Measuring relationship marketing effect in small-scale fishing in Oman and enhancing efficiency and economic gains for traditional fishermen.

Jabri, Omar al ; Collins, Ray ; Sun, Ximing 等

Introduction

Although Oman's fisheries sector contributes just 0.6% to its national GDP (2011 estimate at 2000 constant prices), its importance in social, economic, and cultural fabrics goes well beyond the statistics. Indeed it plays a pivotal role in Oman's efforts to achieve food security, besides being a cornerstone in its traditional way of life. Under the recent economic diversification efforts, the Sultanate of Oman has devoted significant efforts to devise mechanisms to increase the sector's contribution to the country's GDP, in order to create employment opportunities for Omanis, achieve food security, and sustain community welfare, amongst others (MNE, 2007).

The fisheries sector is comprised of two distinct segments, namely, traditional (or artisanal) and industrial. Fishing activities are carried out exclusively by males. There are seven coastal Governorates in Oman: Musandam, Batinah (North and South), Muscat, Sharqyia South, Wusta, and Dhofar. This study focuses on the small-scale fisheries in the Batinah Governorate.

Total fisheries landings in Oman were estimated at 192,000 t in 2012, of which the traditional sector contributed more than 95% (MAF, 2012). In 2012, the traditional sector alone provided direct employment to 42,553 fishermen, full-time as well as part-time (MAF, 2012) (1). The traditional sector is comprised predominantly of small-scale fishermen who are engaged in labor-intensive fishing activities with a low level of capital input and technology, and use traditional fishing gears, such as small nets, traps, spears, lines, and hand-collection methods.

Research Objectives and Justification

Under the notions of supply chain management (SCM) and relationship marketing (discussed in the next section) the main focus of this study is on the post-harvest activities involved in the small-scale fisheries sector of the Batinah Governorate. This is because the existing literature points to several economic inefficiencies in the post-harvest sector that affects the performance of small-scale or traditional fishermen. These inefficiencies may arise from sources including a) market failure, b) lack of control over market supply, c) lack of promotion in developing small-scale fisheries enterprises, d) competition among small-scale fishermen, e) lack of adequate infrastructure and facilities, and f) insufficient attention to product quality (Omezzine, 1998; Al-Jabri, 1999; Al-Oufi et al., 2000; Omezzine et al., 2003). The focus of this study is influenced by the existing claim that effective supply chain management can improve the competitiveness and profitability of actors (Schotzko and Hinson, 2000; Fearne et al., 2001).

The role of supply chain management is crucial for fisheries in Oman as the harvested fish changes hands multiple times before it reaches the end-users. The middlemen (generally truckers) play a significant role in the post-harvest handling and distribution of fish. For example, the middlemen are engaged in transporting and selling fish to local and foreign (neighboring countries) markets. Therefore, a clear understanding of the existing supply chain relationship between fishermen and traders is vital to improve operational efficiency and thereby economic returns to fishermen.

The existing opportunity for enhancing efficiency and economic gains from the post-harvest sector is reflected in the strategic approach stipulated in the government's 5-year plan for the fisheries sector (MNE, 2007). With the establishment of a central wholesale fish market in Barka in the South Batinah Governorate and the development of an action plan for establishing a fisheries industrial estate at Duqm in Wusta Governorate, the Ministry of Agriculture and Fisheries has attempted to address inefficiencies in the supply chain.

The primary objective of this research is to investigate whether engagement in relationship marketing could provide a competitive advantage to fishermen. The published work by Al-Jabri et al. (2013) investigated factors determining income of the small-scale fishermen on Oman's Batinah Coast. It found that fishing inputs, catch, relations with extension services, and other socioeconomic and demographic factors exert positive influence on fishermen's income. All explanatory variables were able to explain 76.4% of the variation in the fishermen's income level. This study furthers the study by Al-Jabri et al. (2013) by investigating whether practices related to SCM and relationship marketing could have additional influence on fishermen's income. Therefore, to address the primary research objective of our study, the analyses are primarily based on the work by Al-Jabri et al. (2013) and investigates to what extent the unexplained variation can be explained by practices related to supply chain management and relationship marketing.

Understanding obstacles affecting supply chains in Oman's fisheries sector and improving their competitiveness is important to this research. This understanding will help in designing solutions to overcome these obstacles and that will lead to improving revenue of the stakeholder and sustainability of the sector.

A Brief Note on Supply Chain Management and Relationship Marketing

Mentzer (2001) argues that the term "supply chain management" (SCM) causes confusion among researchers because it can be viewed as an operational term, a management process, or a management philosophy. He concludes that there should be more effort to identify an exact definition of SCM. Thus, the concept of SCM can be interpreted in different ways, depending on the objectives being addressed by the researcher or the manager, as well as the surrounding circumstances (Mentzer, 2001).

Supply chain management is often defined in terms of managing the flows of products and services--starting from the producer of raw material and ending with the delivery of products to the final customer--through different phases of production and distributional channels using efficient and effective transport, handling, and storage (Schotzko and Hinson, 2000; Dunne, 2001; Collins et al., 2002; Zuckerman, 2002). Food supply and distribution typically requires efficient functioning of a complex set of interrelated activities and services along the supply chain (Shepherd, 1997).

Firms cannot avoid being members of supply chains, and alliances, partnerships, and networks have always existed, though they have only recently been conceptualized as an important part of organized whole-of-chain strategies (Collins et al., 2002). To achieve customer satisfaction in food-business chains, it is necessary to coordinate and form partnerships along the chain so that food products can reach consumers in time as well as in the best quality.

