Introduction

The construction sector is a vital pillar of the Indian economy, accounting for 9% of national gross domestic product and generates formal and informal employment opportunities for around 35 million people. India’s construction sector is projected to grow steadily at a compound annual growth rate of 6% between 2019 and 2024. Commercial and residential building projects are estimated to account for 30.1% of the industry’s total value by the year 2023 [1, 2]. In their study with 50 construction firms across India, Iyer and Jha found that owners and contractors rank negative conflicts between project participants as the main factor impeding project cost and schedule [3]. In India, construction conflicts tend to arise due to varying effects associated with poor financial management, changing government policies, poor project management, mistrust between project participants and other social issues. In recent years, delays in publicly funded construction projects due to unresolved conflicts have also become a source of dissatisfaction among the general public, even casting doubts of corruption [4,5,6]. It is worth noting that construction projects in developing countries such as India tend to be less mechanised and more labour intensive as compared to developed countries in the west [7]. The review of existing literature on construction project management reveals that the majority of studies are performed in the context of developed countries. Owing to many distinct differences in construction practices and socio-political environment between developed and developing countries, there is an obvious need for studies undertaking focussed research in developing nations to identify specific success and failures attributes of their construction sector.

It is possible to characterise construction project performance using certain objective and subjective criteria. Objective criteria include measurable goals such as schedule, cost quality and safety performance, whereas subjective criteria include satisfaction levels of the client, contractor, project management team and the project end-users [8]. The attainment of these objective and subjective performance criteria depends on multiple project stakeholders who are expected to cooperate and efficiently coordinate material, machinery, workforce and capital. In many cases, it is seen that one or more parties involved attempt to maximise their utility from the project at the expense of other stakeholders resulting in the occurrence of conflicts. If such negative conflicts are not resolved harmoniously at early stages of disagreement, they may get converted into psychological struggles and manifest as painful disputes. Disputes can be very disruptive for the working environment, especially if settled using litigation procedure in courts and tribunals [9]. Painful experiences of litigation tend to damage business relations severely, ending all possibilities for future collaborations. Conflicts are regarded as a major source of inefficiency during construction projects limiting their utility and productivity, India being no exception [10,11,12,13,14,15].

To improve the performance of Indian construction sector over all three corners (schedule, cost, and quality) of the project performance triangle, it is crucial to identify and control the source of conflicts between various project participants [16]. In response, this paper identifies and ranks the critical causes of conflicts for public and private building construction projects in India using available literature and expert judgements. Fuzzy Analytical Network Process (f-ANP) method is employed for deriving the local and global priority weights for the sixteen conflicting factors based on their relative influence towards the occurrence of conflicts. It is essential to highlight that this paper has exclusively assessed the various factors responsible for negative conflicts between project stakeholders. Based on the findings, suitable interventions and mitigation strategies have been discussed to minimise the occurrence of negative conflicts during different phases of construction projects. The results of this study shall be useful for construction professionals in India and other developing countries for developing robust conflict mitigation strategies.

Construction Conflicts

The critical questions which most existing studies have endeavoured to address are why do conflicts arise in the first place; their impact on the project outcomes and what are the ways through which conflict can be managed efficiently [17,18,19,20]. Collins et al. define conflict as a serious disagreement due to different ideologies, beliefs or interests of the parties involved [21]. As per risk, conflict, claim, and continuum model developed by Acharya et al., unclear assignment of project risk is the principal factor behind most disagreements and conflicts [22]. As shown in Fig. 1, conflicts not managed proactively at early stages of disagreement can convert into disputes between the contracting parties.

Fig. 1
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(Adopted from [22])

Risk, conflict, claim and dispute continuum model

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As per conflict continuum model developed by Moore, cost and effort involved in resolving conflicts increases with each advancing stage of disagreement and the adoption of arbitration and litigation results in substantial time and cost overruns [23]. A few studies have also highlighted positive aspects of constructive conflicts [8]. For instance, Yiu and Chung discuss the positive aspects of conflict using a conflict behaviour model with tension level and behavioural flexibility as its control variables. As per this model, conflicts are positively correlated with tension level among the project team members subject to the moderating effect of behavioural elasticity shown by other team members. At higher behavioural elasticity, conflicts can be collaborative, cooperative and problem-solving but under low elasticity, conflicts distress the working relationships and project outcomes [24]. Similarly, Wu et al. assessed the functional roles played by conflicts during mega construction projects in China. It was found that fairness perception between team members can stimulate the occurrence of positive task conflicts enhancing the final project outcomes [25]. In contrast, process and relationship conflicts were found to negatively impact project performance. The optimal approach for minimising negative conflicts involves the early identification of all likely sources of disagreement to develop a robust conflict mitigation plan [26].

