Understanding water conflict behaviors and their contributing factors is critical for developing effective agricultural water resource management. Hence, this paper examines how water conflict behaviors are addressed in a model guided by the Theory of Planned Behavior and whether there is a potential to develop the model further to include quality of life (QoL), perception of water crisis, sense of place, and social capital in explaining the farmers' water conflicts. Stratified random sampling was used to survey 212 farmers in the villages that benefited from the Gawshan dam in the Kermanshah region, Iran. Based on the results, the causal role of subjective norms in influencing intention to manifest conflict has not been established, while low social capital was of importance for intention to create conflict. Furthermore, low QoL, as well as high perception of water crisis, was found to be important for attitude formation toward conflict. These results enrich the empirical evidence in support of improving the understanding of farmers' water conflict behaviors.

  • Water conflict behaviors are addressed in a model guided by the Theory of Planned Behavior (TPB).

  • Additional constructs (quality of life (QoL), perception of water crisis, sense of place, and social capital) were included in the TPB.

  • Social capital was of importance for conflict intention.

  • QoL and perception of water crisis were found to be important for attitude toward conflict.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Developing countries face a wide range of socio-ecological crises related to inadequate, and perhaps lack of, access to freshwater resources, which leads to international, regional, and local conflicts (Homer-Dixon, 1994; Gleick, 2014). In this regard, increasing conflicts over water resources arise from claims between two or more stakeholders in relation to the allocation or use of a shared water resource flowing along similar flow paths (OECD, 2005; Thomas et al., 2013), rooted in competition for survival, cultural values (Funder et al., 2010), or perhaps the product of circumstances (Coleman, 1955).

In such context, many latent conflicts have become manifest (Funder et al., 2010). There is evidence of growing competition, tensions, and disputes over fresh and limited shared water resources which have even led to armed conflicts (Gleick & Heberger, 2012). Accordingly, there has been an increase in reported cases of water conflicts in Iran, as the risk of a water crisis due to shortages has also increased during recent years (Bijani & Hayati, 2011; Mohammadinezhad & Ahmadvand, 2020). Certainly, part of this increase can be related to improvements in reporting processes: new internet tools that permit a more comprehensive collection and dissemination of news and information (Gleick & Heberger, 2012), while there appears to be a growing interest in identifying and representing high-risk areas in water shortage (Madani, 2010). It is becoming increasingly clear that many local and regional water-related conflicts that take place in transboundary settings are likely growing in number and intensity (Postel & Wolf, 2001; Ravnborg, 2004; Kreamer, 2012; Gleick, 2014) with no idea of its cause (Funder et al., 2010). It can be said that the nature of the conflicts has been changed from international to local water conflicts (Gleick & Heberger, 2012). However, in Iran for some reason, there are no accurate statistics and documentation on local water conflicts. But there is a common perception that the lack of political tools to mitigate adverse effects of ever-increasing water scarcity often leads the water conflict escalation between different stakeholders (Bijani & Hayati, 2011; Mohammadinezhad & Ahmadvand, 2020). This is especially true in the agricultural sector as the largest consumer of water by far and accounting for more than 92% of total surface water and 60% of groundwater usage in Iran. Only 44% of the agricultural lands in this country are irrigated (Food & Agriculture Organization (FAO), 2008) and the rest is rain-fed (Sheidaei et al., 2016). Furthermore, the area considered to be arid includes almost 65% of the country's territory, meanwhile 20% is semi-arid, and the rest indicates a humid or semi-humid climate. The annual precipitation of 413 billion cubic meters varies greatly across the country from less than 50 mm in central Iran to about 1,000 mm in the Caspian coast. So, the country with average annual precipitation of 250 mm receives less than one-third of the global average. However, according to the dispersion of precipitation reports, most of the country receives less than 100 mm of annual rainfall and 75% of the precipitation falls over only one-fourth of the country's territory (Madani, 2014).

It seems that the reason lies in the food security goal as the most important development priority for these countries. However, the root of this inefficiency in the agricultural sector should be sought in poor water governance (Tatar et al., 2019). Furthermore, the conflicts of idea, perception, and actions and, more importantly, conflicts of interests among different beneficiaries have become one of the most important constructs of instability of water resource management. In fact, these conflicts have generally been found to act as both causes and effects of this instability. They are called the ‘instability causes’ based on whether they cause stakeholders to withdraw constructive involvement in the face of water resource management issues or not. They are called the ‘instability effects’ because unwise and inefficient water resource management has fueled these conflicts and tensions. So, it seems that effective water resource management depends on the establishment of effective conflict management strategies among different beneficiaries, be it the water conflict cause or its effect. In other words, understanding the nature of the conflicts occurring in the context of local competitive conditions along with identifying factors contributing to conflict behaviors could help manage these conflicts more effectively. Therefore, the aims of this research were twofold: first, to provide useful information to understand farmers' conflicts over agricultural water resources in the Gawshan dam irrigation network, Kermanshah province, and second, to determine the key factors affecting water conflict behaviors. In order to achieve these goals, at first, a theoretical framework with the aid of the Theory of Planned Behavior (TPB) model and the additional factors of perception to water crisis, sense of place (SoP), quality of life (QoL), and social capital is presented. The subsequent section provides a description of the methodology involving research method, variables and measurement, and study site. Next, the study results are reported. Lastly, conclusion and policy recommendations are provided.