As an integral part of supply chain strategy, relationship marketing plays an important role in managing food supply chains, whether in developed or developing economies. Kurtz (2012:308) defined relationship marketing as "the development, growth, and maintenance of cost-effective, high-value relationships with individual customers, suppliers, distributors, retailers, and other partners for mutual benefit over time". Relationship marketing is based on assuring delivering customer requirements such as low prices, high quality, prompt delivery, and superior service (Kurtz, 2012). Morgan and Hunt (1994:34) refer to relationship marketing as "all marketing activities directed towards establishing, developing, and maintaining successful relational exchanges."

Study Area Profile

The coastline of the Batinah Governorates extends about 270 km along the Sea of Oman and is characterized by a sandy beach with the continental shelf extending up to 30 nmi (Al-Oufi et al., 2000) offshore. In 2012, the Batinah fisheries contributed 16.4% and 25.6% of the total national landings in volume and value, respectively. There were 11,943 registered fishermen in Batinah representing 28% of all registered fishermen in Oman, revealing fisheries as a vital source of income and employment to thousands of families (MAF, 2012). In 2012, the small-scale fishery of the Batinah Governorates represented about 26% of the total number small-scale fishing boats in Oman (MAF, 2012). The Batinah fisheries' share in the country's fish export was about 6% and 4% in volume and value, respectively (MAF, 2011). Overall, the Batinah fisheries provide sustenance to 128 coastal villages in the Governorates.

In terms of landings of species within the Batinah coast, the small pelagic species represented the highest fish landings (about 32.44%) followed by the large pelagic species (about 29.29%) (MAF, 2011). The value of large pelagic species represented about 37% of the total value of landings followed by demersal species (about 32%) in 2011.

Variations in values are influenced by the demand pattern and consumers' taste (MAF, 2011). Most consumers favored large pelagic species such as kingfish, Scomberomorus commerson, and tuna, Scombridae, to the extent that they do not like to substitute with other species. While various fishing gears such as gill-net, trolling lines, hand lines, beach seine, and traps are used by fishermen in Oman, gillnets are the most frequently used gear (Al-Oufi et al., 2000).

Local fish markets are characterised by poor infrastructure. In some areas, the fish market is an open area next to the beach where the fishermen land their catch. In the coastal towns, these markets are generally located where fish are landed. Fishermen are automatically linked with markets on the landing sites. Fish markets usually are close to residential areas along with vegetable shops and other markets. Fish landings and selling usually takes place in the morning, however, in some coastal towns, fish markets operate in the afternoon. In these markets, the traders sell what is caught in the afternoon or what is unsold in the morning market.

Common problems such as low prices, inefficient marketing, poor quality control, and poor catch handling exist in the Batinah fishery. Therefore, there is a legitimate concern that if these problems go unresolved, the small-scale fishery might fail to achieve long run sustainability (MAF, 2003).

Methodology

Data Collection

Sample representation is one of the aspects the researchers have taken into account in order to make inferences about the population of small-scale fishermen in the Batinah coast. The sample's representation is determined by the type of data required (Bernard, 2005). Furthermore, the subject matter, the unit of analysis (the fisherman), and scale of the survey govern the choice of the data collection method, while the objective of the survey should determine the methods to be used (Moser and Kalton, 1985). For their sampling process, Masuku and Kirsten (2003) revealed that rather than just ensuring that the sample represented the population, selection criteria should aim to increase the validity of the collected data.

Furthermore, data collected using random sampling for a high number of geographically dispersed and heterogeneous villages may result in high sampling errors and may not be reliable (Al-Oufi, 1999). Without due care, a random sample can be unrepresentative and, together with a population that is not homogeneous, can produce errors; and using a simple random sample may produce high random errors (Al-Oufi, 1999). However, when the population is distributed randomly, randomisation is possible even when using non-probability sampling (Kish, 1995).

Although this research will produce individual data, the objective is to investigate the nature of the relationships between buyers and sellers and to understand the strengths of, and common factors among, fishermen who earn higher incomes. It also seeks an understanding of the commercial behavior of the producers and buyers. With careful attention to the nature of this research and the associated circumstances, it has been judged that probability-sampling techniques would not be appropriate. Non-probability sampling was found to be more suitable for this research to ensure data validity. Therefore, the use of non-probability-judgment sampling is a justified procedure for this research. Based on the research objective, the type of data, and from whom the data should be collected, the procedure will fulfill the requirements of the research.

A field survey was conducted to elicit views of fishermen using two types of questionnaires--one for those fishermen who were engaged in relationship marketing with a preferred buyer, i.e., traders (identified as Group A), and the other for those fishermen who were not engaged in relationship marketing (identified as Group B). As there was no a priori knowledge about individual fishermen's marketing relationships, the interviewers asked the respondents, prior to commencing the interview, if they were involved in any sort of relationship with the traders.

Questionnaires for both the groups (A and B) contained three different sets of questions. The first and second sets, which were common to both groups, sought information on the backgrounds of the respondents, the nature of their participation in the sector, and the nature of their relationship with the Extension Services Department of the Ministry of Agriculture and Fisheries. The third set of questions, which was administered solely to Group A, was designed to collect information on the nature of the relationship between the fishermen and their preferred traders and the current practices in the area of supply chain management. Altogether, the questionnaire contained 64 questions for Group A and 29 questions for Group B respondents.