Sources of Conflicts

Several studies have reported the major causes of negative conflicts occurring in construction projects across different countries [27]. Mitkus and Mitkus analysed the causes of conflicts between client and contractor using a communication based approach. It is found that contract documents allowing room for subjective interpretation, unfair behaviour and psychological defence mechanism of project participants are the major sources of negative conflicts during construction projects [28]. Based on the comparative assessment of 24 construction disputes across USA, Mitropoulos and Howell identified project uncertainty, contractual problems and opportunistic behaviour as the most common conflicting factors [29]. In Hong Kong, Kumaraswamy classified conflicting factors under owner, contractor, design, contract, human behaviour, project related and external factors. Contractual matters such as clauses for extension of time, availability of information, quality of technical specifications, administration and management, unrealistic client expectations and determination were identified as the most common reasons behind construction conflicts [30]. Mahamid reported the non-adherence of subcontractors to project schedule, contractor delay in progress payments, frequent change orders by the owner as the significant sources of conflict for construction projects in Palestine [31].

Illankoon et al. assessed the factors responsible for disputes and the selection of an appropriate dispute resolution (ADR) method for construction projects in Sri Lanka. Failure to properly administer the contract was found to be the most important conflicting factor, and negotiation was recognised as the most effective ADR method by the various stakeholders [32]. International projects also suffer from conflicts due to unfamiliarity of foreign companies with the business practices of the host country. Al-Sibaie et al. identified internal and social conflict as the two primary disruptors for international construction projects in Malaysia [14]. Chan and Suen identified the absence of familiarity with the local culture and ways of doing business in China as a chief contributor to disruptive conflicts arising between foreign companies and local authorities [33].

Analysis of Construction Conflicts

Several research methods have been employed to investigate various conflicting factors occurring in construction projects by using primary and secondary data [34,35,36]. Roxene et al. used a case study approach to compare the conflict resolution process used in United States and the United Kingdom. It was found that conflicts can be resolved by encouraging people to communicate more and cooperatively deal with issues at earlier stages without any external adjudicator [37]. Using available literature, Cakmak and Irlayici deployed analytical network process to rank different causes for construction disputes [38]. Several studies have used Likert scales for ranking conflict related issues. For instance, Peansupap and Cheang used a 16 item survey based on a five level Likert scale to rank various cost related change issues responsible for conflicts between clients and contractors in Cambodia [39]. Similarly, in West Bank Palestine, Mahamid used a Likert scale survey for ranking conflicting factors that negatively affect contractor–subcontractor relations [31].

Onsite interviews have also been used to analyse the conflict perceptions of various project participants. In USA, Brockman used critical incident technique to analyse triggers and consequences of interpersonal conflict and their associated impact over project financial costs. Audio transcripts were analysed to calculate the financial implications of interpersonal conflicts [40]. Wu et al. used structural equation models to analyse the influence of trust-conflict interactions on project added value. Trust was divided into calculative trust and relational trust. Task conflicts and relationship conflicts were found to be positively and negatively correlated with the project added value, respectively [41].

Based on their broader relevance with ongoing building construction projects in India, thirty-two possible conflicting factors are identified from available global literature and reported in Table 1. Conflicts can be modelled as the net outcome of positive and negative interactions between one or more underlying factors. It is crucial to capture these interactions, which may occur in a linear or non-linear fashion. Network based models are suited for solving such non-linear problems. One such popular network model was developed by Saaty known as Analytical Network Process (ANP) for solving decision problems involving dependency and feedback. ANP does not assume independence between interacting factors and calculates the final priorities after analysing the direct and indirect interactions and their impact on the goal or control criteria [42]. ANP has been used successfully for solving many multi-disciplinary decision-making problems [43,44,45,46,47,48]. The classical implementation of ANP can suffer from issues such as uncertainty, bias and vagueness. Fuzzy sets are integrated with classical ANP to solve these challenges. Fuzzy ANP allows decision-makers to use simple linguistic expressions to compare different model elements for solving complex problems [49,50,51].

Table 1 Causes of conflicts occurring in building construction projects
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Method and Materials

This study employs a fuzzy ANP model to develop priority weights for various causes of conflicts occurring in building construction projects in India. A two-stage research methodology is developed for this study. An expert survey based on a five point importance scale is conducted to identify relevant factors for building construction projects in India. Twelve experts from public and private sector construction companies with more than ten years of experience in building construction projects across India participated in the survey. The specific details for the twelve field experts are provided in Table 2. Data reliability is ensured by calculating the Cronbach alpha score (α = 0.81), which is found to be higher than the minimum requirement (α = 0.75) [52]. Sixteen factors are selected and categorised into contractor related, client related, consultant related and miscellaneous factors to develop a network structure. The second stage involves the application of the F-ANP model using the following steps.


