Journal of Agribusiness in Developing and Em erging Econom ies Role of Power in Supply Chain Performance: Evidence from Agribusiness SMEs in Uganda Journal: Journal of Agribusiness in Developing and Emerging Economies Manuscript ID JADEE-09-2016-0066.R1 Manuscript Type: Research Paper Keywords: power, triad, structural Equations modeling, agribusiness SMEs, Supply chain performance Journal of Agribusiness in Developing and Emerging Economies Journal of Agribusiness in Developing and Em erging Econom ies 1 Role of Power in Supply Chain Performance: Evidence from Agribusiness SMEs 1 in Uganda 2 3 Abstract 4 Purpose: This paper examined the role of power on supply chain performance in the context 5 of small and medium sized agribusiness enterprises (SMEs). Contrary to most of previous 6 studies, which collect and analyze data from one side of a relationship dyad using a focal firm 7 approach, a matched triad approach was employed in data collection and analysis in this 8 study. 9 Methodology: Empirical data was collected from 150 agribusiness supply chain members 10 from the maize supply chain in Uganda. Analysis was done using multi-group analysis and 11 structural equations modelling. 12 Findings: Results highlights the differences in the perception of power use and how it 13 influences supply chain performance. The differences in perception suggest the existence of 14 power asymmetry amongst supply chain members. This work contributes to the ongoing 15 debate concerning the use of triad as a unit of analysis as opposed to a firm or a dyad. 16 Limitations: This study only focused on one commodity chain in one country, which can 17 limit the broad application of the findings. 18 Managerial implications: A practical implication of the finding is that managers of 19 agribusiness supply chains should be aware of their power positions and appropriately 20 influence the supply chain based on their relative power positions. 21 Originality: The novelty of this work lies in fact that we assess perception of power amongst 22 supply chain members in a triadic context, a perspective that has not been adequately tested in 23 agribusiness supply chain management studies before. 24 Keywords: Power, Triad, Structural Equations Modelling, Agribusiness SMEs, Supply chain 25 performance 26 27 Type: Research Paper 28 29 1.0 Introduction 30 The role of power in supply chains presumes a disproportionate distribution of power amongst 31 supply chain members owing to variations in cost structure, size of the organization capability 32 and nature of contracts (Belaya et al., 2009; He et al., 2013; Cuevas et al., 2015; Lacoste and 33 Page 1 of 28 Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 2 Blois, 2015; Rindt and Mouzas, 2015). This implies that supply chain members are 1 interdependent in a complex supply chain structure (Cai et al., 2013; Chicksand, 2015; 2 Odongo et al., 2016). Supply chain management (SCM) literature demonstrates that power is 3 a vital predictor of supply chain performance (SCP) (Molnár et al., 2010; Nyaga et al., 2013), 4 adoption (Liu et al., 2015), innovation capacity (Kühne et al., 2013), and customer 5 integration (Zhao et al., 2008). 6 However, the role of power relations in supply chains is evolving as firms become 7 more complex and multifaceted. Therefore it is important to understand how power is being 8 used by different supply chain members in order to gain control and share profit and 9 ultimately how it affect the supply chain performances (Nyaga et al., 2013; Rindt and 10 Mouzas, 2015). Especially in the context of small and medium sized enterprises (SMEs), 11 power disparity affect firms collaborative behaviors, either due to opportunism or stronger 12 members taking advantage to appropriate greater value of the relationship (Martin K Hingley, 13 2005; Nyaga et al., 2013; Lackes et al., 2015). Hence this study seek to investigate the 14 negative and positive effects of power on supply chain performance and how supply chain use 15 and perceive power (SCM) (Belaya et al., 2009; Liu et al., 2015). 16 Furthermore, there is a limited research on role of power in SCP in the context of 17 SMEs (Adams et al., 2012; Sukwadi et al., 2013). Large organizations are often well 18 equipped and prepared to play the power games in their favor. It is important for the managers 19 in small and medium sized businesses to get a better understanding of the role of power and 20 how to deal with it (Gelinas and Bigras, 2004; Matanda et al., 2016). Additionally, this 21 research has a significant managerial implication in agribusiness sector give that in 22 developing countries such as Uganda are primarily dominated by small businesses (Matanda 23 et al., 2016). 24 Page 2 of 28Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 3 In departure from previous studies, this paper makes three additional contributions to 1 the SCM literature. Firstly, past studies on power in supply chains have focused on its 2 influence on resource allocation (Pulles et al., 2014); power asymmetry (Nyaga et al., 2013); 3 commitment (Zhao et al., 2008); relationship strength (Maloni and Benton, 2000); and 4 performance (Crook and Combs, 2007; Molnár et al., 2010). With a few exceptions such as 5 Molnár et al. (2010) and Kühne et al. (2013), the majority of these studies collect and analyze 6 data from one side of a relationship dyad using a focal firm approach. Analyzing a supply 7 chain at firm or dyadic levels limits understanding the underlying dynamics of the entire 8 supply chain relationships (Molnár et al., 2010; Wu et al., 2010; Kühne et al., 2013; 9 Touboulic et al., 2014). Consequently, there is a need to look beyond the dyad and into the 10 triad as a unit of analysis. 