Harvard Starbucks Building Customer Loyalty Study

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Read the Starbucks' case study. This case study uses Porter's Value Chain and Five Forces models and a SWOT (strengths-weaknesses-opportunities) analysis to develop strategic recommendations. On page 7 of the Starbuck's case study there are 10 recommendations. write on these 3 of the recommendations below:

(1)  Further build and retain customer loyalty, by building on beta concept of on-the-go home delivery

(2) Starbucks growth strategy in the saturated U.S. market should focus on getting additional penetration into untapped rural markets.

(3) Another growth sector is its packaged coffee packets and iced beverage products. Starbucks should build better relationships with big box retailers to get premium shelf space and increase the efficiency of this distribution channel.

and identify how IT could be used as part of the implementation of that recommendation. Attached are 3 PDF peer reviewed articles on how other companies have done something similar for each of the 3 recommendations. Your paper should be at least 3 pages APA, not counting the title and reference pages. The paper must include at least 3 references from peer-reviewed articles (ALREADY ATTACHED PDF) . Make sure you have in-text citations and a reference page. You can include additional references from websites and books.

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MANAGEMENT SCIENCE informs Vol. 50, No. 1, January 2004, pp. 117–131 issn 0025-1909  eissn 1526-5501  04  5001  0117 ® doi 10.1287/mnsc.1030.0165 © 2004 INFORMS Metaheuristics with Local Search Techniques for Retail Shelf-Space Optimization Andrew Lim Department of Industrial Engineering and Engineering Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, iealim@ust.hk Brian Rodrigues School of Business, Singapore Management University, 469 Bukit Timah Road, Singapore 259756, br@smu.edu.sg Xingwen Zhang Department of Industrial Engineering and Engineering Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, xwzhang@ust.hk E fficient shelf-space allocation can provide retailers with a competitive edge. While there has been little study on this subject, there is great interest in improving product allocation in the retail industry. This paper examines a practicable linear allocation model for optimizing shelf-space allocation. It extends the model to address other requirements such as product groupings and nonlinear profit functions. Besides providing a network flow solution, we put forward a strategy that combines a strong local search with a metaheuristic approach to space allocation. This strategy is flexible and efficient, as it can address both linear and nonlinear problems of realistic size while achieving near-optimal solutions through easily implemented algorithms in reasonable timescales. It offers retailers opportunities for more efficient and profitable shelf management, as well as higher-quality planograms. Key words: retail; shelf allocation; metaheuristics History: Accepted by Thomas Liebling, former department editor; received October 31, 2002. This paper was with the authors 5 months for 2 revisions. 1. Introduction Naert (1988), Borin et al. (1994), Urban (1998), and Desmet and Renaudin (1998). With a well-designed shelf allocation system, retailers can improve inventory return on investment as well as raise consumer satisfaction by reducing the likelihood of products being out of stock. More significantly, the retailer can improve the financial performance of the store and increase profit margins while reducing manpower costs (Buttle 1984, Fanscher 1991, Yang and Chen 1999, Yang 2001). While retailers are continually challenged to display new products for the best returns, how space is used impacts on store operating costs due to procurement, carrying, reshelving, and out-ofstock costs (Zufryden 1986, Drèze et al. 1994). Efficient space management can also allow for better brand exposure, which can encourage impulse buying and boost incremental sales (Levy and Weitz 1995, Walters and Bommer 1996) while providing an effective tool for retailers to implement mixed strategies that combine low cost with differentiation (Helms et al. 1992, Yang and Chen 1999). Well-managed shelf-space planning provides the basis for making category-specific merchandising decisions where the traditional space management tool employed is a planogram, which provides a In the highly competitive retail industry, one of the keys to gaining an edge is an efficient shelf allocation system where shelf space is often the retailer’s scarcest resource. As the number of brand lines continually increases, allocating products to the supermarket shelf in the best possible arrangement poses challenges to the retailer. Within the retail industry, user interest in shelf-space allocation is found to be very high. A recent search of the ABI/INFORM database resulted in over 500 references and numerous recent articles in practitioner journals such as Advertising Age, Supermarket Business, Beverage World, and Electronic Business. Chen et al. (1999) emphasize the importance of store space allocation decisions: “In the face of a deluge of new products and the substantial profit opportunities available through slotting allowances, researchers have powerful incentives to make this decision correctly” (p. 216). Indeed, there have been a number of studies on the effects of space on sales, and others that document the positive impact of space allocation on product performance. These include the works of Cairns (1963), Frank and Massy (1970), Curhan (1973), Corstjens and Doyle (1981), Bultez and 117 118 Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization shelf layout of products and a workable method by which merchandising plans can be communicated efficiently. Retailers, together with merchandisers, focus on developing effective visual merchandising plans to maximize profits on a store-by-store basis. With effective planogramming, derived from efficient shelf allocation algorithms, it is possible to leverage every inch of selling space available and to capitalize on available data to meet financial targets (Zufryden 1986, Drèze et al. 1994, Borin et al. 1994, Yang 2001). Due to the problem’s complexity, only relatively simple heuristic rules have been developed and are available for retailers to plan product-to-shelf allocation (Zufryden 1986, Yang 2001). PC-based systems now available, such as Appollo (IRI) and Spaceman (Nielsen), allocate space using turnover and gross profit margins as their criteria and handling and inventory cost as constraints. These are not effective as global optimization tools (Desmet and Renaudin 1998) and are largely used for planogram accounting to reduce time spent on manual manipulation of shelves (Drèze et al. 1994, Yang 2001). In the U.K., Galaxi, developed by Space Solutions and used in one of its largest supermarket chains, Tesco, does not provide optimization of shelf space and operates mainly by a manual drag-and-drop procedure, i.e., drag a product line and drop it onto a shelf (Bai 2003). The objectives of this paper are to present realistic retail shelf allocation optimization models and to offer efficient algorithms that are operationally viable. To achieve these goals, we develop models that are practicable and easily managed, and solution techniques that can deal with industry-size problems within reasonable timescales. Our starting point is with recent research on this problem by Yang and Chen (1999) and Yang (2001), who used a simplified alternative of the well-known Corstjens and Doyle (1981) model. We first develop a network flow solution approach to the problem and then, using many-to-many neighborhood moves, employ a strategy of combining a strong local search with metaheuristics. We then apply these techniques to more complex models that address product groupings and nonlinear profit functions. We summarize related and previous work in §2 and provide formulations of the linear shelf-space allocation, product grouping, and nonlinear profit function models in §3. A discussion of the most recent solution approach and its limitations is given in §4. The remainder of the paper begins with a description of an improved and generally applicable manyto-many neighborhood search technique that is given in §5. We then develop a network model to obtain upper bounds in §6 and a network flow solution in §7. Metaheuristics, including Tabu Search and a hybrid of Squeaky-Wheel Optimization, are developed in §8. Experimental results and comparisons are given in §9, and §10 provides conclusions to this work. Management Science 50(1), pp. 117–131, © 2004 INFORMS 2. Related and Previous Work A recent survey by Yang and Chen (1999) highlights the lack of academic work on this subject. Only 12 references were cited, of which 5 date to the seventies. Further, we find no definitive shelf allocation model(s) and, consequently, no benchmarks are available. Models have been developed in the past 40 years to address the various objectives associated with product-to-shelf allocation. In a very early work, Cairns (1963) proposed a graphical solution to the problem of allocating shelf space to two products to maximize profits while taking space elasticity into account. More recently, space optimization models were developed by Anderson and Amota (1974), Anderson (1979), and Hansen and Heinsbroek (1979). Anderson and Amota (1974) proposed a model that considered optimal brand selection and display area allocation, given a profile of consumer brand preferences. In his work, Anderson (1979) used the relationship between a product’s share of space and its market share to decide space allocation, and provided an integrated theory of profit maximization of display space. The model by Hansen and Heinsbroek (1979) took into account space elasticity and requires a minimum shelf allocation for each product. Cross-space influences were factored in, as were out-of-stock and replenishment costs, and “near-optimal” solutions are obtained using a Lagrange multiplier approach. Corstjens and Doyle (1981) developed a geometric programming model where both product and crossspace elasticities were considered and where profit maximization was the objective. The model highlights sales-space elasticities that gauge the sales response of a given product to the space allocated to another, and was the first space allocation model to incorporate such interdependencies. Costs were modeled as functions of inventory investment and handling, and constraints included store size and upper and lower bounds on space of each category, as well as availability constraints. Their model was solved using a branch-and-bound technique developed by Grochet and Smeers (1979) and was an important landmark in the development of useful space allocation models. Space allocation was found to be dependent on product profitability, demand space elasticity, and product cross elasticity. Corstjens and Dolye (1983) extended the static model to a dynamic one that allows for the anticipation of changing customer tastes and changing product growth and life cycles that could motivate retailers to allocate more space to new products and to divest from declining ones. Zufryden (1986) proposed a model that takes into account space elasticity, cost of sales, and demand-related marketing variables, but neglected cross elasticity between products while fixing nonspace marketing variables. A Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization Management Science 50(1), pp. 117–131, © 2004 INFORMS dynamic programming approach to the model was presented. The work of Bultez and Naert (1988), Borin et al. (1994), and Borin and Farris (1995) builds on the work of Corstjens and Dolye and extends modeling cross elasticities in product category management models. Bultez and Naert’s Shelf Allocation for Retailers’ Profit (SHARP) model is similar to the Corstjens and Dolye (1981) model and optimized space