Conflict is a complex and multidimensional phenomenon found in social, economic, political, and cultural activities (Antonova, 2014). As such, scholars who were interested in studying conflict approached the issue from different angles (Rahim, 2001). This means that it is difficult to put forward a commonly accepted definition of conflict (Chaudhry & Asif, 2015). However, in a simple classification, some attempts have been made toward a standard terminology defining conflict as a ‘situation in which position, practices, or goals for the different participants are inherently incompatible’ (Rahim, 2001), while others describe the conflict as ‘a type of behavior which occurs when two or more parties are in opposition or in battle as a result of a perceived relative deprivation’ (Litterer, 1966). By following the second path, this research assumes that conflict is defined as ‘a behavior rather than a situation’. This assumption results from incompatibility or opposition in goals, activities, or interaction among the social entities (Shariq Abbas & Singh, 2012). This behavior has emerged from water parties claiming their share of water (Kreamer, 2012; Gleick, 2014). Indeed, exploitation of common water resources leads to conflict and tragedy of the common resources. This tragedy is one of the basic concepts of human ecology (National Research Council, 2002). It is an economic theory of a situation within a shared-resource system where individual users behave contrary to the common good of all users (Stafford, 2018). This independent action is according to their own self-interest, by depleting or spoiling that resource through their collective action (Hardin, 1968). Contrary to the Hardin's theory, Ostrom (1990) has shown that self-organized groups that follow eight key principles for institutional arrangements for sharing common-pool resources (CPRs) can succeed in avoiding the tragedy of the commons. However, achieving such arrangements is not simple; it can be a messy process over some years, needs support from authorities, and needs to take into account the context, including power, conflict, and social learning (Saunders, 2014).

Thus, disputes generally caused by common water resources can be prevented by a better understanding of the factors influencing the cooperative behavior of individuals to perform collective actions (Ostrom, 2008). Over the years, numerous studies have been conducted to examine the factors influencing behavior, drawing on theories from the social sciences (Dunlap & Jones, 2002; Albarracín et al., 2014). One of the popular theories to explain treatment options like behavior and intentions is the TPB (Ajzen, 1985), according to which intention to perform a certain behavior precedes the actual behavior (Ajzen, 1991). Accordingly, behavioral intention is determined by three factors: attitude toward the behavior, subjective norms, and perceived behavioral control (Ajzen, 1991). Attitude is one of the central concepts in social psychology that has different definitions and implications from the perspectives of professionals (Hogg & Vaughan, 2005). It refers to favorable or unfavorable evaluation of the behavior (Ajzen, 1991). A simpler definition of attitude is the tendency to act in a particular way (Pickens, 2005). The second factor is subjective norms that reflect the perceived social pressure to engage in the target behavior (Ajzen, 2006). The third factor is perceived behavioral control. This construct encompasses the total set of accessible control factors that may facilitate or impede performance of the behavior (Knabe, 2012). Perceived behavioral control influences both intention and behavior. It can vary across situations and actions (Ajzen, 1991). Thus, the design of this study is based on Ajzen's TPB as the means to manage common-pool resources at the local level (Figure 1). Since the complexity of contextual concepts inherent within CPR theory is open to the inclusion of additional predictors and the complexity of contextual concepts inherent within CPR theory (Ajzen, 1991), and since it ignores the effects of external factors on attitude (Ajzen, 1991), various studies have proposed some modifications to TPB in order to overcome its limitations. Hence, in this study, the interactions of QoL, perception of water crisis, SoP, and social capital with the standard TPB predictors are used to explain farmers' water conflict behaviors. This modification is justified in the following.

Fig. 1

Conceptual model framework to be tested based on TPB.

Fig. 1

Conceptual model framework to be tested based on TPB.

Close modal

Perception of water crisis

Among factors which influence attitudes and behaviors, perception is one of the most important (Dolnicar et al., 2012). Perception is defined in the dictionary means ‘the way you think about or understand someone or something’ (Merriam-Webster, 2015) and refers to a type of psychological performance that enables individuals to interpret and organize sensory stimulation in order to produce meaningful experiences (Gold, 1980; Pickens, 2005). However, perception may be significantly different from reality (Pickens, 2005). Another issue is that if two people have the same status, their response will vary depending on method of interpretation and perception of the condition. This variation, completely unique to each individual, influences their organization and interpretation based on their needs, expectations, and values (Schiffman, 2012). The elements that shape perception according to Taylor et al. (1988) are experience, memory, definition, and expectation. Experience refers to the relevant events with agricultural water crisis occurred during the farmer's professional life (Taylor et al., 1988). Memory refers to those direct-map water crisis events, some of which were arguably transferred to other farmers and saved by them (Taylor et al., 1988). This perhaps refers to the way the crisis is characterized by farmers using ‘a set of criteria for classifying the water crisis time period’ (Taylor et al., 1988). Additionally, farmers' expectations of future water crises included how often they expected water crises to occur and how severe they expected them to be (Taylor et al., 1988).

Sense of place

One of the key factors that explain the differences in attitudes toward water conflict is SoP (Larson et al., 2013). SoP is used to describe the relationship between people and spatial settings (Jorgensen & Stedman, 2001). In general, SoP has been referred to as ‘meanings and identities’ that people develop through their experiences with places (Harvey, 2001). SoP consists of three constructs in the environmental psychology literature: place identity, place dependence, and place attachment (Jorgensen & Stedman, 2001). Place identity involves those dimensions of self that define the individual's personal identity in relation to the physical environment (Prochansky, 1978). Place attachment is the emotional bond between individuals and their environment (Williams et al., 1992). Finally, place dependence is the perceived strength of association between an occupant and specific places (Stokols & Shumaker, 1981).