11 Secondly, relationships are by nature bi-directional, as such, there will be differences 12 in perceptions and expectations amongst supply members (Molnár et al., 2010; Wu et al., 13 2010; Nyaga et al., 2013; Pulles et al., 2014; Odongo et al., 2016). Positive outcomes for the 14 whole supply chain will contribute to an individual member’s success (Medlin, 2006; 15 Gagalyuk et al., 2013; Petrick et al., 2016). Consequently, focusing on one side of a 16 relationship dyad limits our assessment and understanding of perceptual congruence amongst 17 supply chain members (Erin Anderson and Weitz, 1992; Mentzer et al., 2001; 18 Rungtusanatham et al., 2003). As such, focusing on the triad as a unit of analysis will 19 facilitate our understanding of how supply chain members perceive power use and its 20 influence on performance (Minna Rollins and Schreiner, 2015). 21 Thirdly, by focusing on agribusiness SMEs in a developing country, this paper departs 22 from most previous studies that focused on large enterprises in developed countries (Sukwadi 23 et al., 2013). Agribusiness SMEs participate in several interlinked supply chains which make 24 Page 3 of 28 Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 4 supply chain relationships a critical issues for their success (Park and Krishnan, 2001; Adams 1 et al., 2012; Sukwadi et al., 2013). 2 Using a triadic approach, this paper examined the perception of power use and its 3 influence on SCP amongst agribusiness SMEs in a developing country context. We 4 hypothesize that the perception of power use and its effects on SCP may not be shared across 5 a relationship triad. The subsequent section presents theoretical perspective and hypothesis 6 guiding the study followed by the methodology, results, discussion and conclusions as well as 7 recommendations drawn from the study. 8 9 2. Conceptual framework and hypotheses 10 This paper uses triadic data collection and analysis to examine a triadic business 11 relationship. Using a triadic approach is appropriate because it enables access to detailed data 12 than would be got using focal firm approach (Minna Rollins and Schreiner, 2015). To 13 facilitate understanding of this triadic power relationships, this study is grounded on the 14 Resource Dependence Theory (RDT). The RDT propagates that firms depend on each other 15 because it is not feasible to be self-sufficient and cost effective (Pfeffer and Salancik, 1978; 16 Belaya and Hanf, 2011; Wynstra et al., 2015). Hence, businesses collaborate to the use each 17 other’s resources and enter into a business relationship (Cai et al., 2013; Murthy and Paul, 18 2016). Furthermore, the extent to which a member is dependent on another member depends 19 on two important factors, i.e., uniqueness of the resource, monopoly over it. Therefore 20 managers in small businesses have to make best possible use of resources, thereof power to 21 operate optimally (Pfeffer and Salancik, 1978). Moreover, perception of supply chain 22 members differs regarding use of power and its influence on SCP (Besser and Miller, 2010). 23 The RDT is therefore relevant in this study and has been used in previous studies to assess 24 power relationships in supply chains (Fynes et al., 2005; Adams et al., 2012; Sanfiel‐Fumero 25 et al., 2012; Cai et al., 2013; Chicksand, 2015; Liu et al., 2015). The application of the RDT 26 Page 4 of 28Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 5 in this study is therefore relevant and important in advancing the conceptual and practical 1 understanding of the role of power in influencing SCP in triadic agribusiness SMEs. 2 3 2.1 Supply chain performance (SCP) 4 We define SCP as the operational measures that improve for each member, as well as for the 5 whole chain as result of participation in a supply chain relationship (Arzu Akyuz and Erman 6 Erkan, 2010; Molnár et al., 2010; Whipple et al., 2010; Gagalyuk et al., 2013). Previous 7 studies have established that collaborative relationships are associated with improved SCP in 8 terms of cost reduction, increased fill rate, reduced inventory, and improved quality (Molnár 9 et al., 2010; Nyaga et al., 2010; Thomas et al., 2011). Further, the supply chain members 10 requires a positive evaluation of the performance outcomes of a relationship in order to justify 11 continued involvement in collaborative activities (Wang et al. (2010). 12 Even though collaborative behaviors result in mutual gains, it is important to stress 13 here that these potential gains may not be equally shared among supply chain members. 