  • Step 1 Problem Structuring and Model Construction

Table 2 Professional details of the twelve respondents contacted for the expert surveys (The original version of questionnaire has been after Conclusions)
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The first step involves breaking the decision problem into an appropriate network structure with goals, categories and sub-factors placed in their appropriate clusters and hierarchies. Figure 2 illustrates the network structure with causes of conflicts (goal) placed at the top level with categories and factors placed at lower levels.

  • Step 2 Pairwise comparison matrices of interdependent problem elements

In this step, experts are asked to perform pairwise comparisons between all elements of the network structure using linguistic scales equivalent to triangular fuzzy sets (TFS) as illustrated in Table 3 and Fig. 3. TFS are useful for modelling vagueness and uncertainty associated with human judgements as they offer flexibility to decision-makers to choose their preferences from intervals instead of fixed values [44, 50]. A TFS is denoted as ã = (l, m, u) where l, m and u denotes the lowest possible, most probable and the largest possible values, respectively. Equation 1 describes the membership function of a TFS. Reciprocal TFS are represented as \(\frac{1}{{\tilde{a}}} = \left( {\frac{1}{u},\frac{1}{m},\frac{1}{l}} \right)\).

$$u\left( x \right) = \left\{ {\begin{array}{*{20}c} 0 & {x < 1} \\ {\frac{{x - 1}}{{m - 1}}} & {l < x < u} \\ {\frac{{u - x}}{{u - m}}} & {m < x < u} \\ 0 & {u > x} \\ \end{array} } \right.$$
(1)
Fig. 2
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Network structure for the causes of conflicts occurring in building construction projects across India

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Table 3 Linguistic responses and their corresponding triangular fuzzy sets
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Fig. 3
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Membership function for the triangular fuzzy sets used for pairwise comparisons

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All pairwise comparison between factors and sub-factors are carried out based on their relative importance towards the goal. A response of “Equally Important” is assigned whenever both elements under comparison are perceived as equally important. In contrast, a response of “Absolutely Important” is assigned when the first factor is perceived to be significantly more important than the other for causing a conflict.

  • Step 3 Response Aggregation and Defuzzification

During this step, individual expert judgements, i.e. pairwise comparison matrices, are aggregated using the geometric mean formula for the lower, middle and upper bounds of the fuzzy scale.

\(\tilde{a}_{{{\text{ij}}}}^{k}\) = (\({\mathrm{l}}_{ij}^{k}\),\({\mathrm{m}}_{ij}^{k}\),\({\mathrm{u}}_{ij}^{k}\)) denotes the individual opinion of expert k

$$a_{{{\text{ij}}}}^{{ \sim {\text{agg}}}} = \left( {\left( {\mathop \prod \limits_{{k = 1}}^{p} l_{{{\text{ij}}}}^{k} } \right)^{{1/p}} } \right.,\,\left( {\mathop \prod \limits_{{k = 1}}^{p} m_{{{\text{ij}}}}^{k} } \right)^{{^{{1/p}} }} ,\,\left( {\mathop \prod \limits_{{k = 1}}^{p} u_{{{\text{ij}}}}^{k} } \right)^{{1/p}}$$
(2)

Equation 2 is used for aggregating the opinions of p experts [53, 54]. Local priority vector for each comparison matrix is determined using Eigenvector computations.

$$G.W = ~\lambda _{{\max }} W$$
(3)
$$\left( {G.W - ~\mu I} \right)W = 0,$$
(4)

where \(\lambda _{{{\text{max}}}}\) is the largest Eigenvalue for comparison matrix G. Consistency index and consistency ratios are also computed to assess the level of consistency in expert judgements.

$${\text{Consistency~Index}} = \frac{{\lambda _{{{\text{max}}}} - {\text{~}}n}}{{n - 1}}$$
(5)
$${\text{Consistency~Ratio}} = \frac{{{\text{Consistency~Index}}}}{{{\text{Random~Index}}}}$$
(6)

n is the order (size) of the comparison matrix (G), \(\mathrm{\lambda }\mathrm{m}\mathrm{a}\mathrm{x}\) is the Eigenvector of a comparison matrix (G). Random index value is selected from Table 4 depending on the order (n) of the comparison matrix (G) [42].