The majority of questions were dichotomous in nature. To reduce interviewer bias, 53 research assistants (data collectors) were trained to interview 510 fishermen from 110 villages along the Batinah coast. The respondents from both groups were kept anonymous following the code of conduct by the Ministry of Agriculture and Fisheries, mainly to avoid any inappropriate use of survey information.

There are two major reasons in this research for deciding to use a large number of volunteer data collectors from various villages along the Batinah coast. First is accessibility to fishermen, and second, cost and time constraints. Having over 120 villages scattered along 270 km long coast, with different social and cultural differences, access to fishermen was a major concern to obtain reliable information. Therefore, data collectors were selected from the local area who were enrolled in Sultan Qaboos University. The selected data collectors had the advantage of knowing the region and the fishermen. To ensure data quality, the data collectors were under close supervision and were required to provide day to day feedback at designated control rooms.

Data and Dimension Reduction

A higher number of "non-response" was observed in variables related to income and catch. This was not unusual as it has been commonly observed in socio-economic surveys (Groves et al., 2004). Furthermore, it is noted that these small-scale fishermen were not maintaining records of their income and catch on a regular basis. Finally, following the recommendation by Beale and Little (1975), 39 questionnaires with missing data were eliminated from the analysis to reduce the potential bias.

In an attempt to deal with quantitative measurements of multiple variables, Field's (2005) suggestion was followed and accordingly factor analysis was used as the "dimensionality reducing" technique in this multivariate context. The factor analysis technique has been widely used to overcome the problems of multicollinearity in regression analysis (Batt, 2003). Factor analysis also helped understanding the structure of the dataset as the investigation of relationship marketing involves many components, such as opportunistic behavior, cooperation. information and knowledge, propensity to leave, and power and acquiescence (Morgan and Hunt, 1994).

Two factor analyses were conducted with respect to 1) relationship marketing between the fishermen and their preferred buyer and 2) the supply chain management practices. In the first factor analysis, which was applied to Group A fishermen, 23 variables related to relationship marketing were included. However, the second factor analysis, which was applied to Group A as well, included a set of 15 variables, derived from the supply chain management principles focusing on customers and the right delivery (Collins et al., 2002). These variables were recorded against questions that were asked to assess whether the fishermen were handling their catch in a proper manner and meeting buyers' needs. The factors and their loadings on each variable were determined using the default criteria and the "varimax" method of the SPSS (2) program, respectively.

Econometric Modelling on Relationship Marketing and Its Effect on Fishermen's Income

To conclude whether engaging in relationship marketing could have an impact of fishermen income, several quantitative techniques were adopted. These were done in three steps mentioned below.

Step 1 Computation of Unpredictable Component: Based on an Earlier Study

In this step, we used the study by Al-Jabri et al. (2013) as a basis of further empirical investigation and accordingly the residual is obtained from their estimated logistic regression model. The residual series represents the extent of the unpredictable component (23.6%) and following the objective of this paper, the series is used for subsequent empirical analysis to examine whether the engagement in relationship marketing and adoption of supply chain management practices could explain some of the unexplained (23.6%) variation in fishermen's income.

Step 2 Group Comparison: Independent Sample t-test

An independent sample t-test involving Groups A and B was performed based on the average score of relevant variables (including income) that was common to both the groups and was likely to affect the fishermen's economic performance (Table 1 lists these variables) to determine any significant differences between the groups with respect to the variables.

Step 3 The Heckman Two-stage Procedure

Stage 1: Computing Inverse Mills Ratio It is important to note that when analyzing a subset of the population, censored sampling bias may occur. Therefore, the Heckman procedure was used to construct a selection bias control factor known as the Inverse Mills Ratio (IMR) (Warning and Key, 2002; D'Haese et al., 2005). To compute the IMR. first a dependent (dummy) variable of a relationship group was created where a fisherman in Group A was given a value of "1", and "0" otherwise. A probit model was used and the dependent variable was regressed on potential variables that could determine the probability of a fisherman being from Group A or B (Table 2 lists the variables). (3)

The computed IMR was then used in the second stage of the regression analysis (as discussed below) when estimating the effects of the relationship and supply chain management practices to produce unbiased parameter estimates. The computed IMR was a summary measure that reflected the effects of all unmeasured characteristics related to engagements in relationship marketing. The value of this ratio for each of the respondents was recorded and added, at the same time, to the data file as an additional variable for further analysis (Smits (4)).

Stage 2: Regression Involving Residuals from Step I and Relationship Marketing Variables The concluding stage involved linear regression analysis to examine the impact of relationship marketing on fishermen income. This was achieved by using the residuals from the logistic regression, as determined in Step 1, as a dependent variable against the potential factors associated with the relationship marketing and supply chain management. (5) The potential independent variables were grouped before conducting the regression analysis. The purpose of this grouping was to learn what proportion of total variation a set of variables could explain independently.

Results and Discussion

Factor Analysis

The factor analysis involving 23 variables resulted in five factors (B1, B2, B3, B4, and B5) with eigenvalues greater than 1. Factor B1 explained 24.27% of the variance, Factor B2 explained 12.41% of the variance, Factor B3 explained 7.99% of the variance, Factor B4 explained 7.34% of the variance, and Factor B5 explained 6.73% of the variance. Added together, all factors explained 58.74% of the total variance. The grouping of these factors with their underlying items is presented in Table 3. Having Factors B1 and B2 explaining about 37% of the variance indicated the importance of satisfaction and behavior in explaining the phenomena of relationship marketing between the fisherman and the preferred trader.