14 Previous studies provide evidence of perceptual differences amongst supply chain members 15 with regard to the nature of relationships and SCP (Molnár et al., 2010; Whipple et al., 2010; 16 Kühne et al., 2013; Nyaga et al., 2013). (Erin Anderson and Weitz, 1992) showed that 17 perceptual differences can negatively affect the relationships among chain members and 18 results dissatisfaction and conflict. Similarly, while buyers and suppliers both benefited from 19 collaborations, suppliers had a greater feeling of inequality (Corsten and Kumar (2005). 20 Moreover, supply chain members are likely to possess different sources of power, which can 21 be used to create a certain level of stability or deterrence (Nyaga et al., 2013). 22 This study pre-supposes that suppliers, focal firms and customers differ in their 23 perception of power and its effect on SCP. SCP is measured in terms of efficiency, 24 responsiveness, quality and chain balance. Efficiency is the best use of available resources 25 Page 5 of 28 Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 6 which include measures such as logistic costs and profits (Neely et al., 1995; Aramyan et al., 1 2007). Logistic cost refers to the operating and opportunity cost items that can be influenced 2 by logistic decisions and the integration of management practices and activities throughout 3 the supply chain. Profits are the net positive gains from an investment. Responsiveness is the 4 measure of speed/rate of providing the requested products, hence we considered lead-time and 5 customer complaints (Aramyan et al., 2007; Molnár et al., 2010; Sukwadi et al., 2013). Lead 6 time is the total amount of time which elapses between sending/getting and 7 delivering/receiving goods or services (Gunasekaran et al., 2001). Customer complaint is 8 defined as the formal complaints from customers regarding the product. Product quality 9 means safety and attractiveness while process quality is measured by environmental 10 friendliness (Neely et al., 1995; Chen and Paulraj, 2004; Aramyan et al., 2007). Chain 11 balance is defined as the understanding of distribution of risks and benefits. Risks and benefits 12 distribution refers to the extent to which business risks and compensations are shared amongst 13 supply chain members. Finally, supply chain understanding refers to chain members’ 14 understanding of each other’s products and processes (Molnár et al., 2010). 15 16 2.2 Influence of power on supply chain performance 17 Power has been recognized as an important antecedent of SCP (Geyskens et al., 1999). Power 18 is the supply chain member’s ability to influence the perception, conduct and/or decisions of 19 another (Jonsson and Zineldin, 2003). Research indicates that there is always a power 20 imbalance amongst supply chain members due to the existences of large enterprises with 21 greater power than small ones (Cai et al., 2013; Li et al., 2013; Martin Hingley et al., 2015). 22 Power imbalances usually arise due to differences in expertise, size, dependence, and the 23 nature of contract (Martin K Hingley, 2005; Belaya et al., 2009; Gellynck and Molnár, 2009; 24 Kühne et al., 2013; Li et al., 2013; Jones et al., 2014). 25 Page 6 of 28Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 7 In the context of agribusiness SMEs in Uganda, processors and wholesalers usually 1 have more resources (capital) and better access to market information as compared to their 2 suppliers and customers. This unequal access to resources and information implies that focal 3 firms have a final say on purchasing decisions such as price, quantity, quality amongst others. 4 Consequently, there is more power in with the focal firms (processors and wholesalers) 5 compared to the suppliers and customers. These powerful supply chain members might 6 assume a greater influence and create some stability along the supply chain. Alternatively, 7 powerful supply chain members may use their power advantage at the cost of the weaker 8 members (Belaya et al., 2009; Nyaga et al., 2013; Cuevas et al., 2015; Rindt and Mouzas, 9 2015). Due to their weak position in the supply chain, the weaker members are most likely to 10 comply with the stronger members for fear of losing business. It is therefore important to 11 understand the nature and effects of power in supply chains in order to provide balanced 12 benefit distributions for all supply chain members (Nyaga et al., 2013). 13 Power bases examines the potential reasons why one member may hold authority over 14 another. According to French et al. (1959), power bases include: coercive and non-coercive 15 which indicate the ability of the power holder to mediate punishments or dividends; expert 16 power which is the perception that one member holds information or expertise which is 17 valued by another; referent power, which is one member’s desire for identification with 18 another for recognition by association; and legitimate power where one member believes in 19 the right of the other member to wield influence. Of these power bases, the coercive and non-20 coercive dichotomy is the most apparent and widely recognized power bases (Maloni and 21 Benton, 2000; Bastl et al., 2013). 