the cost of designing, producing, marketing, delivering and supporting goods and services. Value creation was predominantly the responsibility of the firm which occurred inside the firm through its activities in that the firm strongly believed its competitive edge of ability to innovate and placing more emphasis on value delivery [38] [39] [40] .

As showed by [41] , value is internally produced and distributed in the marketplace, through the exchange of goods/services, and money, and without any direct interference by customers. This conventional concept about value creation can be identified as the backbone of the goods-dominant logic (G-D logic) view, where value is embedded into the product of economic exchange during the firm’s production process and is delivered to be sold. The value of the good signifies the market price the consumer is willing to pay.

Literature is silent on the actual definition of value creation and blurs on the distinction between value creation and value co-creation [42] [43] . The concept is generally treated as co-creation, emphasizing the inclusive activities by both the firm and the customer [40] [43] [44] . Its explanation has a strong association with the service-dominant logic (SDL) which states that always, value is co-created with the client-customers role is to be the value creator and perhaps also a value co-creator [39] . Viewing value creation and co-creation from the SDL put the firm in charge of value creation and the consumer is called to join in the whole activity of creating as a co-creator [45] . This leads to a web of interconnections with consumers and other stakeholders where the “we” proposition becomes much broader and powerful than just “I” and “you.” in value creation [46] (Figure 1 and Table 1).

In examining the relationship among parties involved in value creation, the concept of social capital make available valuable and extensive view for understanding the role of its dimensions on consumer involvement in value creation [50] [64] . According to [4] , the concept is central to the understanding of institutional dynamics, innovation and most importantly value creation. Reviewing

related works on consumer engagement, authors such as [60] [65] [66] [67] claimed that consumer engagement is made up of both transactional and non-transactional behavior exhibited when consumers are in interaction with a brand, organization and/or co-created customer experience with other stakeholders. The value of social capital on consumer engagement is evident in the consumers’ willingness to share experiences, competencies, and expertise, exchange tacit information, demonstration of higher disposition to take risks, and having greater openness to create value developed through trust, close bonding and affinity with others within the social structure [2] [15] [20] [52] [60] [64] [68] [69] . The bond or connections that generate among these exchange partners (consumers) within a social structure serve as a drive to gain complementary know-how and/or speed up R&D processes to accomplish co-creation task [70] . Literature is explicit on the assumption that similarity of partners participating in the same event is beneficial for idea generation, speedy dissemination of information among partners thereby enhancing efficiency and cooperation that support value creation [51] . The network partners involved can create value through their engagement via frequent interactions to motivate and create solidarity benefits [14] . According to [2] , solidarity benefits and valuable information supporting value creation are associated with strong ties as opposed to the argument of both [16] [64] that new and valuable information to create value occurs by bridging weak ties. Supported works from [57] [71] indicates that successful transfer of new information via stronger ties are possible because of structural bridging qualities and the willingness of actors to cooperate due to relational qualities. Again, [72] , shows that the strength of strong ties in the creation of value for consumers makes it possible to combine the solidarity benefits of strong ties with the new and valuable information generated and the benefits of weak ties in value creation. While Coleman’s and Burt’s views on social capital are seen differently because they analyze different features of the social system, both views complement each other in that strong ties (quality of relationships) with

weak structural positions (structural holes and network position) bring to light the most intriguing value creating ideas with the most ground-breaking solutions. Information from one partner to another is directly influenced by the strength of their relationship. However, the value of the information may be higher if it is received from weak ties. In substantiating [52] [60] arguments, [64] , stated that network ties preferably strong ties have a stronger association with value creation because information moves faster and is more credible due to its rapid and reflected actions. Implying, weak network configuration without strong ties have no value: they lead to no action.

DEMATEL is one of the most widely accepted and beneficial techniques to measure and analyze structural models relating to cause-effect relationships and the interdependence among complex testing criteria [73] . These relations among the evaluating criteria is supported by numerical values representing the strength of influence. The technique aids in understanding the structural relations among elements and help solve complicated system problems [74] .