allocation within a product category, taking into account interdependencies within product groups and across groups. They used marginal analysis and a search heuristic to derive solutions. In Bultez et al. (1989), space elasticities were estimated using a symmetric attraction model and later an asymmetric attraction model. The model is restricted to linear cases because the marginal analysis used is not practical for nonlinear models. Borin et al. (1994) addressed product assortment and space allocation in a constrained optimization problem for which a heuristic solution approach was employed, with the study concluding that ignoring out of stock and product assortment effects leads to suboptimality. More recently, Desmet and Renaudin (1998) used the Corstjens and Dolye model framework in an empirical study of product category sales reponsiveness to allocated shelf space where the model is based on a demand function linking the share of sales to the share of space allocated to the product category. The results obtained suggest that space elasticities increase with the impulsebuying rate of the product category. In a large-scale experimental study on shelf management, Drèze et al. (1994) concluded that the position of a product on the shelf is far more important to determining sales than the number of facings allocated to the product, as long as a minimum threshold to avoid out of stocks was maintained. Other recent studies involving space allocation include the works of Urban (1998), who studied the integration of inventory control models, product assortment models, and shelf-space allocation models; and Campo et al. (2000), who investigated the impact of location factors and derived optimal space allocation rules for different location profiles. Yang and Chen (1999) and Yang (2001) developed a model based on the nonlinear model of Corstjens and Dolye (1981). However, because the latter is difficult to apply in realistic situations, Yang and Chen proposed a simplified yet practicable alternative model in the form of a linear multiknapsack integer program. In shelf-space allocation studies, we have found that a number of researchers used mathematical programming techniques for the models they studied (Hansen and Heinsbroek 1979, Corstjens and Dolye 1981, Zufryden 1986, Drèze et al. 1994, Yang and Chen 1999, Campo et al. 2000, Yang 2001), while 119 others employed statistical analysis (Anderson 1979, Walters and Bommer 1996). In Zufryden (1986), simulation techniques were used to generate required parameters. Less often, heuristics have been used. Borin et al. (1994), Borin and Farris (1995), Urban (1998), and Yang (2001) developed heuristic solutions to their models. Borin et al. used a heuristic procedure based on simulated annealing; Urban, in an inventory-theoretic approach to shelf-space allocation, used a greedy heuristic and a genetic algorithm for the solution of an integrated problem, whereas Yang applied adjustment heuristics, which we discuss in §4. 3. Model Formulations 3.1. Background Corstjens and Dolye (1981, 1983) and Zufryden (1986) gave comprehensive models for shelf allocation that were managerially useful and are well known to researchers in the field. In the Corstjens and Dolye models, product space elasticities and cross elasticities are considered within a total profit objective function estimated by product demands and costs in the form of polynomials. The basic constraints were: store shelf capacities, product availability, lower and upperbound constraints on products, and nonnegativity constraints. These models are well integrated and have been used and referenced by many researchers (Zufryden 1986, Bultez and Naert 1988, Borin et al. 1994, Borin and Faris 1995, Drèze et al. 1994, Desmert and Renaudin 1998, Urban 1998, Campo et al. 2000, Yang and Chen 1999, Yang 2001). The models of Corstjens and Dolye and of Zufryden are complex and have practical limitations (Yang 2001). Both present nonlinear or multiplicative profit functions. On the other hand, we find technical simplifications that make retail application unrealistic. For example, in Corstjens and Dolye (1981) there is no provision for the requirement for integer-valued number of displayed products, and in Zufryden (1986) product areas are taken to be multiples of shelf area grid “slot” values. Further, these models do not provide for the location factors emphasized by Drèze et al. (1994). In view of these limitations, Yang and Chen (1999) proposed a simplified integer programming model based on the model developed by Corstjens and Dolye. For ease of reference and continuity, we use the notation provided by Yang and Chen (1999) in describing the model. 3.2. The Corstjens and Dolye Model We assume there are m shelves with length Tk for each shelf k and that the length of the facing of a given product i displayed on any of these shelves is ai for i = 1     n. Let Li and Ui be the lower and upper Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization 120 Management Science 50(1), pp. 117–131, © 2004 INFORMS facings bounds for product i, respectively, and Ai be its total availability. Let xik be the number of facings of product i on shelf k. The demand function can then be given by  Qik xik  = i xikik n  j=1 j=i ij xj L  t=1  ytiti  where i , ik , ij , ti are parameters, where i is a scaling constant, ik is the space elasticity of product i on shelf k, ij is the cross elasticity between product i and j, and ti is the elasticity of product i relative  to a market variable, t for t = 1     L. Here, xj = m k=1 xjk is the total facings of product j and yti is the value of the tth marketing variable w.r.t. to i. In Corstjens and Dolye (1981), using survey data on five product groups and the demand equation fitted by regression analysis, the authors found ik to be predominantly positive and to range between −001 and 019. We can  write the gross cost, ci xi  of product i as i ci xi  = i  m k=1 Qik xik  , where i is a sales volume elasticity associated with the variable cost of product i and i is constant for product i If we assume that the gross margin of product i is linear to its unit margin, gi , we can then express a total profit function, P , by m  n n    P = gi Qik xik  − ci xi  i=1 i=1 k=1 which we maximize subject to the constraints n  i=1 ai xik ≤ Tk Li ≤ m  k=1 m  for k = 1     m (1) xik ≤ Ui for i = 1     n (2) Qik xik  ≤ Ai for i = 1     n (3) k=1 xik ∈ 0 1 2    for i = 1     n and k = 1     m (4) 3.3. The Yang and Chen Model Yang and Chen (1999) made simplifications to the model of Corstjens and Dolye (1981). First, the need for inequality (3) is removed by assuming that retailers can prevent out-of-stock occurrences with good logistics. Second, in the absence of an estimation for i , the sales volume elasticity, they made the assumption that the profit of any product is linear with respect to a range of facings for which it is displayed by controlling the lower and upper bounds for that product. Hence, by letting pik be the per-facing profit of product i on shelf k, we can rewrite the profit objective to be P= m n   i=1 k=1 pik xik  which we maximize subject to constraints (1), (2), and (4). Although researchers such as Bultez and Naert (1988) and Drèze et al. (1994) have found that marginal returns to space first increase and then decrease in an S-shaped curve, we can justify the linearity assumption for the profit function here by the fact that retailers would want to operate on the linear (or approximately linear) and more strongly increasing part of the curve. This can be implemented by using appropriate lower and upper bounds for facings. This integer programming multiknapsack problem model is applicable because there are many integer programming packages available. Yang and Chen (1999) describe a multistage approach using this model, in view of the fact that products are commonly Broken down into departments, categories, and items. We shall refer to this model as the Shelf-Space Allocation Problem (SSAP) in this work. 3.4. Product Groupings and Nonlinear Models We study two useful extensions to the linear model. The first deals with product associations, while the second deals with nonlinear profit functions. 3.4.1. Product Groupings. This extension addresses the merchandising strategy of having products or product categories placed together or apart as arises, for example, from market basket analysis in which tendencies of customers to purchase products together is investigated, and from which “crossselling” (cold medicines with Kleenex, beer, and diapers) and “affinity positioning” (coffee with coffee makers) strategies can be better determined (Anonymous 1995, 1997, 1998). To model this, assuming n products and m shelves, we represent cross-product affinity by an n × n matrix, , where each value !ij ∈ is assigned by the manager and is predetermined. In our experiments, we used this cross-product affinity matrix in such a way that if a facings of product i and b facings of product j are put on the same shelf, an additional profit defined to be !ij × mina b is realized. The additional profit value can be defined differently if required, and this expression was chosen as an example of how such a profit can be implemented. From this, the extended objective is to maximize the sum of the original profit and the additional profit (or penalty); i.e., P1 = P + n−1  n  i=1 j=i+1 !ij yij  where P is the original profit function, !ij ∈ , and  yij = m k=1 minxik  xjk , subject to constraints (1), (2), and (4). Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization Management Science 50(1), pp. 117–131, © 2004 INFORMS Here, for a given product pair i j, the value yij is the sum of minxik  xjk  for all shelves k. When there are more facings of product i and j placed together on the same shelf, the additional benefit achieved is higher (or lower, in case !ij is negative). For example, given two products and three shelves, if we place two facings of Product 1 and four facings of Product 2 on Shelf 1, three facings of Product 1 and one facing of Product 2 on Shelf 2, and zero facings of Product 1 and five facings of Product 2 on Shelf 3, then y12 will equal min2 4 + min3 1 + min0 5 = 2 + 1 + 0. This y12 value is multiplied by the coefficient !12 to obtain the additional profit gained in placing Products 1 and 2 together on the same shelves. Because placing product i together with product j is equivalent to placing product j with product i, only the values of yij when i < j are needed. In our example, we need only compute y12 , not y21 . 3.4.2. Nonlinear Profit Functions. This extension addresses the many situations where profit functions that arise are nonlinear, as, for example, in the case where the profit function follows an S-shaped profile discussed in §3.3. Indeed, the model of Corstjens and Dolye (1981) given here is nonlinear in its objective with a nonlinear product resource constraint (constraint (3)). In Zufryden (1986), we find a similar nonlinear product constraint and in Urban (1998) the objective is to maximize a nonlinear demand function of the allocated space. In Bookbinder and Zarour (2001), a direct product profitability is integrated into shelf optimization where the objective is a sum of nonlinear demand and cost functions. For nonlinear profit functions that arise, many are multiplicative as, for example, in the Corstjens and Dolye (1981) model. As a simple example of a nonlinear profit function, 1/2 the function 5 · xik gives a profit of 5 units when one facing of product i is placed on shelf k, while placing two facings achieves 707 units of profit. We note that this function is an example of the top of an S-shaped curve where the derivative is decreasing. In general, we seek to maximize profit functions of the form P2 = m n   i=1 k=1 F i k xik  where F is a nonlinear function, subject to constraints (1), (2), and (4). 