22 Using the coercive/non-coercive dichotomy, we view power as a mechanism by which 23 one supply chain member induces a desired action of another supply chain member by 24 providing/withholding rewards or punishment. Coercive power occurs when a member’s 25 Page 7 of 28 Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 8 power permits it to affect another member’s share of the benefits of a supply chain 1 relationship. It represents a power struggle driven by force by one supply chain member over 2 another (Skinner et al., 1992). This power struggle may reduce the level of cooperation and 3 performance; and increase the level of conflict and tension in a supply chain relationship 4 hence reduced SCP (Terpend and Ashenbaum, 2012). Past studies has shown that coercive 5 power negatively influences SCP. We therefore hypothesize that: 6 H1a: Coercive power negatively affects efficiency 7 H1b: Coercive power negatively affects quality 8 H1c: Coercive power negatively affects responsiveness 9 H1d: Coercive power negatively affects chain balance 10 11 Non-coercive power is based on rewards and the belief that another member is able to 12 administer positive rewards and minimize negative rewards (French et al., 1959). Non-13 coercive power therefore involves rewards and assistances and increases the value of a 14 relationship through team support, common interests and supporting collective goals (Jonsson 15 and Zineldin, 2003). Previous studies have postulated that non-coercive power has a positive 16 effect on SCP (Zhao et al., 2008; Nyaga et al., 2013), hence, we hypothesize that: 17 H2a: Non-coercive power positively affects efficiency 18 H2b: Non-coercive power positively affects quality 19 H2c: Non-coercive power positively affects responsiveness 20 H2d: Non-coercive power positively affects chain balance 21 22 The conceptual framework underpinning the stated hypotheses is presented in figure 1. 23 Insert figure 1 24 25 3. Methods 26 Page 8 of 28Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 9 3.1 The maize supply chain in Uganda 1 Data for this study was collected from SMEs in the maize supply chain in Uganda. The maize 2 industry is selected for this study because maize is Uganda’s most important non-traditional 3 exports and food security commodity. Maize sector provides a source of livelihood to over 3 4 million households in Ugandan (MFPED, 2016). Much of the maize produced in Uganda is 5 sold to regional markets, especially in Kenya, South Sudan, Malawi, Zambia, and Zimbabwe, 6 (Ahmed, 2012). 7 In Uganda, maize is marketed through two major channels, namely the grain and flour 8 channels. The grain channel is the major channel for maize trade and handles up to 75% of 9 domestically traded maize and 100% of exported maize. Participants in the grain channel 10 include farmers, traders, commodity brokers and seed companies. According to Dalipagic 11 (2014), participants in the grain channel include rural and urban SMEs, and large-scale 12 traders, with rural SMEs constituting about 90%. The flour channel handles maize which has 13 been processed into maize flour, animal feeds and human food products amongst others. 14 Participants in the flour channel is dominated by maize millers, who constitute 85% of the 15 SMEs in this channel. 16 17 3.2 Data collection 18 Primary data was collected between April 2014 and February 2015. We employed face to face 19 interviews with managers of agribusiness SMEs. A matched triad approach (Kühne et al., 20 2015) was used in data collection. Using a matched triad approach helped to minimize the 21 chances of sampling bias (Rungtusanatham et al., 2003; Wuyts et al., 2004; Boyer and Swink, 22 2008). Additionally, the choice of a matched triad approach was done so as to facilitate the 23 subsequent triadic data analysis. 24 Therefore, each supply chain considered had a triplet of supply chain members 25 (supplier, focal firm, and customer). Data collection always started with the focal firms (FF), 26 Page 9 of 28 Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 10 which were purposively identified based on their involvement in the maize supply chain as 1 either a processor or wholesaler. This facilitated the subsequent snowball identification of the 2 supplier (S) and customer (C) by the FF. Each FF was first asked to identify one of their 3 important supplier (S) and customer (C) before being asked to indicate their subjective 4 assessment with respect to their individually chosen supplier (F_S) and customer (F_C). 5 Similarly, each nominated supplier was requested to provide their subjective assessment with 6 respect to the FF that nominated them (S_F); and each nominated customer was asked to 7 provide their subjective assessment with respect to the FF that nominated them (C_F). To be 8 considered for inclusion, suppliers had to be dealing directly in maize or maize products. 9 Therefore, nominated suppliers who were dealing in services such as transportation or other 10 inputs were left out of the interview process. For customers, the inclusion criteria was that 11 they had to be buying maize or maize product directly from the FF that nominated them. In 12 case of a non-response or a mismatch from one of the nominated C of S, the whole supply 13 chain was dropped from the interview process. These perspectives of data collection are 14 summarized in figure 2. 