Quality of life

The concept of QoL is the condition of life resulting from the combination of the effect of a complete range of factors (such as those determining health, happiness, education, social and intellectual attainments, freedom of action, justice, and freedom from oppression) (WHO, 1999). Studies have explained the relationship between QoL and other variables based on the top-down and the bottom-up models. The former is based on the premise that the QoL causes certain outcomes in the individual's life, while the latter rests on the proposition that particular variables influence an individual's QoL (Evans, 1994). Especially in the field of conflict, it has been shown that conflict has been associated with lower QoL (Sundquist et al., 1998; Akinyemi et al., 2012), but it is not known whether QoL causes conflict. Therefore, this research was intended to examine whether the QoL causes a certain attitude toward conflict or not. Limited studies have confirmed the association between attitude and the QoL (Ahmadvand et al., 2011).

Social capital

Social capital as ‘those features of social organization – i.e., trust, norms, and networks – can improve the efficiency of society by facilitating coordinated actions’ (Putnam, 1993) or as ‘features of social life can enable participants to act together more effectively to pursue shared objectives’ (Putnam, 1995). Therefore, it encompasses two related components: structural and cognitive (Uphoff & Wijayaratna, 2000). The former is associated with various forms of social organization that contribute to cooperation such as participation, connections, and proactivity in social context, whereas the latter derives from mental processes such as feeling of trust and safety, tolerance of diversity, and value of life (Onyx & Bullen, 2000).

Some studies have investigated the role of social capital to form behaviors (Miller & Buys, 2004). It is particularly effective in improving farmers' collective action (Ostrom, 1990) and transferring conflict into good bilateral cooperation at the level of conflict intention (Trigilia, 2001; Sanginga et al., 2007; Michelini, 2013). Empirical and theoretical studies have illustrated the close relationship between conflict and low social capital (Colletta & Cullen, 2000; Deluca & Verpoorten, 2011; Aghajanian, 2012; Gilligan et al., 2014). These studies clearly demonstrate that conflicts can strongly influence social capital; specifically, conflict within a society weakens its social capital. But they did not consider the role of social capital in forming conflict behavior or intention to manifest conflict. Although it has been proven that social capital, as an informal norm facilitates cooperation for collective action, by which people can resolve their conflicts (Sanginga et al., 2007). Therefore, in this paper by looking at the impact of low social capital on intention to manifest water conflict behavior, we can perceive a certain relationship between social capital and conflict occurrence in the field of agricultural water management.

Sample and data collection

In seeking to investigate agricultural water conflict, this study was designed as a quantitative approach using a survey. Since most of the participants were illiterate or low-literate people, the survey took place via the use of a face-to-face questionnaire as the most accurate method (Salant & Dillman, 1994). The survey was administered from June to October 2018. The target population of this study was farmers who used water from the irrigation network of the Gawshan dam in Kermanshah, Iran. Accordingly, the two plains of Bilevar and Miandarband appear to have benefited from the dam. There are 614 and 1,079 farmers in upstream (the Bilevar Plain) and downstream (the Miandarband Plain), respectively. To determine the sample size, Bartlett et al.’s (2001) table was used with a margin of error of 0.03, t = 2.58, and α = 0.01. According to the table, 212 farmers were estimated as a sample size and were selected through a stratified random sampling procedure that involves dividing the entire population into upstream and downstream groups known as strata. A random sample from each stratum is taken in a number proportional to the stratum's size when compared to the population. Therefore, 77 and 135 farmers were asked to participate in the interview to complete the anonymous questionnaire from upstream and downstream, respectively. The respondents were free to discontinue their participation at any time. The majority of the farmers (90.6%) were men. The respondent's age ranged from 24 to 80 years old (M = 45.53, SD = 14.25). In terms of education, 37.7% of the farmers were illiterate, 59.9% attended primary and secondary school, and 2.4% had diploma. Regarding the farm size, 36.8% of the respondents' farm size ranged from 2 to 4 ha.

There were 86 cases of conflict recorded formally during 2011–2016 in upstream and downstream areas. The water conflicts between farmers in upstream and downstream and also conflicts between farmers and the regional water authority were more acute.

Designing the survey instrument

The questionnaire consisted of three sections: the first section covered the basic variables in the TPB model, including attitudes toward water conflict, subjective norms, perceived behavior control (PBC), conflict intention, and water conflict behavior. The second section composed the additional variables of perception of water crisis, social capital, QoL, and SoP. The last section elicited farmers' general demographic information. Most of the questions for the measurement of the conceptual model constructs were adapted from the scales available in the literature review, with a 5-point Likert scale of ‘strongly disagree’ (1) to ‘strongly agree’ (5), except QoL, conflict intension, and water conflict behavior variables. Measuring the latter variables is as follows:

  • QoL: A revised Rural Quality of Life Index (RQLI) was used to measure Farmers' QoL. The procedure was developed by Danasekaran (1991) and adapted and translated into Farsi by Hayati et al. (2006). So, the RQLI was measured using indicators such as income status (annual household income per capita), assets (land and livestock ownership, use of agricultural machinery and equipment, household living assets and possessions, and other transportation equipment), social status (education, social participation, access to credit, and use of hired farm labor), nutritional status (both of quality and quantity of household food availability), clothing (an annual per capita cost of household clothing), housing (both quality and quantity), and social security (use of health and medical insurance and other insurance services). The scores for the RQLI lie between 1 and 5 where score 1 indicates absolute poverty and score 5 stands for a high QoL score.