4. Yang’s Heuristics Yang (2001) developed a heuristic algorithm commonly applied to knapsack problems to solve the SSAP. The profit of each item per displayed length on a particular shelf is treated as a weight, and the ranking order of weight is used as a priority index in the process of space allocation. The algorithm consists of 121 three phases. First, a preparatory phase checks for the feasibility of a particular problem and builds a set of priority indexes. Second, an allocation phase allocates available space to items one by one following a priority. This phase is further divided into two subphases, which assure that the lower and upper-bound constraints for the number of facings of a product are not violated. Third, in a termination phase, the objective value of the final solution is calculated. Further, an adjustment phase consisting of three adjustment methods is adopted to improve solutions. Adjustment 1 attempts to improve a solution by swapping one facing for a pair of products allocated on the same shelf. Adjustment 2 interchanges one facing for a pair of products allocated on two shelves. Adjustment 3 is an extension of Adjustment 2, which attempts to allocate shelf space still available after interchanging facings between two products on two shelves. The algorithm is then improved by using an adjustment phase, with different combinations of adjustment methods, after the allocation phase. 4.1. Limitations of Yang’s Heuristics Using a self-defined set of testing data, Yang (2001) claimed the mean profit of solutions after the allocation phase to be 98.2% of the optimal mean profit. After using the adjustment phase, the average profit ratio of the solutions obtained by his improved heuristic to optimal solutions was 99.6%. The test data given by Yang, however, was limited. There was only one group of test data. The total available shelf space was generally more than enough to allocate all of the products in the upper bound. That is, the lower and upper bound had no impact on the resulting feasible solution. Also, the test data scales were limited, where, for example, a range of 0–3 units for product-facings bounds was used, which had little impact on optimal solutions obtained. In the data, all shelves were assumed to have the same lengths and all products had the same lower and upper bounds. Further, it is not possible to determine how the heuristics perform with the small problem sizes used, and how CPU time is affected when the three adjustment methods are added was not described. There were limitations in the heuristic. For example, in the allocation phase, to satisfy the lower bound for all products, the algorithm can allocate a product to a shelf for which there may be far better profit if stocked with other products. Indeed, this is why Yang’s (2001) algorithm requires an adjustment phase. However, the adjustment phase itself may not be sufficient to allocate higher-priority products to their preferred shelves because if the length of the facings of products varied widely, which is often the case, then the three adjustments used could hardly make Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization 122 Management Science 50(1), pp. 117–131, © 2004 INFORMS improvements. For example, if product j occupies shelf k, which is the shelf designated for product i, and the facing of j is 20, while that for i is 400, then all three adjustments fail to make any improvement. However, in this case, it would certainly be possible to interchange 20 units of product j with 1 unit of product i for more profit. 5. New Neighborhood Moves To address some of their shortcomings, we extend the three adjustment methods to multiple-facings adjustments of products. These many-to-many moves are a natural extension of the adjustment methods and are better suited to achieve product allocation to shelves. We describe these new neighborhood moves: Multishift Move. This move attempts to improve the solution by swapping multiple facings for a pair of products allocated on the same shelf. Figure 1 illustrates this move: Shift (remove) two out of three pieces of Product B out, and shift five pieces of Product E in if all constraints are fulfilled, for a profit increase of $5. Multiexchange Move. This move interchanges multiple facings for a pair of products allocated on two shelves. Figure 2 shows two pieces of Product B on Shelf 1 interchanged with five pieces of Product E on Shelf 2 for an additional profit of $25 + $2 − $20− $5 = $2, if all constraints are fulfilled. Multiadd and Exchange Move. This move is an extension of the second move. The key point here is that after interchanging a number of facings between two products on two shelves, there may be enough shelf space left over to be reallocated to other products. Figure 3 illustrates this when two pieces of Product B on Shelf 1 are exchanged with five pieces of Product E Figure 1 on Shelf 2, and there is still space left on Shelf 1 for one unit of Product F. The total profit increases after this operation. The detailed description and verification of these moves are straightforward, and hence omitted here. All three moves ascertain whether the profit after the move is greater than before. However, in using these moves in our algorithms, we look for increases in residual space after the moves are made even if profit remains unchanged. 5.1. Improved Yang’s Heuristics with New Neighborhood Moves For the purpose of evaluation, we replaced Yang’s adjustment moves with the new neighborhood moves and applied these to the algorithm given by Yang. There are six combinations of orderings in total, and performance testing of these showed that the (Multiexchange)-(Multiadd and Exchange)(Multishift) combination option has the highest performance increase. Experimental performance using these new moves is provided in §9, where they are compared with the other algorithms. 6. Upper Bounds for the SSAP In this section, we provide a method to obtain upper bounds for the problem. As the SSAP is NP-hard (Yang 2001), we first make the simplifying assumption that all products are of unit length. We call this form of the SSAP the Unit-Length Shelf-Space Allocation Problem (ULSSAP) and show that the ULSSAP can be transformed into an equivalent Minimum Cost Flow Problem (MCFP). The MCFP is defined on a digraph with node and directed arc sets. Each arc is associated with a cost Multishift Move $10 Shelf 1 Product A Product A Product A $10 Product B Products not in any shelves Prod E $10 Product B Prod E Prod E Product B Prod E Product C Prod E Multi-Shift Operator $10 Shelf 1 Product A Product A Product A Removed products Product B $5 $5 $5 $5 $5 Prod E Prod E Prod E Prod E Prod E Product B Product B Product C Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization 123 Management Science 50(1), pp. 117–131, © 2004 INFORMS Figure 2 Multiexchange Move $10 Shelf 1 Product A Product A Product A Product B $1 $1 Prod E Product D Shelf 2 $10 Prod E Product B $1 $1 Prod E Prod E Product C $1 Prod E Multi-Exchange Operator Shelf 1 Product A Product A Product A $5 $5 $5 $5 $5 Prod E Prod E Prod E Prod E Prod E Product D Shelf 2 and capacity. The flow through each edge is nonnegative and not more than its capacity. Each node, x, is associated with a demand or supply value f x that should be equal to the net flow at the node for any valid flow. The MCFP is to find such a valid flow on the network for which total cost is minimized (Ahuja et al. 1993) subject to flow bound constraints. Orlin (1988) has provided a fast polynomial time algorithm to solve the MCFP with time complexity Om log nm + n log n, where n is the number of nodes and m is the number of arcs in the digraph. Here, we show that the ULSSAP can also be solved Figure 3 $1 $1 Product B Product B Product C with polynomial time complexity via a transformation into a MCFP. To achieve this, we implement three transformations. First, we transform the ULSSAP into a MCFP with directed arcs associated with lower and upper bounds, where the upper bound is taken as its capacity. Second, we transform the MCFP with lower bound into a MCFP without lower bound. Third, we transform the MCFP into a Minimum Cost Maximum Flow Problem (MCMFP) to find a maximum flow, F , such that for any other maximum flow, F , the cost of flow F is less than or equal to the cost of flow F . Multiadd and Exchange Move $10 Shelf 1 Product A Product A Product A Product B $1 Prod E Product D Shelf 2 $10 $1 Prod E Product B $1 $1 Prod E Prod E Product C $1 F Prod E Unallocated Product Multi- Add & Exchange Operator $5 Shelf 1 Product A Product A Product A Prod E $5 $5 Prod E $1 Shelf 2 Product D $5 Product B $5 $5 Prod E $5 $5 $5 Prod E Prod E F $1 $5 B $5 Product Product C Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization 124 1 2 3 Figure 4 Length Lower bound Upper bound Profit Shelf 1 Profit Shelf 2 1 1 1 3 2 1 5 7 5 3 4 5 4 2 2 Network After Transformation Phase I -4 2 [1 ,5 ] ,6 ] 6 -2 -5 5 ] ,8 [0 3 -2 The ULSSAP is transformed into a MCMF from the source node S to the destination node T . If the flow amount of the maximum flow is less than the sum of the lower bounds of all products, then the ULSSAP has no solution; that is, there is insufficient shelf space to satisfy all the lower-bound constraints. As long as there is sufficient shelf space, the cost obtained from the MCMFP is the negative of the optimal profit value that is achievable from the ULSSAP. For the example we have given here, using the MCMF model, we find a maximum profit (upper bound) of 55. 6.3. Proof of Correctness For any instance of the ULSSAP, the transformed network model captures all the constraints. The capacity constraint is given by the edge capacity of the arc from a shelf node to the destination node. The total amount of flow from all product nodes into a shelf node is the same as that from the shelf node into the destination node. Because the flow through an arc is not allowed to exceed the capacity of the arc, the total occupied length of a given shelf cannot exceed the capacity of the shelf. Figure 5 Final Network S [0,3 ] [0 ,2 ] -3 ] 1 ,2 ] -4 4 [0 [0 -4 ,6 [0,5] 0 2 [0 [0,6] 6.2. Transformation Phases II and III After Transformation Phase I, to transform the MCFP with lower bound into a MCFP without lower bound and then into the MCMFP, we follow standard steps as given by Ahuja et al. (1993), for example. Figure 5 illustrates the final network for the given example of two products and three shelves. [0 [2,7] [0,1 6.1. Transformation Phase I The purpose here is to transform the ULSSAP into a MCFP with a lower bound. For a ULSSAP with m shelves and n products, a graph of m + n + 2 nodes is created such that Node 1 to node n represent Product 1 to n, respectively (product nodes), and such that node n + 1 to node m + n represent Shelf 1 to m, respectively (shelf nodes). For the remaining two nodes, Node 0 is the source node and node m+n+1 is the destination node of the network flow. The demand or supply values f x of all nodes x are set to 0. From the source node to each product node k, with 1 ≤ k ≤ n, an arc from 0 to k 0 k, is created with lower bound Lower(k) and capacity Upper(k), and the cost is set to 0. From each product node to each shelf node, there is an arc without lower and upper bounds. The cost of the arc is the negative of the corresponding profit; i.e., for an arc from a node representing product i to a node representing shelf j, the cost of the arc is the negative of the profit achieved by placing one item of product i into shelf j, that is, −pij . Finally, from each shelf node to the destination node, an arc with cost 0 is established. The capacity of the arc is the corresponding shelf capacity; i.e., for an arc from a shelf node representing the shelf j to the destination node, the capacity of the node is Capacity(j). Figure 4 illustrates the network after Transformation Phase I for the given example of two products and three shelves. In this figure, each (L U ) label adjacent to an arc indicates that L and U are the lower and upper bound, respectively. An X next to an arc indicates that the cost of the arc is X. With this labeling, the default value for L is 0, for U it is infinity, and for X it is 0. 