15 Insert figure 2 16 The snowball sampling technique was deemed ideal for the study as little was known 17 about the underlying dimensions of the study population. As such, the ex-ante identification 18 of respondents was not feasible (Molnár et al., 2010). At the end, realized 50 matched triads 19 (150 successful interviews), that is 90% completion rate for the initiated interviews, which is 20 consistent with the snowball method of sampling. The 50 maize supply chains comprised 50 21 suppliers, 50 focal firms, and 50 customers. Most (73%) of the responding firms were small 22 enterprises, who had been in business operations for more than five years. The majority (59%) 23 was involved in the marketing of maize as flour. The firms were involved in the production, 24 processing and marketing of maize in form of flour, feeds, seeds and grains (Table 1). 25 Page 10 of 28Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 11 Insert Table 1 1 2 3.3 Measurements and scaling 3 We adapted the survey measurement items based on similar past studies conducted on power 4 (Kühne et al., 2013), and SCP (Molnár et al., 2010; Wu et al., 2010; Kühne et al., 2013) (see 5 table 2 for statements used). The first section of the questionnaire assessed the supply chain 6 members’ characteristics. The second section examined the perception of power use amongst 7 supply chain members, making use of four statements, representing coercive power and non-8 coercive power sources. The third section assessed perception of SCP, using 11 statements 9 belonging to the four SCP constructs. All items were measured on a 5-point Likert scale (1-10 strongly disagree, 2-disagree, 3-neutral, 4-agree, 5-strongly agree). 11 12 3.4 Analysis 13 Data was analyzed in SPSS version 21 and AMOS 22. Since the constructs were being used in 14 Uganda for the first time, we conducted an exploratory factor analysis (EFA) to assess the 15 uni-dimensionality of the scales (Narasimhan and Jayaram, 1998; Zhao et al., 2008). The EFA 16 with principal component analysis (PCA) was done without specifying the number of factors. 17 Varimax rotation with Kaiser Normalization was used to clarify on the number of factors. 18 Some items were dropped because of cross loadings or low loadings on the respective factors. 19 Cronbach alpha was then calculated for each factor extracted to assess the internal consistency 20 of the extracted components (Janssens et al., 2008). For SCP, four factors with Eigen values 21 greater than one were extracted, explaining 60.17% of the variations in SCP. The four factors 22 generally maintained the original dimensions in which SCP was measured. For power, two 23 factors explaining 87% variation in power were extracted (Table 2). 24 Insert Table 2 25 Page 11 of 28 Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 12 The second stage of analysis involved estimating standardized path estimates so as to 1 assess the causal relationships amongst the constructs using structural equations modelling 2 (SEM). Following James C Anderson and Gerbing (1988), we used a two-step approach of 3 testing a measurement and structural model. A measurement model was built based on the 4 two power and four SCP constructs. The fit indices for the measurement model was good with 5 X 2=94.00, p-value=0.005, GFI=0.94, CFI=0.94, RMSEA=0.05; which fall within acceptable 6 limits for a CFA (Hu and Bentler, 1999; Janssens et al., 2008). We then built a structural 7 model based on the measurement model using the maximum likelihood method. The 8 structural model was modified through co-varying the error terms on efficiency with quality, 9 and quality with responsiveness. The modification resulted in a model with good fit indices 10 (X2=104.04.54, p-value=0.002, GFI=0.93, CFI=0.92, RMSEA=0.05), thus explaining clearly 11 the rationale for the acceptability of the model. 12 13 4. Results 14 For the pooled sample, results show that coercive power negatively and significantly 15 influenced efficiency, quality and chain balance; hence providing support for hypothesis H1a, 16 H1b, H1d (Figure 3). This finding is in agreement with previous studies which suggest that 17 coercive power negatively influences SCP (Sanfiel‐Fumero et al., 2012; Terpend and 18 Ashenbaum, 2012; Sheu, 2015). Although positive, the influence of non-coercive power on 19 SCP was not significant and hence H2 was not supported. 20 Insert figure 3 21 Multi-group SEM analysis revealed differences in the perceptions of power use and its 22 influences on SCP amongst supply chain members. On the upstream, suppliers perceived the 23 use of coercive power by the FFs to significantly and negatively influence efficiency and 24 chain balance. FFs perceived the use of coercive power by suppliers to positively and 25 significantly influence responsiveness. This outcome is counter intuitive, as literature suggest 26 Page 12 of 28Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 13 that coercive power negatively influences SCP (Sanfiel‐Fumero et al., 2012). This result can 1 however be explained by the informal nature of business operations in the maize supply chain 2 in Uganda. In the absences of formal contracts, supply chain members might be forced to use 3 threats, for example loss of contract, to have partners adhere to desired performance standards 4 such as delivery time and quality standards. Looking at non-coercive power, focal firms 5 perceived the use of non-coercive power