  • Water conflict behavior: This variable is defined as the farmer's reaction to incompatible interests of another party's over the simultaneous use of water resources. This construct was measured along four conflict levels including 12 questions concerning: no conflict, latent conflict, surface conflict, and open conflict (three questions for each level of the construct) with a 5-point Likert scale from ‘completely disagree’ to ‘completely agree’.

  • Conflict intention: This variable is defined as the farmers' willingness, desire, aspiration, and negative responses to manifest conflict. Intention to conflict questionnaire includes 15 questions on a 4-point scale ranging from ‘never, rarely, sometimes but infrequently, and always’.

The questionnaire's validity was confirmed by panel of experts, and a pilot study, involving 30 farmers, was conducted in outside the study area to evaluate the instrument.

More detailed information on the definition and measurement of the variables used in the study and their Cronbach's alpha reliability coefficient are presented in Table 1.

Table 1

Operational definition of the variables used in the model.

Variable namesDefinition and measurementCronbach's alpha
Conflict behavior The degree of water conflict behavior ranges from no conflict, to latent conflict, to surface conflict, to open conflict (Blackman, 2003). Conflict behavior was measured using 12 items on a 5-point Likert scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5). – 
Conflict intention Intentions are decisions to act in a given way (Ajzen, 1991). Therefore, the total scoring of intention to manifest conflict was being between 0 and 60, scoring 60 indicated the highest intention to manifest conflict. – 
Attitude toward water conflict The attitude toward water conflict refers to the degree of evaluation or appraisal of a person from being favorable or even unfavorable in terms of the behavior in question. Water conflict attitudes were measured using five items on a 5-point Likert scale that ranged from ‘strongly disagree’ (1) to ‘strongly agree’ (5). 0.856 
Perception of water crisis It was measured using 12 items (experiences of farmers in relation to the water crisis, defining water crisis, farmers’ memories and their expectations of the water crisis in the past years and its effects in future) (Taylor et al., 1988). They were measured on a 5-point Likert scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5). 0.962 
QoL QoL was assessed by 15 indicators adopted from the RQLI which was developed by Danasekaran (1991) and revised by Hayati et al. (2006). This variable is classified under seven major components (social status, income status, assets, nutritional status, clothing, housing, and social security). Each respondent was able to select a value between 1 and 5 for each indicator. – 
SoP The three SoP components were measured with 12 items on a 5-point Likert scale ranging from 1-strongly disagree to 5-strongly agree (Jorgensen & Stedman, 2001). 0.784 
PBC PBC consisted of two different sub-constructs: PC and CE. Control beliefs are the perceived power to facilitate or inhibit performance of the behavior (Ajzen, 1991). A 3-item scale measured this variable. The CE refers to an individual's belief in his or her ability to resolve interpersonal conflict across a variety of situations (Alper et al., 2000). Three items scale measured this variable. 0.912 
Subjective norms Two groups of questions measured influence of the subjective norms on water conflict behavior: farmers’ expectancies that others believed there was water conflict in the region and the extent to which farmers value those others’ views. The products of these expectancies and values provide numeric estimates of subjective norms. Total scoring of subjective norms was being between 0 and 9. 0.841 
Social capital This dimension was measured using the adopted version of Onyx and Bullen’ community-level social capital measurement scale (Onyx & Bullen, 2000). It included 10 items (participation, connection, proactivity, trust, tolerance of diversity, and value of life, etc.), on a 5-point Likert scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5). 0.837 
Variable namesDefinition and measurementCronbach's alpha
Conflict behavior The degree of water conflict behavior ranges from no conflict, to latent conflict, to surface conflict, to open conflict (Blackman, 2003). Conflict behavior was measured using 12 items on a 5-point Likert scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5). – 
Conflict intention Intentions are decisions to act in a given way (Ajzen, 1991). Therefore, the total scoring of intention to manifest conflict was being between 0 and 60, scoring 60 indicated the highest intention to manifest conflict. – 
Attitude toward water conflict The attitude toward water conflict refers to the degree of evaluation or appraisal of a person from being favorable or even unfavorable in terms of the behavior in question. Water conflict attitudes were measured using five items on a 5-point Likert scale that ranged from ‘strongly disagree’ (1) to ‘strongly agree’ (5). 0.856 
Perception of water crisis It was measured using 12 items (experiences of farmers in relation to the water crisis, defining water crisis, farmers’ memories and their expectations of the water crisis in the past years and its effects in future) (Taylor et al., 1988). They were measured on a 5-point Likert scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5). 0.962 
QoL QoL was assessed by 15 indicators adopted from the RQLI which was developed by Danasekaran (1991) and revised by Hayati et al. (2006). This variable is classified under seven major components (social status, income status, assets, nutritional status, clothing, housing, and social security). Each respondent was able to select a value between 1 and 5 for each indicator. – 
SoP The three SoP components were measured with 12 items on a 5-point Likert scale ranging from 1-strongly disagree to 5-strongly agree (Jorgensen & Stedman, 2001). 0.784 
PBC PBC consisted of two different sub-constructs: PC and CE. Control beliefs are the perceived power to facilitate or inhibit performance of the behavior (Ajzen, 1991). A 3-item scale measured this variable. The CE refers to an individual's belief in his or her ability to resolve interpersonal conflict across a variety of situations (Alper et al., 2000). Three items scale measured this variable. 0.912 
Subjective norms Two groups of questions measured influence of the subjective norms on water conflict behavior: farmers’ expectancies that others believed there was water conflict in the region and the extent to which farmers value those others’ views. The products of these expectancies and values provide numeric estimates of subjective norms. Total scoring of subjective norms was being between 0 and 9. 0.841 
Social capital This dimension was measured using the adopted version of Onyx and Bullen’ community-level social capital measurement scale (Onyx & Bullen, 2000). It included 10 items (participation, connection, proactivity, trust, tolerance of diversity, and value of life, etc.), on a 5-point Likert scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5). 0.837 