Table 4 Random index for calculating consistency ratio
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Defuzzification and Eigenvector computations are performed using Microsoft Excel software [55]. In our calculations, C.R. value for all element comparison matrices is found to be lower than the maximum allowable limit of 0.10 [42, 46].

  • Step 4 Formation of Initial Supermatrix

In order to determine the global priorities in a system with interdependent influences, local priority vectors are placed at their appropriate places to create the initial supermatrix. Initial supermatrix is a partitioned matrix where each segment represents the relationship between two clusters of the system. Let the clusters in a decision system be denoted as \({C}_{p}\), p = 1, 2, 3……m and each cluster p has \({n}_{p}\) elements denoted by \({e}_{p1}\),\({e}_{p2}\)……………\({e}_{np}\) elements. Local priority vectors obtained in the previous step are placed at appropriate positions in the supermatrix based on the flow of influence from one cluster to another, or from a cluster to itself in a closed-loop as seen in Table 5.

((7))

  • Step 5 Formation of Weighted and Limit Super Matrix

In the final step, each column of the initial supermatrix is normalised to obtain weighted supermatrix shown in Table 6. Raising matrices to considerable exponential powers is used for computing the long term relative influence of its elements over each other. The weighted supermatrix is raised to the 27th power using the R statistical package to achieve convergence condition [56]. Convergence is checked by ensuring that all rows in the supermatrix are identical and summation of individual columns produces unity (refer Table 7). The global priority weights for the four categories are determined by normalising their corresponding row value in the final limit supermatrix. Similarly, local priority weights for the sixteen conflicting factors are determined by normalising their corresponding row value within categories. Finally, global priority weight for each factor is determined by multiplying their local weight with the corresponding global category weight.

Table 5 Initial super matrix for the four categories and sixteen conflicting factors
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Table 6 Weighted super matrix for the four categories and sixteen conflicting factors
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Table 7 Limit super matrix for the four categories and sixteen conflicting factors
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Results

Table 8 presents the local and global priority weights for the sixteen conflicting factors. Global weights are used for determining the relative importance of four categories and sixteen conflicting factors. Contractor related factors (33.91%) are major contributors towards conflicts followed by client (27.46%), consultant (22.91%) and miscellaneous factors (16.40%). As shown in Fig. 4, dissatisfaction over the quality of completed work (18.3%) is the most important conflicting factor followed by delay in progress payments (10.5%), poorly written contract (9.3%), frequent changes in project scope (7.8%), accuracy of project cost estimates (7.7%) and approval delays by owner (6.7%), inefficient planning and scheduling (6.5%), lack of sufficient contractor experience (6.4%) and lack of skilled labour (6.1%). The top nine factors account for 80% of the motives behind conflicts occurring in building construction projects across India (Fig. 5).

Table 8 Local and global priority weights for the sixteen conflicting factors
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Fig. 4
figure 4

Global priority weights for the sixteen conflicting factors

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Fig. 5
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Individual and combined contribution (global priority weights) for the top nine conflicting factors

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Figure 6 illustrates the local priority weights of conflicting factors in the four categories. In contractor related factors, poor quality of completed work (54.1%) is the most important factor followed by inefficient planning and scheduling (19.4%) and lack of sufficient contractor experience (19%). In client related factors, delay in progress payments (38.4%) is the major conflicting factor followed by a change of scope (28.6%) and approval delays caused by the owner (24.5%). In case of consultant related factors, poorly written contract (42.1%) is the major conflicting factor followed by the accuracy of the project cost estimate (35%) and mistakes in design (13.3%). Under the miscellaneous category, lack of skilled labour is the most influential factor (37.5%) followed by difficulty in obtaining work permits from concerned authorities (33.2%) and delays in resolving contractual issues (16.1%). Client and contractor related factors together account for 62% of all causes during building construction projects.

Fig. 6
figure 6

Local priority weights for conflicting factors in the four categories

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Discussions

Modern construction projects involve multiple stakeholders, including clients (project financers), contractors, subcontractors, consultants, project managers, architects, designers, and specialists from various disciplines. In such a large multi-party environment, it is inevitable to encounter some sort of conflicts at various phases of the project. In order to minimise the frequency of harmful negative conflicts, various stakeholders should have clarity regarding their roles and must act accordingly.

Client Role

In order to minimise negative conflicts with the contractor, clients should ensure the availability of sufficient funds and release payments as per the pre-approved payment plan. In public-funded construction projects, government bodies must work with contracting parties for simplifying the procedure for work and payment related approvals. The client (financing party) should work diligently in developing the project scope document as all the construction plans, schedules, material quantities and quality control mechanism developed at later stages of the project are based on this scope document.