to positively influence SCP. This results find support 6 in previous studies such as (Sheu, 2015) which suggests that non-coercive power has a 7 positive effect on SCP. The results with respect to the supplier was however not significant, 8 hence inconclusive. Similar studies by (Kühne et al., 2013) in agri-food chains showed 9 indifference on the influence of non-coercive power on SCP. 10 On the downstream, FF did not consider the use of coercive power by the customer to 11 significantly affect SCP. Customers on the other hand perceived the use of coercive power by 12 the FF to negatively and significantly influence quality and chain balance (Table 3). The 13 perception of customers on the influence of coercive power on SCP finds support in literature 14 from previous studies such as Sanfiel‐Fumero et al. (2012) who suggested that coercive 15 power negatively influence SCP. On the other hand, focal firms were indifferent on the 16 influence of non-coercive power on SCP, while customer perceived the use of non-coercive 17 power to negatively influence quality. 18 Insert Table 3 19 Concluding, while our pooled sample results generally provided partial support for H1 20 (H1a, H1b, H1d,), it did not provide support for H2. For the multi-group analysis, we found 21 partial support for both H1 and H2 across the three supply chain members. For H1, there was 22 partial support for H1a (S_F), H1b (C_F), and counter intuitive support for H1c (F_S) and H1d 23 (S_F). For H2, there was no support for H2a, while there was partial support for H2b (C_F), H2c 24 (S_F) and H2d (S_F). The results for the pooled and multi-group analysis support the 25 Page 13 of 28 Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 14 assumption that there are perceptual differences amongst supply chain members on the use of 1 power and its influence on SCP. 2 3 5. Discussion 4 Although most researchers believe that empirical studies on SCP should collect and 5 analyze data from at least three firms in a supply chain (Mentzer et al., 2001; Choi and Wu, 6 2009; Wu et al., 2010), only a few have attempted to do this empirically. This paper provides 7 insights into perception differences amongst supply chain members in a triadic agribusiness 8 SMEs context. The pooled sample results provide partial support H1 (H1a, H1b, H1d). This is in 9 agreement with previous studies which show that use of coercive power has negative effects 10 on SCP (Sakano and Johnson, 1993; James R Brown et al., 1996; Zhao et al., 2008; Terpend 11 and Ashenbaum, 2012; Nyaga et al., 2013). The results underline the informal environment in 12 which agribusiness SMEs operates in Uganda. Because business relationships are non-13 contractual and based on trust, exercise of power will only serve to discourage supply chain 14 members from continuing in a business relationship. In practice, if one member perceives that 15 another member is being coercive, it is most likely to retaliate by declining to make specific 16 required adjustments or collaborate in joint relationship activities. The implication is that 17 agribusiness managers need to control their use of coercive power, as it may be 18 counterproductive to their performance in the long run. 19 The multi-group analysis revealed differences in perception amongst supply chain 20 members on the use of power and its influence SCP. While the perception of suppliers and 21 customers on the use of coercive power is in line with previous studies, there were deviations 22 when it came to the different performance parameters. For suppliers, efficiency and chain 23 balance were significantly influenced by a partners use of coercive power, while for 24 customers, quality and chain balance were critical. For focal firms, the use of coercive power 25 Page 14 of 28Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 15 by the supplier significantly influences responsiveness. This difference in perception reveals 1 that critical SCP parameters vary from one member to another. 2 Contrary to previous studies (Molnár et al., 2010; Terpend and Ashenbaum, 2012), 3 focal firms perceived the use of coercive power to positively influence performance. This 4 could suggest the existence of power asymmetry amongst agribusiness SMEs. This could be 5 the case where there are few suppliers, supplying maize with specific quality requirements to 6 focal firms. Since only few suppliers can meet these quality requirements, suppliers have the 7 power to choose which FF to sell. Hence suppliers can use this power to leverage benefits for 8 themselves. 9 Focal firms perceived the use of non-coercive power to have significant positive 10 effects on responsiveness and chain balance. This is in agreement with previous studies 11 (James R. Brown et al., 1995; Nyaga et al., 2013), which reported a positive association 12 between non-coercive power and SCP. This suggests that the use of rewards and incentives is 13 a strong signal from a member that they value that relationship. By implication, supply chain 14 members need to consider providing incentives, such as rewards and bonuses to their partners. 