Data was processed using IBM's SPSS 19 software through multiple regression, χ2, ordinal regression, and Spearman's correlation coefficient. A significance level of 0.05 was used to establish statistical significance.

The study site: the Gawshan dam basin

The study was conducted in the basin of the Gawshan embankment dam on the River Gaveh, 45 km south of Sanandaj city and 90 km north of Kermanshah, Iran (Figure 2). The study area is located between 34°57′N latitude and 47°01′E longitude. The dam supplies 395 million cubic meters of water to irrigate 31,000 ha of lands and generates 11 MW of electricity. Additionally, the reservoir is used to provide 63 million cubic meters of water annually for domestic use in the city of Kermanshah. The Gawshan project consists of water transmission lines, and the irrigation and drainage networks with an area of 30,652 ha of farmland. The irrigation networks cover wide ranges of two major plains including: upstream network 1,097 (ha) in the Bilevar plain and downstream networks 19,678 (ha) in the Miandarband plain (Mahab Consulting Engineers Company, 1999). It supplies agricultural water for approximately 57 villages and 1,693 beneficiaries.

Fig. 2

Iran map and the study area (Kermanshah province, Gawshan dam).

Fig. 2

Iran map and the study area (Kermanshah province, Gawshan dam).

Close modal

The water governance main actors in the water delivery domain are the Regional Water Company (RWC) and Jihad-e-Agriculture Organization (JAO). These institutions perform water supply task, from the Gawshan dam and the canal system, distribute domestic water to the urban areas, and farms, and take the responsibility to conserve surface and groundwater sources. Water management in the Gawshan dam and the surrounding plains, in this domain, is the responsibility of the RWC of Kermanshah province. Furthermore, 46 farmers' water users associations (WUAs) participate in water governance in the area. The WUAs take responsibility for preparing and approving local water instructions and regulations. It seems that organizations in local level use their potential mainly for participating in supply, distribution, and delivery of irrigation water to the associated farmers (Nabiafjadi et al., 2021). To do these tasks efficiently training and empowering farmers are of paramount importance.

Meteorological data indicates that the district is characterized by climatic change which resulted in water scarcity and severe droughts (Portal of Iran Meteorological Organization, 2016). Examination of runoff records showed decline in precipitation. For example, the average annual rainfall ranges from 395 to 299 mm during 2011/2012–2014/2015. This is despite the fact that annual average precipitation had been approximated to be 535 mm on reservoir storage (Mahab Consulting Engineers Company, 1999).

Agricultural water conflict behaviors

In the first stage, to understand and analyze water conflict behaviors, some questions are designed to reveal whether water-related conflicts exist and if so, what are their levels and frequencies. Four categories into which most conflicts will fall were developed to test the level and the frequency of conflicts among farmers. As shown in Table 2, the identified conflicts range from no conflict to the open conflict, including (1) no conflict (any peaceful community is likely to face conflict sometimes, although communities in this category are good at managing conflict before it develops); (2) surface conflict (with shallow or no roots that may be due to misunderstanding of goals); (3) latent conflict (below the surface conflict might need to be brought out into the open before it can be effectively addressed); and (4) open conflict (visible with deep roots, sometimes over several generations) (Blackman, 2003). About 160 out of 212 farmers have experienced water conflict behavior.

Table 2

Agricultural water conflict behavior, levels, and frequency.

Level of conflict behaviorFrequencyPercentageCumulative percentage
No conflict 52 24.5 24.5 
Latent conflict 37 17.5 42 
Surface conflict 48 22.6 64.6 
Open conflict 75 35.4 100 
Total 212 100  
Level of conflict behaviorFrequencyPercentageCumulative percentage
No conflict 52 24.5 24.5 
Latent conflict 37 17.5 42 
Surface conflict 48 22.6 64.6 
Open conflict 75 35.4 100 
Total 212 100  

Multicollinearity

Table 3 presents the results of correlation analysis of the variables. As can be seen from Table 3, all nine variables correlate positively and substantively. Accordingly, there are two or more highly correlated explanatory variables (e.g., between perception and QoL), hitting 0.7–0.8. To determine whether any multicollinearity effects existed, the variance inflation factor (VIF) was used. A formal detection-tolerance or the VIF for multicollinearity indicates the degree to which each predictor variable is explained by other predictor variables (Hair et al., 1998). A threshold VIF that is less than or equal to 10 (i.e., tolerance >0.1) is commonly suggested (Asher, 1983; Hair et al., 1998). The VIFs for perception and QoL were 2.70 and 2.06, respectively, in predicting intention and behavior, providing further evidence against multicollinearity.