In the sphere of human activities of decision making and management characterized by uncertainties and complexity, it is important that suitable established models are chosen in accordance with the problem at hand so that an in-depth analysis into the nature of the problem can be conducted. This makes DEMATEL the appropriate model to be used since it works with specific features of objective affairs to confirm the relationship and ascertain the interdependence among variables [75] . DEMATEL has been applied in many areas including supply chain management [76] [77] [78] [79] , Education [75] [80] [81] , Health [73] [82] , innovation policies in small and medium-scale enterprises (SME’s), barriers to entrepreneurship and facility prioritization [83] [84] [85] . Moreover, the DEMATEL technique has been used in combination with other methods such as fuzzy, failure mode and effects analysis (FMAE), technique for order preference by similarity to ideal solution (TOPSIS), analytic network process (ANP) and analytic hierarchy process (AHP), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for evaluating the spare-part industry [80] , reliability allocation [86] , e-learning evaluation [87] , product concept evaluation [88] and innovation support system for higher education [89] . As part of this study, the DEMATEL was used to determine the multifaceted relationships and the level of influence each element has on others.

Building on the opinions of ten experts, ten (10) sub-dimensions of social capital were analyzed: network ties (a₁), size of the network (a₂), density (a₃), social interaction (a₄), structural holes (a₅), trust (b₆), frequency of interaction (b₇), commitment (b₈), shared codes and language (c₉). Shared narratives (c₁₀). Then, consumer engagement (d₁₁), and co-creation task (e₁₂). The experts were called on to examine the level of relationship these sub-dimensions have on consumer engagement to a co-create task. The experts included university professors, social network analyst and Ph.D. candidates whose research area are centered on the study in question. The formulating steps of the technique summarized as follows:

Step 1: Scale definition.

The influence scale is defined to show the impact level of the criterion. For this study, the values for the influence score ranges from 0 to 4. Where 0 = no effect, 1 = low effect, 2 = moderate effect, 3 = high effect and 4 = very high effect.

Step 2: Construct a relationship matrix

An assembly of experts and factors were used in this step. Each individual assessor is requested to evaluate the level of direct influence between two criteria in a pair-wise comparison including the consumer engagement and the co-creation task. In so doing a direct-relation table was created which was symbolized as xij signifying the degree to which the experts view factor i effects on factor j. All the diagonal elements are zero (0).

A = [ 0 a 12 a 13 ⋯ a 1 n a 21 0 a 23 ⋯ a 2 n a 31 a 32 0 ⋯ a 3 n ⋮ ⋮ ⋮ ⋱ ⋮ a n 1 a n 2 a n 2 ⋯ 0 ]

Afterward, the judgments from experts are all aggregated to attain the average matrix A = [a_ij] indicated in Table 2. The aggregated matrix A was built using Equation (1).

A i j = 1 e ∑ i = 1 e   a i j m (1)

where, e = total number of experts, m = individual expert.

Step 3: Normalise the matrix.

In applying Equation (2) and (3), a normalized matrix A was constructed on the bases of the column vector and the maximum value (Table 3).

Source: Own study.

Source: Own study.

Q = 1 max 1 ≤ i ≤ n ( ∑ j n a i j ) (2)

N = Q X (3)

Step 4: Construct a total relations matrix.

Total relations matrix T was constructed by using Equation (4). By doing so, the overall strength of influence and effect from other criteria are determined.

T = D + D 2 D 3 + D n = D ( I − D ) ( − 1 ) , n → ∞ (4)

Step 5: Compute the sum of the rows and column in matrix T, to obtain the total values for C and R using Equations (5) and (6). Where C signifies the level of direct or indirect effects on other criteria, and R, the level to which it is affected by other criteria: (Table 4)

C i = ∑ j = 1 n t i j (5)

R i = ∑ j = 1 n t i j (6)

T = [ t i j ] ,     i , j = ( 1 , 2 , 3 , ⋯ , n )

Step 6: Compute a threshold value by averaging the elements in matrix T. This is to help do away with minor effects in the matrix T elements with the intention of minimizing the complexity of the impact relations-map. If the influence level of an element in the matrix T is higher than the threshold value then the element is included in the influence relations map (IRM) (Table 5, Table 6 and Figure 2).

Source: Own study.

Source: Own study.