15 Such incentives will make partners feel they are appreciated and can result in a positive view 16 of the relationship. Customers perceived non-coercive power to have a significant negative 17 effect on the quality. This result is counter intuitive. However, it finds support from a study by 18 Kühne et al. (2013), who concluded that higher levels of non-coercive power use was 19 associated with low levels of innovation capacity in European traditional food chains. 20 Comparing the downstream and upstream, our findings suggest that different 21 performance aspects are perceived differently in the two sides of the supply chain. For 22 instance, while responsiveness is an important factor in the upstream, quality is an important 23 factor in the downstream. This actually reflects the actual situation in the maize supply chain 24 in Uganda. In the upstream, there is an need for faster delivery of products so that processing 25 Page 15 of 28 Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 16 can be done on time. On the downstream, customers are always demanding for quality 1 product, hence the importance of quality. On the other hand, chain balance appears to be 2 critical in both upstream and downstream. This finding underpins the fact that relationships 3 are bi-directional in nature, as such supply chain members will have differences in 4 perceptions towards business relationships. For agribusiness SME managers, it is essential to 5 understand how their business partners perceive the business relationships. For successful 6 business relationships, focal firms should take effort to understand their relative power 7 positions with respect to both the suppliers and customers (Lacoste and Blois, 2015). This is 8 because high levels of power asymmetry leads to more adversarial relationships, as the more 9 powerful partner will tend to be more assertive in the business relationship (Tretyak and 10 Radaev, 2013). Additionally, a lack of understanding of relative power positions of chain 11 member may lead the supply chain members to build and use wrong strategies towards its 12 business partners. Besides showing the differences and similarities between the upstream and 13 downstream, our results also highlight the importance of business relationships to 14 agribusiness SMEs performance (Adams et al., 2012). 15 16 6. Conclusions 17 Results of this study give justification to the use of triad in studying supply chain 18 relationships. Pooled sample results could not reveal the underlying differences in perception 19 amongst supply chain members; which were clearly brought out when multi-group analysis 20 was conducted. Consequently, a triadic analysis exposes the underlying dimensions of a 21 supply chain better than a dyadic or firm level analysis. 22 By collecting and analyzing data at the supply chain level, this paper advances the 23 empirical understanding of supply chain relationships. Theoretically, the results of this paper 24 contribute to the ongoing debate in the supply chain management literature that a firm or a 25 Page 16 of 28Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 17 dyad is heavily influenced by the network in which it operates. This research also advances 1 the resource dependence theory, and builds on previous work by testing the role of power in 2 supply chain performance in an agribusiness SMEs context that has received limited attention 3 in literature. It further contributes to theory by empirically testing the model in a developing 4 country in Africa. 5 6 Managerial implications 7 Several implications can be drawn from this research. First, managers of agribusiness SMEs 8 should be aware of their power positions and use appropriate influences based on power 9 positions in a supply chain. Since coercive and non-coercive power have contrasting effects 10 on SCP, it is important that both power source and power target recognize the presence of 11 power and reconcile their supply chain strategy to take into account power influences. For 12 managers, this implies that being open about their power positions with supply chain 13 members can help to improve on the performance of each member as well as the performance 14 of the whole supply chain. 15 Secondly, SMEs in agribusiness would greatly benefit from trust and relation benefits, 16 this implies that SMEs managers can enhance their positional advantage through realizing a 17 better performance in the supply chain. Building a mutually beneficial relationship is critical, 18 however, that requires a some level of commitment and understanding from all stakeholders. 19 This can be attained by viewing the relationship as an investment wherein a supplier or a 20 customer should be viewed as an extension of the SMEs. It is up to the focal firms to convey 21 this approach to their suppliers and customers. 22 Thirdly, use of rewards and incentives (non-coercive power) is a strong gesture from a 23 member that they value that relationship. Hence, supply chain members may need to consider 24 providing incentives, such as awards, bonuses or performance incentives to their supply chain 25 Page 17 of 28 Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 18 partners. Using incentives make partners feel appreciated and can result into a positive view 1 of the relationship. However, both researchers and managers in agribusiness SMEs should be 2 cautious of the recommendation that use of non-coercive power have a positive effect on the 3 quality. Finally, SMEs in the agribusiness sector need to limit the use of coercive power by 4 investing in the relational variables in order to improve efficiency, chain balance and 5 responsiveness. 