Table 3

Correlation matrix for latent constructs.

Variables123456789
Behavior 1.00         
Intention 0.77** 1.00        
Attitude 0.67** 0.53** 1.00       
Social norms −0.26** −0.11 −0.16* 1.00      
Social capital −0.59** −0.50** −0.39** 0.33** 1.00     
PBC −0.54** −0.49** −0.50** 0.13* 0.38** 1.00    
Perception 0.81** 0.66** 0.72** −0.22** −0.56** −0.51** 1.00   
SoP −0.64** −0.48** −0.57** 0.30** 0.52** 0.42** −0.68** 1.00  
QoL −0.79** −0.67** −0.623** 0.28** 0.54** 0.44** −0.712** 0.55** 1.00 
Variables123456789
Behavior 1.00         
Intention 0.77** 1.00        
Attitude 0.67** 0.53** 1.00       
Social norms −0.26** −0.11 −0.16* 1.00      
Social capital −0.59** −0.50** −0.39** 0.33** 1.00     
PBC −0.54** −0.49** −0.50** 0.13* 0.38** 1.00    
Perception 0.81** 0.66** 0.72** −0.22** −0.56** −0.51** 1.00   
SoP −0.64** −0.48** −0.57** 0.30** 0.52** 0.42** −0.68** 1.00  
QoL −0.79** −0.67** −0.623** 0.28** 0.54** 0.44** −0.712** 0.55** 1.00 

Note: All constructs have a Pearson correlation coefficient, except conflict behavior.

*Significant p < 0.05; **Significant p < 0.01.

Investigation of the causal model of the factors affecting farmers’ conflict intention and behavior

The purpose of this research was to examine the main factors that influence the conflict intention and behavior of farmers. Additionally, the study examines whether explanatory variables, including perception, QoL, and SoP, give a significant improvement over the baseline model. Since linear regression models cannot test all relationships within a single model, therefore, our analysis proceeded in three steps (see Table 4). First, a multivariate linear regression analysis was performed on the data to investigate the dependence of attitude on the perception, SoP, and QoL. The results of analysis indicate that these variables explain 55% of the total variation in conflict attitude and the model was found to be significant (F = 85.874, df = 3). Significant variables included perception (t = 6.44; β = 0.491) and QoL (t = −3.09; β = −0.20), while SoP showed no significance (see Table 4).

Table 4

Regression of conflict behavior on the TPB.

βTPR2dfFp (for model)
DV: attitude 
Perception 0.49 6.44 0.0001 0.55 85.874 <0.01 
QoL 0.20 3.09 0.002 
SoP 0.123 1.92 0.056 
DV: behavior 
Attitude 0.32 6.46 0.0001 0.66 101.232 <0.01 
Subjective norms 0.057 1.334 0.184 
Social capital 0.25 5.28 0.0001 
PBC 0.400 7.73 0.0001 
BWaldPR2dfχ2p (for model)
DV: behavior 
Intention 0.528 79.271 0.0001 (0.87, 0.93, 0.75) 435.38 <0.01 
PBC 0.350 7.172 0.007 
βTPR2dfFp (for model)
DV: attitude 
Perception 0.49 6.44 0.0001 0.55 85.874 <0.01 
QoL 0.20 3.09 0.002 
SoP 0.123 1.92 0.056 
DV: behavior 
Attitude 0.32 6.46 0.0001 0.66 101.232 <0.01 
Subjective norms 0.057 1.334 0.184 
Social capital 0.25 5.28 0.0001 
PBC 0.400 7.73 0.0001 
BWaldPR2dfχ2p (for model)
DV: behavior 
Intention 0.528 79.271 0.0001 (0.87, 0.93, 0.75) 435.38 <0.01 
PBC 0.350 7.172 0.007 

The most important variable in predicting farmers' attitudes toward water conflict is water crisis perception. As discussed earlier, perceptual tendencies influence attitude (Pickens, 2005). In fact, farmers who have perceived agricultural water crisis as a managerial phenomenon in the region have had a more positive attitude toward water conflict rather than those who perceive it through meteorological parameters. These farmers believe that water crisis is rooted in inequality, poverty, and power (UNDP, 2006) rather than water scarcity. Therefore, they believe that conflict resolution will be possible through negotiation and participation. In return, farmers who perceived water crisis as a climatic phenomenon due to their experiences, perception, and expectations linked the water crisis to issues like sin, wrath of God, or denying alms-giving. Thus, they perceived that water crisis cannot be solved, so they do not have motivation and pre-disposition to participate in water conflict management. Another feature of the proposed framework is that QoL is a fundamental contributor toward conflict attitude formation. Moreover, the attitude of farmers with poorer QoL is more prone to engage in water conflict. In contrast to the findings of this study, others have found that QoL did not significantly contribute to attitudes (Karami & Mansoorabadi, 2008), although this study had a different focus than our study did. In addition, based on the relationships we identified, SoP was not associated with farmers' attitude toward water conflict. This finding is in contrast with the reports from the literature, which have been successful in identifying positive correlations between SoP characteristics and attitude (Jacobs & Buijs, 2011; Larson et al., 2013). To justify this finding, as previously mentioned, we measured SoP as a multidimensional construct composed of place identity, place dependence, and place attachment (Jorgensen & Stedman, 2001; Anton & Lawrence, 2014). The findings of this study illustrate that the extent of farmers' place dependency sense has decreased in rural areas, but it does not appear to affect the level of attachment (link between people or individuals and specific places) (Hidalgo & Hernandez, 2001) and the place identity. So, they still want to live in the place. Hence, overall, the effect of reducing dependence could not imply farmers' SoP. Afterwards, a multivariate linear regression was performed using intention as the dependent variable and attitude, subjective norms, social capital, and perceived control behavior as the independent variables. R2 was found to be around 0.66 and the model was significant (F = 101.232, Sig. = 0.0001).