4 -4 0 We use a simple example to illustrate the transformation steps of a ULSSAP into an equivalent MCFP. Assume we have two shelves and three products. The capacities for Shelves 1 and 2 are 6 and 8, respectively. Product properties and profits are given in Table 1. -3 1 5] Product Product Properties and Profits [3 , Table 1 Management Science 50(1), pp. 117–131, © 2004 INFORMS ,4 5 3 -2 T 6 -2 -5 ] ] [0 ,8 ] Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization 125 Management Science 50(1), pp. 117–131, © 2004 INFORMS The lower and upper-bound constraints are captured by the corresponding lower and upper bounds of the arc from the source node to the product node. As the only incoming arc for a product node is from the source node, and all outgoing arcs are from the product node to all the shelf nodes, the amount of flow through the incoming arc is the total length occupied by that product. Further, as the amount of flow through each incoming arc is not less than the lower bound and not more than the upper bound, the lower and upper-bound constraints for the product are satisfied. The maximum profit constraint is satisfied by the fact that the MCFP generates a network flow that produces maximum total cost and at the same time satisfies the capacity constraint and the lower and upper-bound constraints. As the cost for the MCFP is minimal and negative, its negative is the maximum profit for the ULSSAP. Lastly, if no solution exists for the ULSSAP, the MCMF model would give a maximum flow value less than the sum of lower-bound values of all the products and infeasibility would be identified. This completes the proof of correctness of the representation of the ULSSAP by the MCFP. 6.4. Catering for Additional Constraints Besides the capacity and bound constraints, the network flow model given here is easily extended for the ULSSAP to handle other types of constraints related to lower or upper bounds. One possible constraint type could limit the amount of a product allocated onto a shelf. For example, we may require that at least three and at most five items of Product 1 are placed on Shelf 2. This constraint could be catered for by setting correct lower and upper bounds for arcs from product to shelf nodes. The network model thus has the advantage of being able to cater for this and similar constraints arising in shelf allocation. 7. Network Flow Model We have shown that a network flow model can be used to solve the ULSSAP to achieve maximum profit. For the general SSAP, products are of variable lengths, and because the problem is NP-hard, we do not expect that the polynomial-time MCFP will be sufficient to solve it directly. 7.2. Integrality Satisfaction Although integral, solutions for the ULSSAP may not be integral solutions for the SSAP. For example, for a product of length 8, the flow model may allocate 12 unit-length spaces of it to a shelf which comprises of 15 product items. This forces fractions of product items to be allocated onto shelves. For each allocation, the fractional part is truncated. For example, if product i is allocated shelf space L on shelf j, after truncation, product i would have space L−remL Length(i), where rema b is the remainder when a is divided by b. The fractional space remL Length(i) is added to the free space of shelf j. After truncation, the capacity and upper-bound constraints will not be violated, although the lower-bound constraint can be. For example, if product k has lower bound Lower(k) = 3 and length Length(k) = 4 and it is allocated to three shelves with space allocations of 5, 6, and 2, then although the total allocated space of 13 = 5 + 6 + 2 exceeds the lower-bound requirement of 12 = 3 × 4, after truncation the remaining space is 4, 4, and 0, and the lower-bound constraint for product k cannot be satisfied. To resolve the lower-bound violation for product k, the following procedure is used. From the source to the sink, we attempt to find a least-cost path to augment the flow for product k until its lower bound is reached. If this is not possible, a maximum cost path from sink to source is located via edges representing products above their lower-bound requirements. Flow Figure 6 Network After Transformation Phase I with Variable Product Lengths -3 1 5] 7.1. -4 [3 , Adaptation of the ULSSAP for Nonunit Lengths We transform the SSAP into a network flow model, using its ULSSAP as a transition step. This transformation is similar to that employed to transform the ULSSAP. The difference here is in the first transformation phase. For arcs from the source node to product nodes, the lower and upper bounds are no longer the corresponding lower and upper bounds of the products. For the arc from the source node to a product node representing product k, the lower bound is (Length(k) × Lower(k)) and the upper bound is (Length(k) × Upper(k)). For an arc from a product node representing product i to a shelf node representing shelf j, the cost is −pij divided by Length(i). We illustrate with the example given above, with three products and two shelves. When the lengths are changed to 1, 2, 1 for Products 1, 2, and 3, respectively, the resulting network graph is shown in Figure 6. 4 [0 ,6 ] -2 [4,14] 0 2 [1 ,5 ] 6 -1 -5 5 3 -2 ,8 [0 ] Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization 126 Management Science 50(1), pp. 117–131, © 2004 INFORMS is reduced along this maximum cost path without violating the lower-bound requirement of any product, and the procedure is repeated to augment product k. This is done until a feasible solution is found or no backflow from sink to source can be found. In the latter case, the algorithm will return a failure, as no feasible solution can be found. If there is a feasible solution, a similar procedure is applied. Here, we choose the product and shelf combination that provides highest profit per unit length. We will attempt to increase the placement of the product on that shelf by one. This is repeated until no improvement is possible. We provide experimental results of the network flow method and comparisons with other algorithms in §9. While no one approach can address every problem that arises, we seek to develop techniques that can be applied to a range of problems. As we know, the SSAP is linear and already NP-hard in complexity. This suggests the development of heuristics for this and other more intractable problems. Specifically, in the next part of this study, we develop artificial intelligence techniques that have not been applied to shelf allocation problems. These include Tabu Search (TS) and Squeaky-Wheel Optimization (SWO). Although other hybrids were developed and tested, their performance was no better than that of TS and SWO, to which we limit our discussion here. We first apply the heuristics developed to the (linear) SSAP and then extend their application to the product grouping and nonlinear models. Elite Candidate List. Careful and intelligent organization in choosing a candidate list is the key when faced with the SSAP’s huge search space. We select a candidate list for iterations by selecting the best K (elite) nodes within each iteration and, in the next run, evaluate these K best nodes to select the K best nodes among the K × K candidate nodes to be the next starting nodes. There are always K nodes evaluated in an iteration. Probabilistic Evaluation. To avoid local optima, evaluations are translated into probabilities of selection. The evaluation function for selecting candidate nodes combines a satisfaction factor that is used to tune search directions together with a random factor. Given a list of candidate nodes, the nodes are sorted by decreasing profit value. The selection process starts from a node with the highest profit value and selects the node with a random probability. The process goes on until K nodes have been selected. The reason for having the random factor is to ensure that the best K nodes are not always selected so as to avoid local optima. Tabu Reactive Strategy. This strategy is aimed at preventing repetition. For the SSAP, we disallow repeat exchanges whenever an exchange/shift/add has been applied to products and shelves. Restarting with Elite Solutions. Whenever there are insufficient suitable K candidates selected for current iteration, we restart. Restarting can be done with the K elite nodes either from the previous iteration or from preceding iterations. Restarting can effectively help prevent searches from repeating. Referent-Domain Optimization Strategy. This is a learning strategy: Products selected often during search are given increased weights. An outline of the TS algorithm follows: 8.1. Tabu Search The iterative TS metaheuristic was developed by Glover (Glover and Laguna 1997). Its strategy is to avoid being trapped in cycles by forbidding moves which take the solution, in successive iterations, to points in the solution space already visited. To avoid retracing steps, the method records recent moves in Tabu lists (Tabu Memory) which forces the search to explore new areas of the search space. At the point of initialization the solution space is checked in a process of “diversification,” but as candidate locations and local optima are found, the search becomes more focused in a process of “intensification.” In effect, TS is basically deterministic, although probabilistic elements can be augmented to it as we do here. For the SSAP, we first obtained an initial solution using the original heuristic without adjustments, and then applied TS strategies, including the following: TABU_SEARCH Run original allocation heuristic to get an initial solution Update the Elite List to contain the initial solution while (current iteration < max iteration)  for each current node of the Elite List  for each products i, j and shelves k, l of current node  Exchange: evaluate (all possible exchanges) remove Tabu active nodes return best K nodes with exchanges Shift: evaluate (all possible shifts) remove Tabu active nodes return best K nodes with shift performed Add: evaluate (all possible products that can be added) remove Tabu active nodes 8. Metaheuristics Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization 127 Management Science 50(1), pp. 117–131, © 2004 INFORMS  return best K nodes with add performed  select the best K nodes for current node  Update Elite List to contain best K chosen from all nodes if (Elite List contains less than K nodes) Restart Experimental results for the TS approach, together with comparisons with other approaches studied, are provided in §9. 