The second factor analysis, which involved 15 variables, resulted in three factors (C 1, C2, and C3). The factor C1, "overall buyer satisfaction with product" explained 29.5% of the variance; factor C2, "adding value" explained 22.2% of the variance; and factor C3, "product differentiation and satisfaction with delivery" explained 14.9% of the variance. Altogether, these factors explained 66.6% of the total variance. The grouping of these factors with their underlying items is presented in Table 4. The results of the factor analysis revealed the importance of factors A1, B1, B2, C1, C2, and C3 in terms of their contribution to the study, as measured by the percentage of variance explained by these factors.

Econometric Modelling

Step 1

We gathered from the results of the logistic model used by Al Jabri et al. (2013) that the model explained 76.4% of variation in the fishermen's income levels, and 23.6% of the variation remained unexplained. As mentioned earlier, the residual from the logistic model was computed and used subsequently to investigate how much of the unexplained variation in fishermen's income could be predicted by factors such as "relationship marketing" and "supply chain management practices."

Step 2

Based on 5% significance level (Table 1), out of 19 variables, only the "income" variable indicated a significant difference between the two groups. On average, Group A fishermen (who were engaged in relationship marketing with buyers) experienced higher (about 53.2%) annual incomes than that of Group B. This may suggest group homogeneity with respect to all other variables but income. It led to the conclusion that fishermen in Group A were more competitive than the fishermen in Group B, because they got a better value for their catch due to relationship marketing.

This finding raised a question about the influence of "fisherman-agent" relationship on income. To gain an initial insight on this subject a closed-ended question with the following four potential responses was administered to Group A fishermen to know the type of benefits they gained from their relationship with the preferred trader: 1) a secure income, 2) increased quantity of fish sold, 3) improvement in the quality of catch to meet traders' preference, and 4) better prices.

"Better prices" secured the highest response rate (97.1%), followed by "a secure income," and "an improvement in the quality of catch." Less than one-third of the respondents mentioned that they experienced a gain in the quantity of fish sold. These findings indicated that the fishermen in Group A gained competitive advantage because of the high prices they received.

Step 3

Computing of the IMR From the probit model (Table 2), the Inverse Mills Ratio (IMR) was calculated. There were some unobserved factors that could increase the probability of engaging in a relationship with a buyer, which could be indicated by the concluded significance of the IMR. The model shows that the IMR turned out to be statistically insignificant at the 5% level. The non-significance of the Inverse Mills Ratio ruled out the possibility of any censored sampling bias in estimating the parameters (Warning and Key, 2002; D'Haese et al., 2005).

Linear Regression of the Residual

Accordingly, five groups of predictors were proposed and entered as blocks to the model (Table 5). These five blocks included maintain product quality and buyer satisfaction, trust, agreement and trading, sharing value and information, and familiarity and association with buyers. As shown in Table 5, variables under the "maintain product quality and buyer satisfaction" categories included the predictors on product quality and storage, which produces [R.sup.2] value of 0.420 and the corresponding F-ratio of 5.278 was significant at the 5% level. Model 1 therefore, explained 42% of the variance in the dependent variable.

Because the residual counted for 23.6% of the unexplained variance, so the predictors of quality, product, and storage counted for 9.9% (0.42 x 23.6) of the variance in fishermen's income levels. This result indicated the importance of delivering products that buyers expect. It also indicated the importance of maintaining quality through the use of ice and iceboxes during storage and transportation.

The variables representing trust in Model 2 caused [R.sup.2] to change from 0.420 to 0.543, with a significant F-ratio of 3.169, at the 5% level. The predictors in Block 2 explained 12.3% of the variance in the dependent variable, which accounted for (0.123 x 23.6) 2.9% of the variance in fishermen's income levels. This indicated that mutual trust between the fisherman and his preferred trader can affect fishermen's income. Adding "sharing value and information" as a predictor further caused [R.sup.2] to change from 0.543 to 0.643. This addition changed the F-ratio to 2.984, which was significant at the 5% level. The predictors in Model 3 explained almost 10% of the variance in the dependent variable. Therefore, "sharing value and information" accounted for approximately 2% (0.099 x 23.6) of the variance in fishermen's income.

Model 4 caused an increase in [R.sup.2] value to 0.725. The change was significant at the 5% level with an F-ratio of 2.927. Being familiar with the buyer and the social capital built through the relationship explained 8.3% of the variance in the dependent variable, which accounted for (0.083 x 23.6) about 2% of variance in the fishermen's income.

Finally, Model 5 on the type of agreement and trading caused [R.sup.2] to change from 0.725 to 0.773, with a significant F-ratio of 3.870, at the 5% level. The predictors of Block 5 explained 4.8% of the variance in the residual. Therefore, the nature of the agreement and the amount of catch sold to the buyer accounted for 1.1% of variance in the fishermen's income level.

Therefore, regression analysis of variables on the nature of relationship between the fisherman and practices of supply chain management was able to explain 77.3% ([R.sup.2]) of the unexplained variance (the residual) of logistic regression analysis on the fishermen's income level. This indicated that 18.2% (23.6 x 0.773) of the variance in the income level of fishermen from Group A was explained by these variables.

From the above analysis we concluded that the overall contribution of engaging in supply chain management practices and maintaining a good relationship with traders, associated with trust and good manners, explained 18.2% of the variance in income of the fishermen. This indicated that fishermen's engagement in relationship marketing and supply chain management practices could improve their income by 18.2%.