The most important factor in farmers' conflict intention was PBC.

It exerted a negative influence on behavioral intent (t = −3.968; β = −0.40). In this paper, PBC consisted of perceived controllability (PC) and conflict efficacy (CE). CE is defined as people's beliefs about their capabilities to create or maintain social relationships, cooperation with others and resolve interpersonal conflict across a variety of situations (Alper et al., 2000; Eizen & Desivilya, 2005). This newer and less understood construct helps farmers understand and select different styles of conflict management (Alper et al., 2000; Spana, 2013). Literature has provided evidence that high sense of CE leads to an integrating style in which both opposing parties try to find out an acceptable solution (Spana, 2013). Additional research has suggested that people who perceived less CE prefer competitive approaches to conflict (Eizen & Desivilya, 2005). It was found that conflict attitude had the second highest total effect on conflict intention (t = 6.460; β = 0.32). Attitude positively impacts farmers' intentions. This finding is consistent with prior studies (Yazdanpanah et al., 2014; Chang et al., 2016; Pino et al., 2017). Social capital is the third variable that had the highest total effect on conflict intention (t = −5.285; β = −0.25). Limited research has confirmed the role of social capital in conflict prevention and management (Trigilia, 2001; Michelini, 2013). In contrast to Pino et al. (2017), Tohidyan Far & Rezaei-Moghaddam (2015), and Yazdanpanah et al. (2014), subjective norms appear not to affect the intention significantly.

Since the response variable, conflict behavior was categorized into no conflict, latent conflict, surface conflict, and open conflict, an ordinal regression was suitable for analyzing the ordered categorical data, evenly distributed among all categories. The independent variables used in this stage include intention and perceived behavior control. The analysis allows us to interpret how a single unit increase or decrease in independent variables is associated with higher or lower levels of water conflict behavior. In the first step, we need to find out if the model gives adequate predictions.

The model fitting information is shown in Table 4. As seen, the significance level is less than 0.01 (p < 0.01). This means that the final model gives a significant improvement over the null model. In the second step, goodness of fit is tested. As Table 4 shows, from the p-value it can be concluded that the data and the model predictions are similar. Hence, we have a good model. In ordinal regression models with a categorical dependent variable, it is not possible to calculate a single R2 (Elamir & Sadeq, 2010). So, three approximations are proposed instead. Pseudo R2 values (e.g., Nagelkerke = 0.93%) indicate that the independent variables (intention and perceived behavior) explained more than 90% of the conflict behavior variation.

The significance of the Wald statistic (Sig. Wald <0.01) in Table 4 is used to decide whether or not a particular coefficient (β) is significant. This shows the influence of both explanatory variables on conflict behaviors. It is revealed that higher conflict intention resulted in a stronger conflict behavior among farmers (see Table 4). We also found that perceived behavior control has negative effects on conflict behaviors. In other words, farmers with high levels of CE believed they could handle various conflicts and, therefore, are less likely to engage in water conflict. It means that the lack of the CE and control ability influences the conflict behavior and may increase conflict level. In other words, we can reject the null hypothesis that states the location parameters are the same across response categories.

The test of parallel lines is also conducted (see Table 5). The null hypothesis states that the location parameters are the same across response categories. P-values are not statistically significant for all groups.

Table 5

Test of parallel lines.

Model− 2 Log Likelihoodχ2dfSig.
Null hypothesis 137.026    
General 131.825 5.201 0.267 
Model− 2 Log Likelihoodχ2dfSig.
Null hypothesis 137.026    
General 131.825 5.201 0.267 

Figure 3 graphically presents the respective results of the model test based on the results of regression analysis and the standardized path coefficient for the data set.

Fig. 3

Path diagram with standardized path coefficients and explained variances.

Fig. 3

Path diagram with standardized path coefficients and explained variances.

Close modal

Therefore, farmers with a low sense of efficacy in dealing with conflicts have more intention toward water conflict. In other words, farmers with high levels of CE believed they could handle various conflicts and, therefore, are less likely to engage in water conflict.

Total causal effects of the conflict behavior

Table 6 summarizes the direct and indirect effects of each independent variable (IV) on conflict behavior (DV). One of our assumptions was that the variable intention and perceived behavior control would affect conflict behavior. As shown in Figure 3, both of them have a direct impact on conflict behavior. Intention appears to have the greatest effects (0.52) on conflict behavior. The total direct effect of these two factors is 0.17%. Attitude, social capital, and PBC were found to have positive significant total effects on behavior over intention.

Table 6

Direct, indirect, and total effect of independent variables on conflict behavior.