8.2. Squeaky-Wheel Optimization In SWO, introduced by Joslin and Clements (1999), a construction algorithm first processes each element of a solution in an order that is determined by priorities assigned to each element based on certain criteria. The solution is then examined to determine which elements are positioned disadvantageously. These elements are deemed to “squeak” because they contribute negatively to the objective function of the solution. These “trouble” elements are then advanced to the front in the ordered priority list so that the construction algorithm handles them earlier when the next solution is constructed. This process of constructing, analyzing, and reordering is repeated, producing a variety of candidate solutions to the problem at hand. In favorable situations, near-optimal or even optimal solutions can be found with this procedure. The basic approach in SWO is to form a ConstructAnalyze-Prioritize three-component cycle. The “constructor” uses priorities assigned to construct a solution, employing a greedy algorithm. The “analyzer” assigns a numerical value as a “blame” factor to each element that has contributed to the shortcomings in the solution constructed in the previous step. The “prioritizer” then modifies the priority list according to the blame factor assigned for each element, by moving elements with greater blames to the front of the list. We propose an enhanced five-phase algorithm for the SSAP that is a hybrid developed by combining the SWO technique with local search heuristics. Because the greedy heuristic by itself may not generate good solutions, SWO fine-tunes the solutions and reorders the priority list so that “trouble” elements can be handled earlier. This process can be viewed as jumping between two search spaces: the solution and prioritization spaces, where a small change in prioritization space can result in a large transformation in the solution space. This allows SWO to find good solutions rapidly. However, there are also many deterministic limitations that need to be considered carefully in designing such an algorithm (Joslin and Clements 1999). We designed the new five-phase SWO with Local Search (SWOL) to overcome possible limitations. Two new main components are added to the core SWO Figure 7 The Five-Phase SWO with Local Search Initialization Initialize ju Ad st Analyzer Bla m Adjustor (Local Search) So lut ion e Prioritizer Constructor o Pr i ze riti cycle: a “special constructor” that is only used for generating the initial solution, and a “local search adjuster” that serves to enhance constructed solutions. This extended five-phase cycle is illustrated in Figure 7. Initialization. Although SWO finds solutions rapidly, it does not guarantee the feasibility of solutions generated. For the SSAP with multiple constraints, the constructed solution tends to violate several hard constraints. To obtain feasibility of solutions, it is important that the initial solution be feasible. Therefore, a good initial solution is important. Blame-Factor Design. The effectiveness of the blame factor is key to the success of SWOL. We have found that using a satisfaction factor is appropriate. A satisfaction factor can be described as a subjective value of how satisfactory the current allocation of a product and a shelf is when compared with its most favorable allocation. To control the scalability of construction so that feasible solutions are constructed, other dynamic values, including the allocation number and an infeasibility penalty, are combined with the satisfaction factor. These are described in greater detail in the next section. We also attempt to control the effect misassigned blame has on the quality of solutions. Due to the way initial solutions are constructed, it is possible for the lower bound of solutions to be violated. Whenever this happens, we have an indication that, in the current solution, the blame values assigned for some products are too low to fulfill its lower-bound requirement. To remedy this situation, we assign additional blame value to these elements so as to force reallocation in the next iteration. Local Search. SWO effectively avoids local optima traps by moving between the two search spaces. Although this characteristic of making large moves is an advantage of SWO, it is also a limitation, causing SWO to be poor at making small tuning moves in the solution space. To improve on this, we combined SWOL to explore neighborhoods of good solutions. 128 Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization Another reason for employing local search is that in certain situations, some elements should be handled badly (given lower priorities) to achieve a good overall solution. SWO alone is unable to achieve good solutions under such situations because high blame is assigned to these elements, leading the search further from optimal because the constructor will handle them first. By performing local search before the analysis component, these elements are made to keep their low priorities. Furthermore, the adoption of adjustments in local search will allow the constructor to avoid taking excessive time in evaluating unnecessary alternatives if the previous configuration already possesses the characteristics of a good solution. 8.2.1. Implementation. Initial Constructor. We use Yang’s (2001) initial heuristic to get the initial feasible solution. This allocation algorithm consists of two steps. The algorithm first checks whether total available shelf space is large enough to allocate products in the minimum amounts required. The first round of allocation aims to fulfill the minimum number of allocations required for each product (referred to as lower-bound constraints), starting with the product having the greatest unit profit, pik /ai , to the product with the lowest. The second round uses a greedy algorithm to allocate each type of product until its maximum allocation is reached, starting from the one having the greatest unit profit to the product with the lowest. A feasible solution is then obtained. The initial constructor can also use a network flow solution. Analyzer. We experimented with various methods of assigning blame. The blame factors that result in achieving near-optimal value performance comprise a satisfaction factor, an allocation number, and an infeasibility penalty. Satisfaction factors are assigned in such a way that a poorly allocated product in current iteration is very likely to be blamed highly so as to get a better allocation in the next iteration. In the implementation, each product i is associated with a shelf best K where it achieves highest profit. If product i is placed on shelf k, the satisfaction factor pi best K − pik /ai is assigned. The allocation number is the current number of facings for product i on shelf k. It is used to ensure that the solution constructed will not be far away from infeasibility because the starting point (initial solution) is feasible. Each subsequent reallocation will not change the total allocation for each product among all shelves; it will only change the distribution among shelves. Thus, the allocation number guides the next SWOL iteration in allocating the number of facings of the product. However, when the allocation is changed, it is possible that the resulting solution becomes infeasible. When this happens, an additional allocation number is added to these “exceptional” products with high blame value so as Management Science 50(1), pp. 117–131, © 2004 INFORMS to ensure they can be reallocated with more facings in the next iteration. For pik ranging from 200 to 7,000, the infeasibility penalty was set to be 105 . Prioritizer. The prioritizer builds an object to record all information from combined evaluation for each nonzero placing. Next, it assigns allocation numbers for each blame factor recorded in this object, together with information on the current product or shelf and the ideal product or shelf. It then goes on to construct the priority list using the object by sorting the products in order of blame value from highest to lowest. For products with zero-value blame factors, a random order will be assigned so that they will be allocated differently for subsequent iterations. A restart is applied whenever solutions are detected to form cycles within the solution space. Constructor. The constructor builds a sequence of allocations based on the priority list guided by the prioritizer. The constructor takes an object one at a time from the beginning to the end of the priority list. There are many different options involved in allocation. The best option tested was chosen. Local Search. This can be the greedy starting algorithm with the three proposed adjustment moves. 9. Experimental Results and Analysis 9.1. SSAP For problems of small size, an optimal profit value Po is obtained from exhaustive enumeration, where To is the actual time taken to compute this profit. For problems of large size, a profit upper-bound value Pub is obtained from the network flow model, where Tub is the time taken to compute this upper bound. For performance comparisons, the upper bound is treated as optimal although any such upper bound is likely to be higher than the actual optimal value. The actual profit obtained and time taken by a heuristic are denoted by Ph and Th , respectively. Simulated problems were generated to test the performance of the adjustment neighborhood moves and the other algorithms. To have accurate and complete performance comparisons between the two sets of adjustment methods, new parameter sets are simulated. In total, there are five sets of parameters generated: m n: the ordered pair of shelves and products; Tk : the length of the shelf k; Li : the lower bound for the amount of facings of product i; 0i : the upper and lower bound difference for the total facings of product i; and ai : the length per facing of product i. To ensure that our samples were general enough, we used a random generator for the parameter sets, within a set range, with a normal distribution. Moreover, to see how selection of parameter ranges can affect solution performance, different ranges are tested for each parameter set. Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization 129 Management Science 50(1), pp. 117–131, © 2004 INFORMS Table 2 Parameter m n ai Li i Tk Table 3 Parameter Values and Ranges for Large Problems Value Range 5 10 5 30 5 50 5 100 10 30 10 50, 10 100 30 50 30 100 [9 values] Random1 A A = 5 10 30 50 100 300 [6 values] Random0 L L = 0 10 30 50 100 [5 values] Random0  = 10 30 50 100 300 [5 values]   RandomTl /4 Tu  Tl = i Li · ai /m Tu = i Li + i  · ai /m In experimentation with Yang’s (2001) results, we implemented his original heuristic (Original Heuristic) without adjustments and then ran the first two phases (Preparation and Allocation) of his algorithm, before applying 1 his adjustment methods (Yang’s Adjustments) and 2 our neighborhood moves (New Neighborhood Moves). The remaining algorithms implemented are as described in the preceding sections: network flow, TS, and the five-phase SWOL. We present the results for large problems first because they are more significant. 9.1.1. Results for Large Problems with Limited Shelf Space. To evaluate the performance of all the algorithms when the number of shelves and products is large and the shelf spaces are limited, we choose the parameter sets to be as shown in Table 2. For problems of large size, Tl and Tu values are cho  sen as Tl = i Li · ai /m and Tu = i Li + 0i  · ai /m, respectively, which are the total shelf length required to allocate all products with the amount equal to their lower bounds or upper bounds, respectively. Thus, the total shelf space is generally sufficient to allocate all products and satisfy the lower-bound constraint, but not enough (limited) to allocate all the products to reach the upper bounds. The profit pik has a uniform distribution between 0 and 10. For each the nine m n pairs, 6 · 5 · 5 · 1 = 150 test cases were run (see Table 2), giving a total of 1350 test cases. Profit and time analysis for large problems are given in Table 3. The results for the nine m n pairs are shown in Table 4. From the results given in Tables 3 and 4, the new neighborhood moves heuristic, the network flow approach, and the fivephase SWOL all outperform the original heuristic and Yang’s adjustments. The five-phase SWOL obtains the Table 4 Profit and Time Analysis for Large Problems (Time in Milliseconds, 10−3 s) Original heuristic Yang’s heuristic New neighborhood moves Tabu search Five-phase SWOL Network flow Average Ph Average (Ph /Pub ) (%) 2403057 2411434 2445856 9674 9724 9891 2407813 2456715 2460330 (Average Pub ) 9726 9952 9946 Average Th Average (Th /Tub ) 198 4888 42181 004 004 015 2646649 2951272 4240279 (Average Tub ) 35094 8760 10 best performance ratio of 99.52% with an average Th of 2951272 milliseconds, well within reasonable limits. Parameter Testing. Experiments were conducted for different parameter sets: A, L, and 0. In all of these, our new neighborhood moves, network flow, and five-phase SWOL heuristics perform much better than Yang’s method, while the TS, the weakest of the new algorithms, does slightly better than Yang’s (2001) method. We provide an analysis for the parameter L below for large problems in Figure 8. Performance for changes in the other parameters, A and 0, were similar to performance for changes in L. 9.1.2. Results for Small Problems. Similar testing was carried out for instances when the number of shelves and products is small. In this case, for m ranging from 2 to 6 and n from 2 to 10, an exhaustive enumeration algorithm was used to compute actual optimal profits (Po ). A total of 800 test cases were used. In these, Yang’s (2001) heuristics fared better, with an average Ph /Po of 97.86%. All new heuristics performed better than Yang’s, with the five-phase SWOL again achieving the best results with an average Ph /Po of 99.16%. Yang’s method obtained an average for Th /To of 0.87, while the five-phase SWOL obtained an average of 48.55 with an average Th of 106.59 milliseconds. Details are given in Table 5. 