The regression model results, as shown in Table 6, highlight the important factors in each block that contributed significantly to the final model. The significance of Factor C2 (even at 1% level) indicates the importance of quality in the determination of small-scale fishermen's income in the Batinah coast, particularly in the area of supply chain management. The data also reveal that despite being statistically insignificant, buyer dissatisfaction makes a negative impact on fishermen's income. These two findings altogether indicate that focusing on customers and final consumers, in addition to delivering the right products, is important for the income of the small-scale fishermen in the Batinah Governorates. Furthermore, maintaining good relations with more than one buyer is important, especially when a single buyer refuses to accept the entire catch. As a result, a fisherman would not be forced to discard his catch.

Trusting the preferred buyer was found to have a positive relationship with income. Building trust between both the fisherman and the buyer improved mutual understanding and led to a higher number of deals between them. On the contrary, opportunistic behavior was found to be negatively related to income. Opportunistic behavior reduced trust and caused parties to rethink before the deals, and decreased the number or amount of transactions in the long run.

Holding relationships with different buyers provided more opportunities for outlets for the product. Having some knowledge of the downstream customers of the preferred buyer and the deals (prices) was found to be highly significant in predicting income level. This knowledge provided fishermen an opportunity to avoid selling below market price.

Results revealed that sharing information, which was also a reason for successful relationships, showed positive relationships with income. These findings showed that sharing information between the partners in the supply chain was important in relationships and could improve income. It was also found that over a period of time, as the relationship with the buyer increased, social capital was established and relations became stronger, which in turn, induced discounts and prevented bargaining on prices.

Fishermen's income was found to be higher in cases where they agreed to sell their entire catch to a single buyer. Alternatively, the proportion of a fisherman's catch sold to the buyer was found to be positively related to income, i.e., a higher percentage meant higher income. This implied the importance of maintaining a good relationship to guarantee better income.

Conclusion and Recommendations

One of the strategic objectives of the fisheries sector in Oman is to enhance the income of the traditional fishermen (MNE, 2007). Giving primary consideration to this strategic objective, our study ascertains the important role that the fishermen-trader arrangements can play on the income of the traditional fishermen in the study area. However, the study does not claim that the end results are ideal. This is because the existing fishermen-trader marketing relationship is not mature and lacks transparency in the determination of price, quality, and quantity as the results of these activities failed to show up in national statistics. These factors are likely to cause market imperfections, and thereby lead to inefficient results. Therefore, appropriate strategic actions seem desirable to improve fishermen's share in output price and improve marketing systems.

There are various approaches that can be followed to attend to market imperfections. One approach to addressing market imperfections is through institutionalizing the fishermen-trader arrangements in a collaborative manner and shaping up the arrangements through regulatory measures and incentives (such as providing infrastructures, access to improved technology, etc.) so that such arrangements can generate the most economic value to society, and fishermen can negotiate the price of their products with a greater degree of transparency.

Another approach is to create opportunities for fishermen to access markets and obtain competitive price for their harvests. This option was adopted by the Ministry of Agriculture and Fisheries in April 2014, when it formally established the central wholesale fish market in Al-Batinah Governorate with a goal of improving the fish marketing system in Oman.

The main reasons for the establishment of the wholesale market include, among others, boosting fishermen's income by giving them access to the market to get fair price for their products through competitive bidding and ensuring product quality to protect consumers' health and well-being. This strategic action is certainly a significant step in the right direction as the initiative has the potential for correcting the extent of existing imperfections in the post-harvest sector.

However, to reap the benefits of such strategic action and achieve the intended objectives of the sector stipulated in the 5-year plan appropriate regulatory measures need to be taken to improve its operational efficiency so that the market can meet its economic objectives, and the effort needs to be intensified by including other coastal governorates. The authorities should play an active role in raising awareness among fishermen regarding the potential of the wholesale establishment to improve their economic situation in the long run. Furthermore, it is envisaged that improvement in transportation and storage facilities will play a significant role in improving fishermen's income through preserving product quality. Acronyms used in this paper. MAF Ministry of Agriculture and Fisheries MNE Ministry of National Economy R&D Research and Development FDC Fisheries Development Centre OMR Omani Rial ($1 USD = OMR 0.384, fixed exchange rate)

doi: dx.doi.org/10.7755/MFR.77.4.3

Omar Al-Jabri (Corresponding Author) is Assistant Professor, Department of Natural Resource Economics, Sultan Qaboos University, P.O. Box 34, Al Khoudh, P.C. 123, Sultanate of Oman (email: omar@squ.edu.om). Ray Collins is Emeritus Professor of Agribusiness, University of Queensland, Gatton Campus, Building 8117a, Gatton, QLD 4343, Australia. Ximing Sun is Research Fellow, University of Queensland, Gatton Campus. Building 8117a, Gatton, QLD 4343, Australia. Shekar Bose is Associate Professor, Department of Natural Resource Economics, Sultan Qaboos University, P.O. Box 34, Al Khoudh, P.C. 123, Sultanate of Oman. Rakesh Belwal is Associate Professor, Faculty of Business, Sohar University, P.O. Box 44, P.C. 311, Sultanate of Oman.

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(1) Further details on the socio-economic contributions of the sector can be found in Bose et al. (2010).

(2) Mention of trade names or commercial firms does not imply endorsement by the National Marine Fisheries Service, NOAA.