Independent variableBehavior
Direct effectIndirect effectTotal effect
Perception 0.08 0.08 
QoL −0.03 −0.03 
Attitude 0.16 0.16 
Social capital −0.13 −0.13 
PBC −0.35 −0.20 −0.55 
Intention 0.52 0.00 0.52 
Independent variableBehavior
Direct effectIndirect effectTotal effect
Perception 0.08 0.08 
QoL −0.03 −0.03 
Attitude 0.16 0.16 
Social capital −0.13 −0.13 
PBC −0.35 −0.20 −0.55 
Intention 0.52 0.00 0.52 

Experiencing water crisis, Iran faces an increase in conflicts over shared water sources at both the local and the regional level. In fact, many CPR including agricultural water resources have resulted in tragic levels of overuse and sometimes conflict. Despite the recognition that communities can manage CPR effectively, it has encountered difficulties in practice. Much of the community-based natural resource management is unsuccessful in implementing the specific institutional design principles identified in Ostrom's work. Therefore, this paper discussed how Ajzen theory may have contributed to preventing the poor performance of common pool resource and identifying contextual concepts inherent within CPR theory such as social capital, SoP, and perception of water crisis. Besides, this paper is concerned with factors influencing farmers' conflict behavior in a common water resource management project. We developed a simple theoretical framework based on Ajzen theory which, in spite of its extensive use in the literature on water-saving behaviors (e.g., Yazdanpanah et al., 2014; Werff & Steg, 2016; Pino et al., 2017; Tatar et al., 2019; Mohammadinezhad & Ahmadvand, 2020), has rarely been employed to investigate the determinants of water conflict behavior.

The findings showed that farmers who used water from the irrigation network of the Gawshan dam have experienced four important levels of conflict behavior. These levels consist of no conflict, surface conflict, latent conflict, and open conflict. Furthermore, it was found that over 40% of all the water conflict is latent and surface conflict, the state in which disagreements turn into open conflict through overt physical, social, psychological, and environmental damage (Blackman, 2003).

Based on the findings from this study, intention to manifest conflict and perceived behavioral control seemed to have a direct influence on conflict behavior. From these two variables, perceived behavioral control additionally decreases the intention to conflict and conflict behavior. Therefore, we recommend holding workshops on conflict and training conflict resolution in order to improve the sense of CE in rural communities by extension service. In this light, extension agents, for example, can play a role as honest brokers between opposing sides of conflict. With this focus on agricultural extension service, ‘conflict management-based extension’ will come to the fore. In this way, not only water conflicts will be resolved, but also indirectly through resolving conflict in other context, the stability and peace in the region will be reached. Thus, the condition will be provided for the efficient management of water resources. The next question is what other factors affect farmers' intention to conflict? The present study suggested that subjective norms do not seem to contribute significantly to conflict intention, but in this regard low social capital has been identified as an informal norm that contributes to the formation of water conflict intention. Therefore, water resource management in the region should shift toward the collaborative models. In fact, social capital can be seen as a vital coping strategy, not only in terms of bonding and bridging social connection but also in terms of providing space for stakeholders to negotiate. As such, the sincere commitment of conflict stakeholders will be fostered.

Furthermore, attitude toward conflict can predict willingness to behave in a conflict-induced manner. This factor is particularly important because if the government is supposed to intervene on conflict issues, it would not be possible without a true picture of the farmers' attitude toward conflict. As long as farmers believe that, they will not be successful in the negotiation process to resolve the conflict. The next question discussed is, what are the fundamental factors that were the key determinants of attitude formation regarding water conflict? This study revealed that farmers' perception of water crisis seemed to be the major influence on farmers' attitude toward water conflict. These results have contributed greatly to conflict resolution in the region. In other words, what the intervention is supposed to do in conflict resolution in the region is to seriously introduce the programs and determine their aims to promote farmers' awareness and real perception about water crisis.

Therefore, before any action, farmers should receive true and accurate information to reach real perception of the water crisis. In this case, many conflicts have been created due to misperceptions. Previous research has indicated that conflict event may lead to a reduction in QoL, and our research suggests that this decrease may also be associated with water conflict. The results indicated that farmers' attitudes toward conflict are influenced by their QoL. The most relevant aspects that defined the QoL in our research were having social status, income status, assets, nutritional status, clothing, housing, and social security. Considering the negative effect of poor QoL on conflict attitude, it is recommended that government-provide services and resources are very basic particularly in education and health. Moreover, despite related literature supporting the role of SoP on forming attitude (e.g., Jacobs & Buijs, 2011; Larson et al., 2013), it was not a significant predictor of conflict attitude in the current study.

Finally, the participation and activism of WUAs can play a valuable role in managing conflict among stakeholders, especially farmers. Despite the presence of WUAs in the region, they are often small, poor, and badly managed, having ‘very poor’, ‘limited’, or ‘inadequate’ impact on conflict resolution. Therefore, to empower WUAs, it is recommended to engage them in decision-making and water management planning. This way it could be expected that they would develop and eventually find their place in the society.

All authors contributed to the study conception and design. Data collection and analysis by M.T. and M.A. A.P. and M.A. helped supervise the project. All authors discussed the results and approved the final manuscript and have agreed to submit the manuscript to this journal.

The authors did not receive support from any organization for the submitted work.

The authors declare that there is no conflict of interests regarding the publication of this paper.

All relevant data are included in the paper or its Supplementary Information.

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