Frequent changes in project scope can modify the time and cost needed for performing various activities which may not be acceptable to the contractor resulting in conflicts. The client should allocate sufficient time and resources for initial project feasibility to assess all the associated technical, financial and environmental risks before developing the scope document. The client should also acknowledge that some changes may be inevitable and should be treated as an integral part of the project as it may be needed to enhance the quality of the final outcome. Appropriate terms and clauses for change management should be included in the contract documents. In many cases, contractors win the project bid by offering a lower than normal price and later attempt to deliver lower-quality work and put up unjustified claims to cover their financial risks. In India, project financing bodies must forego the culture of favouring lower cost bids and favour contractors having a proven record of quality, cost-effectiveness and strong project management skills.

Consultant Role

Consultants must act as a bridge between the clients and contractors. They are responsible for understanding the needs of the client for developing all contract-related documents. Consultants must undertake a comprehensive risk assessment covering the technical, financial and environmental risks to understand the project complexity fully. Contractual terms should clearly reflect the specific roles and responsibilities of each stakeholder. Specific clauses related to work revisions, material and labour cost fluctuations, payment stipulations should also be included in the contract. Consultants are also responsible for drawing project timelines and developing structural, electrical and plumbing drawings and calculating associated costs. Mistakes in the design and specifications of construction items can cause work revisions altering the baseline time and cost estimates. These unintended corrections can create tension between the client and contractor, leading to harmful conflicts. It is recommended that clients should hire experienced consultants having a dedicated team of qualified professionals responsible for developing contract documents, detailed design drawings, and quality monitoring tasks. Also, wherever possible, consultants should involve contractors and clients to review the design drawings to reduce chances of design defects.

Contractor Role

Contractors are responsible for the actual construction of the building structure. The contract must be awarded to a competent contractor having relevant experience and needed trade competency to ensure conflicts regarding the poor quality of finished work can be avoided. The main contractor is responsible for building a well-qualified and dedicated construction team comprising of an experienced project manager, skilled subcontractors and well-trained labourers so that project milestones can be achieved within allotted time and budget. The contractor must appoint a project manager who can lead from the front and possess necessary leadership skill, contract implementation skill and project organisation skill. Contractors must ensure that sufficient working capital is available for day to day operations at the site, subcontractor payments and must only use the pre-approved quality and quantity of materials. Contractors should also be attentive to the safety and well-being of the labourers, especially females, to prevent conflicts and clashes arising due to any potential accident or unrest. Contractors must also take necessary steps for maintaining smooth communication with the clients and consultants updating them about the progress at regular intervals.

Authors recommend the adoption of a partnering approach as it encourages the contracting parties to engage directly and identify common goals and develop innovative ways of handling conflict. Open discussions concerning the expectations of both parties can empower the project team to work together without fear of backlash. Partnering becomes even more vital when project uncertainty is high.

Conclusions

Identification of critical causes of conflict is crucial for developing robust conflict mitigation strategies. Fuzzy Analytical Network Process method has been employed for ranking the various conflicting factors relevant to building construction projects in India. It is found that contractor-related issues are mainly responsible for the occurrence of negative conflicts followed by client and consultant related issues. It is found that dissatisfaction over the quality of completed work, delay in progress payments, poorly written contract, frequent changes in project scope, inaccuracy of cost estimates, approval delays by the owner, inefficient planning and scheduling, lack of sufficient contractor experience and lack of skilled labour are the top nine factors responsible for explaining 80% conflicts occurring in building construction projects across India.

Building construction projects involve many stakeholders including financers, contractors, clients, subcontractors designers and specialists from various disciplines. Project stakeholders should have clarity regarding their roles and must act accordingly to prevent the occurrence of disruptive conflicts. The client must have sufficient clarity regarding their expectations from the project and ensure the availability of sufficient funds for timely payments. The client should build a dedicated team of client and consultant, having a proven record of successful projects. Consultants should ensure all designs and cost proposals are developed after a thorough risk assessment to prevent chances for errors and work revisions at later stages. Contractors must exhibit exceptional management, contract implementation and project organisation skill to ensure that so that project milestones can be achieved within allotted time and budget. A proactive approach based on trust and cooperation is suggested for minimising negative conflicts in building projects across India.

The current study has considered 32 common sources of conflict in building construction projects across India. Follow-up studies can also adopt this methodology for the assessment of key factors which determine the time and cost performance of various types of infrastructure projects in India and abroad. It will be very interesting to assess a cross-sectional view of different projects either within the same country or across different countries.