9.2. Product Groupings and Nonlinear Profit Functions The techniques developed for the linear case were adapted to deal with these models. Specifically, we Detailed Profit Analysis for Algorithms for Large Problems Algorithm\m n 5 10 5 30 5 50 5 100 10 30 10 50 10 100 30 50 30 100 Original heuristic Yang’s adjustments New neighborhood moves Tabu search Network flow Five-phase SWOL Best performance 9509 9580 9828 9647 9702 9904 9696 9746 9931 9715 9778 9949 9640 9683 9857 9687 9725 9891 9731 9779 9927 9673 9718 9837 9768 9800 9893 9762 9891 9934 5-p SWOL 9748 9968 9977 5-p SWOL 9734 9981 9979 Network 9736 9991 9987 Network 9668 9925 9942 5-p SWOL 9710 9957 9954 Network 9736 9979 9967 Network 9676 9880 9899 5-p SWOL 9769 9929 9941 5-p SWOL Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization 130 Management Science 50(1), pp. 117–131, © 2004 INFORMS Figure 8 Performance of Algorithms When the Value of L Changes Original Heuristic Y ang's A djustments New Neigbhd. Moves Tabu Search Netw ork Flow 5-phase SWOL 100.00% 99.50% Ph / Pub 99.00% 98.50% 98.00% 97.50% 97.00% 96.50% 96.00% 0 10 30 50 Values of the Parameter L 100 10. adapted the TS and the five-phase SWOL heuristics, which were found to be suitable for these problems and gave good results. 9.2.1. Results for Product Groupings. We adapted our TS and the five-phase SWOL heuristics to deal with this model by modifying the cost evaluation functions for each operation they performed. Experiments were conducted with parameters set as in Table 2, where m n = 5 10 5 30 5 100 10 30 10 100 30 50 30 100; A = 10 30 50; L = 0 10 20; and 0 = 10 20. In all, 126 test cases were run with pik uniformly distributed between 200 and 7000 and !ij uniformly distributed over −200 and 200. Results were compared against the best profit, Pmax , obtained for both methods. The fivephase SWOL performed best again, with an average Ph /Pmax value of 99.85% and an average Th of 101723 milliseconds. 9.2.2. Results for Nonlinear Profit Functions. We adapted the TS and five-phase SWOL heuristics to address this class of nonlinear problems through Table 5 Profit and Time Analysis for Small Problems (Time in Milliseconds, 10−3 s) Original heuristic Yang’s heuristic New neighborhood moves Tabu search Network flow Five-phase SWOL Exact enumeration Average Ph Average (Ph /Po ) % 5246 5362 5400 9584 9786 9848 5358 5293 5444 5479 (Average Po ) 9773 9641 9916 100 Average Th 122 129 135 532485 306 10659 846350 (Average To ) modifying cost evaluation functions for each operation performed by removing the linear assumption. For our experiments, we tested polynomials and found that the heuristics were able to handle these well, giving good results in reasonable times. For 3/2 example, taking F = cij · xij , where the coefficient cij has a uniform distribution between 200 and 7000, and the product-shelf range and parameters are those for the 126 test cases for product groupings, we compared profits against the best profit instance, Pmax , obtained for both methods and all instances. The fivephase SWOL performed best again, with an average Ph /Pmax value of 99.97% and an average Th of 3772 milliseconds. All experiments were run on a Pentium 4, 2.5 GHz PC with 512 Mb of RAM. Average (Th /To ) 084 087 090 368433 162 4855 10 Conclusion In this paper, we explored the potential of certain metaheuristics and their hybrids for solving a basic shelf allocation problem originally proposed by Corstjens and Dolye (1981, 1983) and recently simplified by Yang and Chen (1999). In our study, we developed exact upper bounds though a network model which we used to determine the efficiency of each technique. Also, we developed a network flow model for the SSAP that gave good results and could be exploited for its network structure. As a starting point of our study, we developed neighborhood moves which could possibly find application in a wider range of shelf allocation problems, as they are easily applied. We found these “manyto-many” moves to be well suited for this problem, having worked well in improving Yang’s basic hillclimbing algorithm significantly. These new neighborhood moves were important in the development of techniques given here and provided the good initial solutions crucial to achieving high-quality solutions. The potential myriad of constraints and competing objectives in shelf allocation problems, emanating from the prerogative needs of the retailing industry and the need to improve on current results, motivated the exploration of various AI techniques in this work. Specifically, a number of metaheuristics and hybrids were studied. These included TS and SWO. To exploit these, we used the strategy of combining a strong local search technique with a metaheuristic, and experimentation has shown that this hybridization strategy worked better than if we used only a metaheuristic. Indeed, the strategy of embedding a local search within an extended five-phase SWOL metaheuristic worked extremely well for the problem at hand. As a step towards addressing more complex models and for testing the techniques developed, Lim, Rodrigues, and Zhang: Metaheuristics with Local Search Techniques for Shelf-Space Optimization Management Science 50(1), pp. 117–131, © 2004 INFORMS we were able to apply some of the heuristics developed here to product grouping and nonlinear models, where they continued to work well. Near-optimal results are important to shelf allocation and even more so when product-shelf numbers are low. In practical applications, including that of producing planograms, algorithms must consistently provide near-optimal or optimal solutions for all ranges considered. The implementation of the five-phase SWOL is significant in that it provides near-optimal solutions for low-number product-shelf ranges while giving consistently good results in all ranges tested, for the linear as well as the nonlinear problems studied within reasonable timescales. Acknowledgments The authors thank the anonymous referees for invaluable suggestions, which helped improve the content of this work and its presentation. Andrew Lim and Xingwen Zhang acknowledge the funding support received from the Logistics and Supply Chain Management Institute, Hong Kong University of Science and Technology, Brian Rodrigues acknowledges funding support received from the WhartonSingapore Management University Research Center, Singapore Management University. References Ahuja, R. K., T. L. Magnanti, J. B. Orlin. 1993. 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Res. 131 107–111. Yang, M., W. Chen. 1999. A study of shelf space allocation and management. Internat. J. Production Econom. 60–61 309–317. Zufryden, F. S. 1986. A dynamic programming approach for product selection and supermarket shelf-space allocation. J. Oper. Res. Soc. 37(4) 413–422. Inf Syst Front (2015) 17:159–175 DOI 10.1007/s10796-012-9392-7 Antecedents of cognitive trust and affective distrust and their mediating roles in building customer loyalty Jung Lee & Jae-Nam Lee & Bernard C. Y. Tan Published online: 8 November 2012 # Springer Science+Business Media New York 2012 Abstract The present research investigates how trust and distrust differently mediate in customer perceptions of various web features in the process of building customer loyalty. Assuming trust and distrust are different in their psychological aspects, we propose that trust is a cognitively active construct, whereas distrust is an affectively active construct. To support this proposal, we select six antecedents of trust and distrust and hypothesize their different relationships as follows: 1) Antecedents with capability-based elements, such as site convenience and content relevance, are associated with trust; 2) Antecedents with relationship-affecting elements, such as customer involvement and web fraud, are associated with distrust; 3) Antecedents with both elements, such as content truthfulness and customer responsiveness, are associated with both trust and distrust. A survey is conducted on 279 online shopping mall users in Korea, and the result shows that most of the foregoing hypotheses are supported. The finding suggests: 1) Trust emerges when customers expect positive result with confidence, thereby implying that it is cognitively activated; J. Lee Bang College of Business, KIMEP University, 4 Abay Avenue, Almaty 050100, Kazakhstan e-mail: junglee@kimep.kz J.-N. Lee (*) Korea University Business School, Anam-Dong Seongbuk-Gu, Seoul 136-701, Korea e-mail: isjnlee@korea.ac.kr B. C. Y. Tan Department of Information Systems, National University of Singapore, 13 computing Drive, Singapore, Singapore 117417 e-mail: btan@comp.nus.edu.sg 2) Distrust emerges when customers suspect that the seller has a vicious motivation, thereby implying that it is affectively activated. From these premises, the present study contributes to the literature by showing how trust and distrust are different, and why they should be managed differently to establish customer loyalty. Keywords Trust . Distrust . Cognition . Affect . Customer loyalty 1 Introduction The importance of trust in online businesses has long been discussed in voluminous research. Trust is an expectation that the other party will not opportunistically behave by taking advantage of the situation (Gefen et al. 2003b). Trust reduces business transaction complexity (Luhmann 1979), facilitates transactions among business parties (Moorman et al. 1993), and accelerates economic exchanges, which all lead to high scales of sales and profit (Barney and Hansen 1994). Trust also has a positive impact on various essential business factors, such as familiarity (Gefen et al. 2003a), subjective norm (Awad and Ragowsky 2008), and privacy (Kim 2008). Therefore, trust is certainly an important element, especially in an online environment (Ridings et al. 2002). In recent years, the presence of distrust in online businesses has attracted the attention of researchers due to its destructive impact on the success of businesses (McKnight et al. 2002). Simply put, distrust is a negative feeling on the conduct of another person (Lewicki et al. 1998). It is an emotional repulsion between people and can also be a fear that one party would not care about or might hurt the other (Grovier 1994). Distrust suppresses economic transactions between parties (Bigley and Pearce 1998). It blocks further 160 business exchanges, especially in online businesses where transactions are not interpersonal. Understanding trust and distrust, such as how they emerge and diminish and how they are related with each other, is considered a high priority by researchers and practitioners because of their crucial effect on businesses (Pavlou and Gefen 2004; Lee and Choi 2011). The antecedents, mediators, and consequences of trust and distrust have been discussed in various contexts and perspectives (Dimoka 2010), whose aim is to guide organizations on how to enhance trust, avoid distrust, and outperform their competitors (Cho 2006). However, despite a significant body of research on trust and distrust (Komiak and Benbasat 2008), how the two can be distinguished from each other has not been fully discussed. For example, in the past, distrust has been considered as simply the opposite of trust (Lewicki et al. 1998). At present, however, trust and distrust are accepted as not necessarily opposite concepts (McKnight and Choudhury 