6 Limitations and future research 7 Some limitations of this study are worth mentioning. Firstly, the study only focused on one 8 commodity chain in one country, which can limit the applicability of our findings. Future 9 studies could assess power perceptions across different commodity chains and countries to 10 understand if there are differences in perceptions. The second limitation arises from the use of 11 the matched triad approach of data collection. While ideal for studying a triad, this approach 12 is difficult to operationalize in the field especially where there is no established database for 13 SMEs. 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Page 24 of 28Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies 25 Page 25 of 28 Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies List of Table Table 1: Profile of the sample (%) Categorization Supplier Focal firm Customer Business age ≤ 5 years 10 12 10 6-10 years 22 24 32 11-20 years 62 50 46 >20 years 6 14 12 Business size* Micro 32 16 22 Small 68 78 77 Medium - 6 4 Product type Flour 14 82 82 Feeds 50 4 2 Seeds - 14 12 Grains 36 - 4 *1-4=micro, 5-50=medium, >50=medium sized enterprises, Classification based on number of employees (MTIC, 2014) Table 2: Exploratory factor analysis for SCP and power Construct Factor loading Eigen values Cronbach’ s alpha Supply chain performance Efficiency(EFF) 1.79 0.58 Doing business with this XX helps my company to lower transport costs significantly 0.81 Doing business with this XX helps my company to maintain acceptable profitability 0.49 Doing business with this XX helps our company to significantly reduce transaction costs 0.76 Quality(QUA) 1.58 0.52 Doing business with this XX contributes to reducing customer/consumer complaints 0.53 Page 26 of 28Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies Doing business with our XX helps my company to manage product safety 0.75 Doing business with our XX helps my company to produce more attractive products 0.72 Responsiveness(RES) 1.45 0.45 Doing business with this XX helps my company to reduce lead time (time from sending/getting the request till reply) 0.68 Doing business with this XX enable our company to deliver products on time 0.78 Supply chain balance(BAL) 1.19 0.24 Doing business with this XX contributes to a more balanced distribution of risks and benefits along the chain 0.76 Doing business with this XX helps my company to better understand other chain members’ interests 0.70 KMO=0.68; Bartlets tests of sphericity: X 2 =219.11; p=0.000 Power Coercive power(CP) 1.04 0.97 We cannot be sure that this XX will not retaliate on our company (e.g. terminate contract, lower prices) when we do not accept their business proposal 0.97 We cannot be sure that this XX will not neglect our interests (terminate the contract without any notice) even if we fully meet the conditions detailed in the contract with them 0.97 Non-coercive power (NCP) 2.07 0.97 Our company receives benefits from this XX when we regularly meet their requirements (e.g. financial support, market information) 0.73 This XX rewards our company without requiring specific behaviour in return (e.g. financial support, better prices) 0.92 KMO=0.56; Bartlets tests of sphericity: X 2 =118.57; p=0.000 Note: in the interview process, XX would be replaced with Supplier, customer and Focal firm to represent the F- S, F-C; and C-Fand S-F context respectively. Table 3: Standardized path estimation for sub-group specific estimates Paths and perspectives Estimates S-F F-S F-C C-F Coercive power Efficiency -0.61*** 0.43 0.25 -0.20 Coercive power Quality 0.02 -0.58 -0.20 -0.73*** Coercive power Responsiveness -0.16 0.22*** 0.00 -0.04 Coercive Power Chain balance -0.07* 0.23 -0.36 -0.55** Non-coercive power Efficiency 0.22 0.67 0.47 0.06 Non-coercive power Quality -0.01 -0.24 0.04 -0.45* Non-coercive power Responsiveness 0.20 0.16* 0.73 0.16 Non-coercive power Chain balance -0.01 1.16* 0.14 -0.11 Note: 1.*,**,*** indicates significance at 0.05, 0.01 and 0.00 respectively 2. F=focal firm; S=supplier; C=customer 3. S-F=suppliers perception about the focal firm; F-S=focal firms perception about supplier; F- C=focal firms perception about customer; and C-F=customers perception about focal firm Page 27 of 28 Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Journal of Agribusiness in Developing and Em erging Econom ies List of figures CP NCP EFF QUA RES BAL H1a H2a H1b H2 H1 H2 H2d H1d Figure 3: Conceptual framework and hypotheses CP=coercive power; NCP=non-coercive power; EFF=efficiency; QUA=quality; RES=responsiveness; BAL=chain balance Supplier (S) Focal firm (F) Customer (C) Figure 2: Relationship directions considered in data collection and analysis CP EFF QUA BAL -0.22* -0.43** -0.41** Figure 1: Significant paths for the pooled sample CP=coercive power; EFF=efficiency; QUA=quality; BAL=chain balance Page 28 of 28Journal of Agribusiness in Developing and Emerging Economies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60