(3) There is neither theoretical background nor previous research on which variables can predict whether or not a fisherman was engaged in relationship marketing, that is, being a fisherman in Group A or B. In probit models, the independent variables are presumed to affect the choice or category or the choice maker and represent a priori beliefs about the causal or associative elements important in the choice or classification process, the independent variables in this analysis were chosen based on the researcher's presumption that they could predict the dependent variable. The variables entered in the model are the regions, number of weekly trips, engine power, boat length, difficulty of getting ice, difficulty of getting fuel, willingness to invest in another boat, other sources of income, age, ownership of the boat, ability to read and write, and a strong relation with the MAF.

(4) Smits, J. 2003. Estimating the Heckman two-step procedure to control for selection bias with SPSS. Dept. Economics of Nijmegen School of Management, Nijmegen Univ. (Online at http:// home.planet.nl/~smit9354/selbias/HeckmanSPSS.doc) (retrieved 8 April 2007)).

(5) Questions relating to supply chain management and relationship marketing were derived from Morgan and Hunt (1994) on relationship marketing, Batt (2003) on trust, Collins et al. (2002) on the principles of supply chain management, and Warning and Key (2002) and D'Haese et al. (2005) on the contribution of being in a relationship to the fishermen's income. Table 1.--Independent Sample t-test. Variable Mean A Mean B Are you a partner in another fishing boat? 0.373 0.373 Is it difficult to obtain ice? 0.147 0.133 Is it difficult to obtain fuel? 0.240 0.241 Fisherman's age 38.893 40.277 Can you read and write? 0.773 0.687 Is the boat made of fiberglass? 0.773 0.843 Do you have another job or source of income? 0.320 0.337 Are you the owner of the boat? 0.787 0.747 Do you keep income in-house instead of 0.093 0.024 sharing with the crew (if they are relatives)? Are the crew your relatives? 0.747 0.795 Engine power 55.347 50.157 Number of crew 2.267 2.349 Average weekly catch 471.467 308.373 Approximately, what is the average 1895.00 1237.00 annual income from fishing activity? Exchange of information and 0.017 0.157 cooperation with MAF (Factor A1) Strongly involved with MAF (Factor A2) 0.059 -0.205 Trips per week 7.707 6.964 Total weekly fishing cost 61.75 69.41 Boat length 21.13 20.66 Variable t Sig. Are you a partner in another fishing boat? 0.002 0.998 Is it difficult to obtain ice? -0.255 0.799 Is it difficult to obtain fuel? 0.014 0.989 Fisherman's age 0.569 0.570 Can you read and write? -1.250 0.222 Is the boat made of fiberglass? 1.110 0.269 Do you have another job or source of income? 0.230 0.818 Are you the owner of the boat? -0.585 0.559 Do you keep income in-house instead of -1.831 0.070 sharing with the crew (if they are relatives)? Are the crew your relatives? 0.723 0.471 Engine power -1.160 0.248 Number of crew 0.620 0.536 Average weekly catch -1.354 0.178 Approximately, what is the average -2.655 0.009 annual income from fishing activity? Exchange of information and 0.922 0.358 cooperation with MAF (Factor A1) Strongly involved with MAF (Factor A2) -1.684 0.094 Trips per week -1.182 0.239 Total weekly fishing cost 0.659 0.511 Boat length -0.958 0.339 Table 2.-*Probit model coefficients. (1) Predictor b S.E. Z Trips per week 0.055 0.020 2.799 Strongly involved with MAF 0.175 0.086 2.046 (Factor A2) Engine Power 0.004 0.005 0.770 Boat Length 0.028 0.035 0.816 Fisherman age -0.002 0.007 -0.281 Fisherman thinks of 0.223 0.178 1.251 investing in another boat Difficulty getting fuel -0.146 0.193 -0.754 Difficulty getting Ice 0.203 0.214 0.947 Fisherman owns boat 0.271 0.216 1.255 Barka 1.647 0.370 4.456 Masana'a 1.499 0.353 4.243 Suwaiq 1.209 0.318 3.808 Khabora 1.017 0.316 3.222 Sah am 1.364 0.344 3.969 Shinas 0.924 0.356 2.596 Other sources of income -0.059 0.188 -0.314 Fisherman can read and write 0.347 0.228 1.521 Constant -2.634 0.717 -3.676 Predictor P[[absolute Effect value of Z] < z] size r Trips per week 0.003 0.167 Strongly involved with MAF 0.021 0.122 (Factor A2) Engine Power 0.221 0.046 Boat Length 0.209 0.049 Fisherman age 0.390 -0.017 Fisherman thinks of 0.106 0.074 investing in another boat Difficulty getting fuel 0.227 -0.045 Difficulty getting Ice 0.174 0.056 Fisherman owns boat 0.105 0.075 Barka 0.000 0.265 Masana'a 0.000 0.253 Suwaiq 0.000 0.227 Khabora 0.001 0.192 Sah am 0.000 0.236 Shinas 0.005 0.155 Other sources of income 0.378 -0.019 Fisherman can read and write 0.063 0.091 Constant 0.000 -0.219 (1) N = 282; Pearson Goodness-of-Fit Chi Square = 286.300; DF = 264; p = 0.165. Table 3.--Factor analysis of relationship marketing and trust. Factor Factor Total loading Factor Bl: Relational satisfaction 3.641 and cooperation Do you think that your preferred -0.372 buyer cheats you? Have you complained (at least once) to -0.402 your preferred buyer about his commercial behavior toward you? Is there continuous cooperation between 0.782 your preferred buyer and yourself? Are you always friendly toward your 0.633 preferred buyer? Does your preferred buyer always 0.574 keep his promises? Are you convinced that dealing with 0.731 this buyer is of benefit to you? Factor B2: Opportunism and 1 862 commercial behavior Do you think that your preferred 0.511 buyer cheats you? Are there frequent disagreements 0.714 between you and your preferred buyer? Have you complained (at least once) to 0.387 your preferred buyer about his commercial behavior toward you? Do you agree that you are getting more -0.581 benefit out of this relationship than if you sold to other buyers? Do you think that your preferred buyer 0.586 would not abandon your interests even if there were advantages to him in doing so (that is, is he an opportunist)? Factor B3: Power, dependence, 1.198 and acquiescence Have you complained (at least once) to 0.491 your preferred buyer about his commercial behavior toward you? Must you always do what your preferred 0.816 buyer tells you? Do you think that if you change 0.335 and deal with another buyer, you will lose? Do you agree that you are getting more 0.446 benefit out of this relationship than if you sold to other buyers? Are you highly dependent on this buyer? 