2006). They may independently emerge from the same person (Lewicki et al. 1998) and can be manifested in different mechanisms (Cho 2006). The concept of distrust, possibly as a distinct entity from trust, needs to be investigated for its critical but opposite impact compared with trust. When two factors with opposite effects exist, such as trust and distrust, their dynamics must be identified to avoid misunderstanding between the cause and the effect. Without a clear understanding of the roles of trust and distrust, attributing trust or distrust to the success of a business is difficult. This particular issue on the formation of trust and distrust has been highlighted in recent research (Dimoka 2010; Komiak and Benbasat 2008). This paper aims to identify the characteristics of trust and distrust, especially their psychological aspects. It proposes that trust and distrust are different in psychological status. Therefore, people who trust and people who distrust will respond differently to various external stimuli in online businesses. Through a testing of the associations between certain stimuli and trust and distrust, trust and distrust are anticipated to be differently positioned in the cognitive and affective dimensions and hence elicit different responses to external factors and different mediations of customer loyalty. The study is organized as follows. First, the cognitive– affective dimensions of the human mindset are reviewed. A research model of trust and distrust in an online business context is then proposed. Various antecedents with different characteristics are selected to highlight the psychological differences between trust and distrust. Large survey data from Korean Internet shopping mall users are collected and analyzed using structural equation modeling to validate the hypotheses. Finally, the results of the analysis are interpreted, and the theoretical contribution and practical implications of the study are discussed. Inf Syst Front (2015) 17:159–175 2 Theoretical background 2.1 Cognitive and affective dimensions of the human mindset The psychological foundations of human beings have been extensively discussed in various studies (Fredrickson 2001; Russell 2003) because these foundations convincingly explain why individuals behave the way they do in certain situations. These foundations also provide a theoretical lens for people to understand better how they think and act, which is generally difficult to observe from the outside. A number of researchers, including psychologists, have investigated the brain activities of humans and have developed strong foundations as reflected in various theories and practices (Fredrickson 2001). In the development of their studies, researchers used various ideas and advanced tools, such as f-MRI, which were of great help (Dimoka 2010). The use of cognition and affect as two important, exhaustive, but highly distinguishable psychological units that mediate consumer perception on behavior has been repeatedly practiced in the literature (Homburg et al. 2007). According to the cognitive–affective systems theory of personality, individuals differ on how they categorize and encode situational stimuli and on how these encodings activate such stimuli through the complicated mediation of cognition and affect on human behavior (Mischel and Shoda 1995). In addition, McAllister (1995) and Chua et al. (2008) investigate the configuration of the cognitive and affective dimensions of trust as important business factors with distinguishable effects. The cognitive dimension of the human mind captures how people judge and assess an object based on facts and evidence (Chua et al. 2008). This dimension connotes the straightforward and conscious process of being aware of an event. Cognition is a knowledge-based, immediate understanding of exogenous stimuli, such as a partner’s capability and potential (McAllister 1995). It can also subsequently form beliefs or expectations based on reason and rationale, such that it works as a base for an individual’s further behavior. The cognitive aspect of an individual emphasizes the perception based on reason and evidence. Chua et al. (2008) calls it as “referring to [the] head.” The affective dimension captures how people “feel” about a subject. Affect is a state of mind that arises from one’s own emotion and a sense of others’ feeling and motives (Chua et al 2008). It is an emotional bond between parties which does not necessarily result from reasoning and understanding but from feeling and sense (Morrow et al. 2004). Unlike cognition, however, affect is divided into two types, namely, positive affectivity such as happiness and pride (Fredrickson 2001), and negative affectivity such as worry and anger, which need to be discussed separately Inf Syst Front (2015) 17:159–175 because of their different functions ( Watson and Clark 1984). Chua et al. (2008) calls affect as “referring to [the] heart.” Regarding the relationship between cognition and affect, whether cognitive activity is a necessary pre-condition of emotion has long been an issue of discussion. In some studies, the causal relationship between cognition and affect—that cognition influences affect—has been proposed and exercised (Chang and Chen 2009; McAllister 1995). These studies argue that the cognition unit (i.e., expectation and beliefs) should be formed first based on encoded information in order to build affect. The expectations and beliefs then shape affective responses with psychological reactions. Based on this view, cognition comes before affect. However, cognition and affect do not necessarily have causal relations; rather, they are independent units. Zajonc (1980) has argued that affect is precognitive in nature as it occurs without any extensive perceptual and cognitive processes. In addition, Hoch and Loewenstein (1991) propose that feelings of desire that consumers often experience in shopping situations may occur with little or no cognition. Berkowitz (1993) proposes that an experiential system, which is affective in nature, and a rational system, which is cognitive in nature, tend to operate in parallel in any given task. All these studies relax the causality between cognition and affect, but they also admit that affective reactions can happen automatically without active higher-order cognitive processes. 3 Research model and hypotheses A research model of trust-distrust in online business context is proposed (Fig. 1). Electronic commerce has now become one of the largest business sectors (Nielson report 2008) and numerous studies have attempted to identify the roles of Fig. 1 Research model 161 trust and distrust in online business to understand the behavior of online consumers (McKnight et al. 2002; Pavlou and Gefen 2004). Further, as antecedents of trust and distrust, site convenience, content relevance, content truthfulness, customer responsiveness, customer involvement and web fraud are selected. The selection is made among the key factors in online business (Dai et al. 2008; Tam and Ho 2006) in a way to highlight the difference between trust and distrust. Trust and distrust are then set as cognitively- and affectively- activated mediators, respectively. Finally, customer loyalty is selected as a dependent variable for its significance in online business (Srinivasan et al. 2002). In the following sections, the variables and their relations will be described in detail. 3.1 Business value: Customer loyalty to the website Loyalty refers to the overall attachment and deep commitment to a product, service, brand, or organization of a buyer (Oliver 1999). Loyal customers not only spend more than usual customers, but they also act as enthusiastic advocates for the business (Harris and Goode 2004). Therefore, loyal customers are considered as the most important profit source of online businesses (Flavián et al. 2006). In the literature, many sources of customer loyalty have been identified. For example, factors such as customer satisfaction (Chang and Chen 2009) and trust (Aydin and Özer 2005) are found to be the important cursors of customer loyalty. Functional factors, such as convenience (Srinivasan et al. 2002) and website quality (Chang and Chen 2009), are also found to be the antecedents of loyalty. To form a definition, a group of researchers have attempted to integrate the behavioral aspect of loyalty with the attitudinal aspect. For example, Assael (1992) and Srinivasan et al. (2002) view loyalty as a “favorable attitude toward the brand (product, seller) resulting in consistent purchase over time.” 162 Keller (1993) defines it as “favorable attitude manifested by repeated purchasing.” However, when researchers empirically validate customer loyalty, behavioral aspects have been the critical indicators of loyalty affecting the feasibility of measurement. In the present study, the referent of customer loyalty is the website and not the seller or product. This is because the long-term relationships of most online shopping mall users are built based on the website and not on the specific product or seller. For example, Amazon.com users visit and purchase from Amazon.com because they have faith in the website. Amazon.com users may change the product they purchase or the sellers from which they buy, but they hardly change the website from which they purchase. Therefore, the current study views that loyalty is built toward the website and defines it as “the overall attachment to a certain website with favorable attitude manifested by repeated purchasing.” 3.2 Mediators: Cognitive trust and affective distrust To conceptualize trust and distrust, this study investigates the psychological aspects of both factors and proposes that trust is a cognitively activated construct, whereas distrust is affectively activated. Such difference can be argued based on their roles that trust encourages customers to buy the product, whereas distrust restrains customers from buying, hence making them passive in the business. In online businesses, trust is a factor that makes customers actively participate. It changes customer behavior from not buying to buying. Customers, on the other hand, become active when expecting positive results. Customers think, assess, and judge the situation of a business, and if the desired outcome is anticipated, customers build trust and become active. In other words, unless an evident reason can be based on rationales, customers are usually passive in the business and do not purchase products. The tendency of most customers is to make business decisions based on reasoning and evidence before they decide to enter transactions with their partners. This process is cognitive in nature. In contrast, distrust is a factor that inactivates customers. It blocks, inhibits, and restrains business transactions and changes customers from being active to passive or keeps them passive. Unlike trust, it is not only a negative anticipation of the result but can also be in various forms including “just a feeling of insecurity,” which makes customers passive. Most customers are risk aversive (Mas-Colell et al. 1995). Hence, although the transaction has no evident reason to result in damage, customers often become passive and decide not to buy. Based on the idea that trust is a cognitively activated construct and distrust is an affectively activated construct, Inf Syst Front (2015) 17:159–175 the details of the psychological statuses of trust and distrust are explained in the following sections. 