0.585 Factor B4: Sharing value and information 1.101 Do you think that your preferred buyer 0.754 keeps back some useful information for himself? Do you think that the profits of your 0 782 preferred buyer are much higher than yours? Do you think that your preferred buyer 0.425 would not abandon your interests even if there were advantages to him in doing so (that is, is he an opportunist)? Factor B5: Propensity to 1.010 leave and change buyer Do you think that your preferred 0.355 buyer cheats you? Do you think that if you change to deal 0.531 with another buyer, you will lose? Are you highly dependent on this buyer? 0.332 Do you think that it is not to your 0 826 benefit to stop dealing with this buyer? Initial eigenvalues Factor % of Cumulative % variance Factor Bl: Relational satisfaction 24.271 24.271 and cooperation Do you think that your preferred buyer cheats you? Have you complained (at least once) to your preferred buyer about his commercial behavior toward you? Is there continuous cooperation between your preferred buyer and yourself? Are you always friendly toward your preferred buyer? Does your preferred buyer always keep his promises? Are you convinced that dealing with this buyer is of benefit to you? Factor B2: Opportunism and 12.411 36.682 commercial behavior Do you think that your preferred buyer cheats you? Are there frequent disagreements between you and your preferred buyer? Have you complained (at least once) to your preferred buyer about his commercial behavior toward you? Do you agree that you are getting more benefit out of this relationship than if you sold to other buyers? Do you think that your preferred buyer would not abandon your interests even if there were advantages to him in doing so (that is, is he an opportunist)? Factor B3: Power, dependence, 7.985 44.667 and acquiescence Have you complained (at least once) to your preferred buyer about his commercial behavior toward you? Must you always do what your preferred buyer tells you? Do you think that if you change and deal with another buyer, you will lose? Do you agree that you are getting more benefit out of this relationship than if you sold to other buyers? Are you highly dependent on this buyer? Factor B4: Sharing value and information 7.342 52.009 Do you think that your preferred buyer keeps back some useful information for himself? Do you think that the profits of your preferred buyer are much higher than yours? Do you think that your preferred buyer would not abandon your interests even if there were advantages to him in doing so (that is, is he an opportunist)? Factor B5: Propensity to 6.732 58.742 leave and change buyer Do you think that your preferred buyer cheats you? Do you think that if you change to deal with another buyer, you will lose? Are you highly dependent on this buyer? Do you think that it is not to your benefit to stop dealing with this buyer? Table 4.--Factor analysis of focusing on customers, adding value, and getting the product right. Factor Factor loading Total Factor C1 : Buyer 2.066 dissatisfaction with product Did your preferred buyer 0.815 complain (at least once) that you did not provide him with enough fish (that is, buyer satisfied with quantity)? Did your preferred buyer 0.804 complain (at least once) that he did not get the species he wanted (that is, buyer satisfied with species)? Did your preferred buyer 0.740 complain (at least once) about the quality of your catch (that is, buyer satisfied with quality)? Factor C2: Adding value and 1.555 quality maintenance Do you use ice when fishing? 0.857 Do you have an icebox to keep 0.846 the catch in your boat or at the landing site? Factor C3: Product 1.041 differentiation and satisfaction with delivery Do you grade your fish by 0.893 size and species before delivery to your preferred buyer (do you provide the buyer with a bulk catch)? Did your preferred buyer show 0603 disapproval (at least once) while receiving catch? Initial eigenvalues Factor % of variance Cumulative % Factor C1 : Buyer 29.510 29.510 dissatisfaction with product Did your preferred buyer complain (at least once) that you did not provide him with enough fish (that is, buyer satisfied with quantity)? Did your preferred buyer complain (at least once) that he did not get the species he wanted (that is, buyer satisfied with species)? Did your preferred buyer complain (at least once) about the quality of your catch (that is, buyer satisfied with quality)? Factor C2: Adding value and 22.209 51.719 quality maintenance Do you use ice when fishing? Do you have an icebox to keep the catch in your boat or at the landing site? Factor C3: Product 14.866 66.585 differentiation and satisfaction with delivery Do you grade your fish by size and species before delivery to your preferred buyer (do you provide the buyer with a bulk catch)? Did your preferred buyer show disapproval (at least once) while receiving catch? Table 5.--Group description of predictors of multiple regression with diagnostics. Model and variable Model 1: Maintain Do you have problems associated with product quality and transportation and storage to satisfy buyer satisfaction the requirements of your preferred buyer? Buyer dissatisfaction with product (Factor C1). Adding value and quality maintenance (Factor C2). Product differentiation and satisfaction with delivery (Factor C3) Does the buyer always accept your catch (that is, never rejected)? If your preferred buyer rejects the catch do you bring it to the market immediately or call another buyer? Model 2: Trust How confident are you that your preferred buyer is trustworthy? Opportunism and commercial behavior (Factor B2). Power, dependence, and acquiescence (Factor B3). Propensity to leave and change buyer (Factor B5). Model 3: Sharing Do you often discuss future demand value and information for fish with your preferred buyer? Do you have minimum knowledge of the customers and selling prices of your preferred buyer? In your opinion, do you agree that sharing information is the reason for the success of this relationship? Sharing value and information (Factor B4). Model 4: Familiarity Is this buyer a trucker/agent? and association Do you deal with this preferred buyer with buyer because he is a friend and a known person? How long have you been selling to the preferred buyer named above? How often, per week, do you sell your catch to this named buyer? Model 5: Agreement Do you have an agreement that and trading the catch is only for him? Approximately, what is the percentage of your sales to this buyer? Overall summary Durbin-Watson: 2.83 statistics Model and Cumulative Successive variable [R.sup.2] F-value (d.f.) Model 1: Maintain 0.420 5.28 product quality and (7, 51) buyer satisfaction Model 2: Trust 0.543 3.17 (4, 47) Model 3: Sharing 0.643 2.98 value and information (4, 43) Model 4: Familiarity 0.725 2.93 and association (4, 39) with buyer Model 5: Agreement 0.773 3.87 and trading (2, 37) Overall summary statistics Table 6.--Results of the multiple regression model. Variable B S.E Beta (Constant) -0.604 0.143 Inverse Mills Ratio (LAMBDA) 0.043 0.075 0.055 Do you have problems associated 0.043 0.043 0.111 with transportation and storage to satisfy the requirements of your preferred buyer? Q13 Adding value and quality 0.176 0.022 0.892 maintenance (Factor C2) Buyer dissatisfaction with product -0.036 0.020 -0.209 (Factor C1) Product differentiation and 0.015 0.021 0.077 satisfaction with delivery (Factor C3) Does the buyer always accepts your 0.172 0.130 0.200 catch (that is, never rejected) (Q8) If your preferred buyer rejects 0.240 0.104 0.354 the catch do you bring it to the market immediately or call another buyer? (Q7) How confident are you that your 0.083 0.041 0.212 preferred buyer is trustworthy? (Q23) Opportunism and commercial -0.040 0.017 -0.220 behavior (Factor B2) Power, dependence, and -0.011 0.019 -0.063 acquiescence (Factor B3) Propensity to leave and change 0.017 0.025 0.077 buyer (Factor B5) Do you have an agreement that 0.094 0.041 0.211 the catch is only for him? (Q6) Approximately, what is the 0.002 0.001 0.188 percentage of your sales to this buyer? (Q10) Do you often discuss future demand 0.065 0.054 0.133 for fish with your preferred buyer? (019) Do you have minimum knowledge of 0.126 0.038 0.394 the customers and selling prices of your preferred buyer? (Q21) In your opinion, do you agree that 0.061 0.044 0.126 sharing information is the reason for the success of this relationship? (Q37) Sharing value and information -0.019 0.022 -0.099 (Factor B4) Is this buyer a 0.079 0.045 0.178 trucker/agent? (Q3) Do you deal with this preferred 0.076 0.045 0.200 buyer because he is a friend and a known person? (Q5) How long have you been selling -0.009 0.004 -0.255 to the preferred buyer named above? (Q4) How often, per week, do you sell -0.008 0.014 -0.066 your catch to this named buyer? (Q9) Variable t Sig. (Constant) -4.219 0.000 Inverse Mills Ratio (LAMBDA) 0.577 0.567 Do you have problems associated 0.992 0.328 with transportation and storage to satisfy the requirements of your preferred buyer? Q13 Adding value and quality 7.870 0.000 maintenance (Factor C2) Buyer dissatisfaction with product -1.831 0.075 (Factor C1) Product differentiation and 0.723 0.474 satisfaction with delivery (Factor C3) Does the buyer always accepts your 1.324 0.194 catch (that is, never rejected) (Q8) If your preferred buyer rejects 2.311 0.026 the catch do you bring it to the market immediately or call another buyer? (Q7) How confident are you that your 2.022 0.050 preferred buyer is trustworthy? (Q23) Opportunism and commercial -2.333 0.025 behavior (Factor B2) Power, dependence, and -0.596 0.555 acquiescence (Factor B3) Propensity to leave and change 0.691 0.494 buyer (Factor B5) Do you have an agreement that 2.277 0.029 the catch is only for him? (Q6) Approximately, what is the 1.897 0.066 percentage of your sales to this buyer? (Q10) Do you often discuss future demand 1.194 0.240 for fish with your preferred buyer? (019) Do you have minimum knowledge of 3.331 0.002 the customers and selling prices of your preferred buyer? (Q21) In your opinion, do you agree that 1.387 0.174 sharing information is the reason for the success of this relationship? (Q37) Sharing value and information -0.894 0.377 (Factor B4) Is this buyer a 1.738 0.090 trucker/agent? (Q3) Do you deal with this preferred 1.711 0.096 buyer because he is a friend and a known person? (Q5) How long have you been selling -2.483 0.018 to the preferred buyer named above? (Q4) How often, per week, do you sell -0.550 0.586 your catch to this named buyer? (Q9) N = 59 (Group A).