3.2.1 Cognitively-activated trust In online businesses, trust refers to the belief of customers that the seller will transact in a manner consistent with the customers’ expectations (Pavlou and Gefen 2004). Such expectation can be formed based on the sellers’ competency and credibility (Dimoka 2010), which conceptually include various capabilities such as professionalism and punctuality (McKnight et al. 2002). Trust is driven by knowledge on the trustee and sensitive to the calculated risks and rewards (Johnson and Grayson 2005). Hence, it often reflects the functional competency and predictability of the trustee (Rempel et al. 1985). Dimoka (2010) also recently stated that trust is associated with brain areas linked to anticipating rewards, predicting the behavior of others, and calculating uncertainty. These characteristics indicate that building trust is a cognitively activated situation. Forming trust relies on rational evaluation and available knowledge and is based on good reasons (Jeffries and Reed 2000). Trust influences customers to initiate and continue business with their partners based on the knowledge he acquires about the trustee, reasoning, and judgment and not on their feelings and hunches. Various studies on trust also stated that trust is initiated by cognition and not affection (McAllister 1995). Cognitively initiated trust may lead, but not necessarily, to affectively initiated trust. Trust has been considered critical in establishing longterm business relationships (McKnight et al. 2002). Once trust between parties is formed, the relationship between parties can last longer by overcoming uncertainties and reducing perceived risks (Moorman et al. 1993). Naturally, trust can be argued to strengthen customer loyalty, as many previous studies have indicated (Harris and Goode 2004; Sirdeshmukh and Singh 2002). Therefore, the following hypothesis is proposed: H1: Trust is positively associated with customer loyalty in online businesses. 3.2.2 Affectively-activated distrust In literature, distrust is defined as a strong negative feeling regarding the conduct of another person (Lewicki et al. 1998). It is the concern that the other person does not care about one’s welfare and thus may act in a harmful manner (Grovier 1994). More intuitively, distrust is a frantic, fearful, frustrated, and vengeful feeling (McKnight and Choudhury 2006). These negative characteristics of distrust suppress transactions and result in business failures once it emerges. Inf Syst Front (2015) 17:159–175 Distrust is an affectively activated construct that generates a repulsive force between two parties. To emerge, an affectively activated construct does not have to go through higher order cognitive processes (Zajonc 1980) because the feeling often arises with little or no cognition (Hoch and Loewenstein 1991). For example, distrust can emerge only when the other person is suspected of being capable of doing harmful things (Sitkin and Roth 1993). With or without strong cognitive processes, distrust, which is an affectively activated notion, can arise. Distrust affects the behavior of an individual because it is an affectively strong notion. Affection is one of the most important psychological statuses that determine the subsequent action (Mischel and Shoda 1995). In this case, distrust is a construct with a negative impression. Therefore, it also has a negative impact on customer behavior. More intuitively, when customers suspect the competency and intention of sellers, their fears and worries will eventually decrease their loyalty to the sellers. Therefore, the following hypothesis is established: H2: Distrust is negatively associated with customer loyalty in online businesses. 3.3 Antecedes of trust: Capability-based factors Trust is formed when an individual can have positive expectations on the result of the transaction. It emerges when a partner appears to have sufficient competence to complete the tasks on time, as promised, and has credibility such that an individual does not have to worry about the future. Trust is strongly determined by the capabilities of a partner. Numerous studies have identified the partner’s specific capabilities, such as know-how and knowledge, as the main foundations of the trust (McKnight et al. 2002). 3.3.1 Site convenience The concept of convenience has been practiced in various business contexts for a long time (Berry et al. 2002; Seiders et al. 2000). However, one generic feature of convenience is that it saves the time and effort of customers. While efficiency, which also involves a reduction in time and costs of the business, considers the organization as referent, convenience involves a reduction in the time and effort of individuals, not of the organization, and considers the customer as the referent. In online business literature, convenience has been especially important because it is one of the powerful drivers that lead consumers from offline to online purchases. Many people started to do online shopping because of the convenience involved (Bhatnagar et al. 2000). Online shopping mall users face various situations, and each time, the feature 163 of convenience is of different levels whenever they access, search, and pay, among others. Accordingly, numerous ecommerce studies have been conducted on convenience and have identified the sub-concepts, antecedents, results, and dimensions of convenience (Fassnacht and Koese 2006; Seiders et al. 2007). The original concept of convenience is simple but it has well developed with enriched contexts in e-commerce. Websites that are conveniently designed help customers a lot, and business is also smoothly facilitated. In turn, customers who experience convenience when purchasing from a website will have positive expectations about the business because of the time and effort they saved in transactions and the easy achievement of their goal. A conveniently designed website is one of the competencies of online shopping malls, which has also become the foundation of the development of trust for customers. From this, the following hypothesis is proposed: H3: Site convenience is positively associated with trust in online businesses. 3.3.2 Content relevance Relevance refers to the meaningful relation between two objects under a certain circumstance, which are engaged in a common activity and have a purpose (Allwood 1984). The two relevant objects are not simply related with each other but are “meaningfully” connected. In online businesses, this meaningful connection is practiced when a customer searches for “relevant” product information from data on the web. Customers with certain purchase purposes look for relevant product information because not all web data are relevant to customers. Hence, content relevance in this study would be defined as the meaningful relation between product information on the web and the customers whose purpose is finding product information about the product or service that he is interested in. For these reasons, content relevance — how much the information on the web is relevant to the objective of the web browsing — is considered important in online businesses. It was proposed as a sub-concept of information quality in Delone and Mclean’s IS success Model (1992) and also considered as an important factor in web personalization (Tam and Ho 2006). A relevant web content enhances the accuracy and speed of information processing of the customers, thereby making the decision-making process more effective. Eventually, a relevant web content increases user acceptance (Tam and Ho 2006) and exerts a positive effect on the flow experience of the user (Jiang et al. 2009). Relevant web content, including product information, expectedly increases the positive expectation of customers regarding the business transaction. It helps customers make 164 decisions faster and more accurately. In turn, customers will perceive the higher level of information quality on the web. From this, we propose the following hypothesis: H4: Content relevance is positively associated with trust in online businesses. 3.4 Antecedes of distrust: Relationship-affecting factors Distrust is formed when it is observed that the partner has bad motivations or does damage on purpose. Distrust can be defined as feelings of hurt, suspiciousness, or fear of a partner’s intentions and future behavior. It is not based on capability or skill of a partner that can be objectively assessed; rather, it is more related to the sincerity and motivation to continue the relationship. Thus, relationshipaffecting factors (e.g., involvement and responsiveness) that show the motivation and intentions of a partner become the foundations of distrust (Goodman et al. 1995). 3.4.1 Customer involvement Customer involvement embraces the number and type of activities that a customer engages in together in the course of their regular economic transactions with organizations (Goodman et al. 1995). It determines the strength and depth of the relationship between the customer and the organization (Goodman et al. 1995). For example, the customers can increase their involvement level by actively participating in the service and product design. Tightening the feedback loop between consumption and product can also enhance customer involvement level (Lundkvist and Yakhlef 2004). The impersonality of online business makes customer involvement more difficult than in offline businesses. However, services such as special contract, active customization or refund flexibility, can increase the level of customer involvement between online sellers and customers. Customer involvement is a relationship-affecting factor that represents the total quality (strength and depth) of the relationship between the customer and the organization. Frequent involvement by the customers strengthens the bond between the customer and the seller or organization. Customers would intuitively feel that they have a good relationship with the sellers if they are fully acknowledged and deeply involved in business operations. These activities affect the customers in their affective dimension, that is, the greater the extent of the customers’ involvement, the greater their sense of control, thereby lessening fear and worry in their transactions. Based on this premise, we derive the following hypothesis. H5: Customer involvement is negatively associated with distrust in online businesses. Inf Syst Front (2015) 17:159–175 3.4.2 Web fraud Web fraud refers to a broad category of crimes conducted by online users through website transactions. Due to the impersonality, anonymity, and information asymmetry between online sellers and customers (Turban et al. 2010), the web environment is vulnerable to a variety of fraudulent acts, such as misrepresentation, failure to ship, and improper handling (Chua et al. 2007). Web fraud is different from a seller’s typographical error or simple information mistake due to confusion; it refers to an intentional misconduct in a web-based business transaction to deceive customers. Auctions are commonly the settings where vast majority of online fraudulent acts are committed (Abbasi et...
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Star Bucks Case Study Outline
1. Building Customer Loyalty
2. Growth Strategy
3. Improving Efficiency through Relationships


Running Head: STAR BUCKS CASE STUDY

Star Bucks Case Study
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STAR BUCKS CASE STUDY

2
Building Customer Loyalty

Building loyalty is not a simple process for any business, and thus Star Bucks will be
required to undertake a rigorous process in ensuring that this happens seriously. Trust and loyalty
are intertwined, and customers tend to be loyal when they are confident of the processes of a
business. The impact of trust and loyalty is that it dramatically reduces the complexities of
business while at the same time creating an avenue to generate high incomes for any business
(Lee et al., 2015). In the case of star bucks, it is equally important for them to devise a
mechanism by which they can build and retain customer loyalty especially by leveraging on the
existing platforms that deliver products to customers (Lee et al., 2015). Business practitioners,
especially in online enterprises, are tasked with the responsibility of ensuring that they are
reliable and can be trusted by their customers (Lee et al., 2015). Loyalty in business settings can
only be achieved if the online presence is available and in the right way. In the case of ...


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