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Retailers’ communication support for their price promotions is shifting from traditional flyers and circulars (so-called feature ads) to conventional media channels, especially digital ads. It is not clear, if and how supporting price promotion with advertising in digital media benefits sales of the promoted product above and beyond the price promotion itself. Further, retail managers require guidance on whether only the promoted product or also their overall business gains from ad support (e.g., from category or cross-period expansions) to negotiate trade promotion support with manufacturers of the promoted products. Using a field experiment with a grocery retailer, we decompose the effects of the advertising support of price discount promotions across digital and print marketing channels. We find that the effectiveness assessment of the advertising channels depends on the beneficiary: while digital channels most effectively support sales of the promoted product (35 % uplift vs. non-promotion period) – especially for popular consumer-pull products (+85 %), traditional print channels improve the performance for the retailer as a whole (+3 % uplift of the total category sales), with a combination of ads having the largest effect (+5 % uplift of the total category sales). This research offers guidance for retail and manufacturer managers tasked with designing price promotions and configuring the ad support across channels, and negotiating trade promotion budgets or manufacturer support for the advertisements.
Many e-commerce retailers are adding “bricks to clicks” - that is, opening an offline channel in addition to their digital sales channel(s). Taking the perspective of such an online pure player, this research assesses the effects of offline channel additions on the financial performance (e.g., sales, profits) and customer behavior (e.g., basket size, return rate) in the extended channel network as well as the initial online channel of the retailer. Across two studies, one at the zip code level and the other at the customer level, we find that the channel addition of a fashion and lifestyle retailer is synergistic in terms of increasing not only overall sales but also profits. At the same time, the new offline channel does not significantly cannibalize the existing online shop, as new customers are attracted through the channel addition. The effects of channel additions, however, are influenced by characteristics of customers gained before the channel addition and of the trade area around the newly opened stores: among existing customers, those who bought more in the online channel do not react as positively to the addition of an offline channel, and trade areas with socioeconomic characteristics that are often viewed as disadvantageous for digital retailing (e.g., an older population, lower average income) show a stronger positive sales effect of a brick-and-mortar addition. The attractiveness of the offline channel for these customer segments highlights that adding bricks to clicks might be most attractive for those customers who were previously unwilling to purchase from an online-only retailer.
Innovation-focused co-creation between companies and individual external contributors is accompanied by the challenge of managing intellectual property (IP). The existing literature presents scattered evidence of various elements of the arrangements adopted by companies to manage their IP (such as a high or low degree of IP control, monetary or non-monetary compensation, non-disclosure agreements, additional agreements and the waiver option) in different co-creation settings (including crowdsourcing contests, virtual communities, single expert sessions and lead user workshops). However, the existing literature exhibits little understanding of how particular IP arrangements influence co-creation project performance in specific settings. Drawing upon contingency theory and configurational theory, we provide a framework that explains both the effectiveness of different IP configurations and the moderating role that co-creation settings may have on the relationship between IP arrangements and project performance. By the means of fuzzy-set Qualitative Comparative Analysis (fsQCA) on a sample of 116 co-creation projects, we determine the impact of various IP arrangements on project performance in different co-creation settings, and we show how this effect differs across those settings. Our study also demonstrates that IP matters for success in co-creation, while highlighting the interdependence of multiple elements of IP arrangements and their joint influence on co-creation project performance. Our study thus fills the gap in the literature where previous research failed to embrace the context-dependent and multidimensional effect of IP arrangements on co-creation project performance. Additionally, this study offers best-practice guidelines for managers for designing IP arrangements to meet the specific characteristics of their co-creation projects and to ensure their success.
This paper addresses the order- and rack-sequencing problem at a single picking station in the context of robotic mobile fulfillment systems, a warehouse technology typically applied in large distribution centers. Following the parts-to-picker concept, items are stored on movable racks that are lifted and transported by automated guided vehicles from the storage area to picking stations for order-processing. The order-picking process involves two linked decisions: How to sequence the processing of orders and how to sequence the rack visits to supply the picking station with the requested items. We present a novel mixed-integer linear programming formulation achieving stronger linear programming bounds than a previous formulation. Including preprocessing techniques it quickly solves instances of medium-size to proven optimality for the first time in literature. For large real-world instances, we provide a three-stage heuristic solution procedure suitable in a dynamic environment, while providing competitive solutions within a short run time. Computational experiments on a broad set of benchmark instances and a comparative study with approaches from literature verify our results.
A single machine scheduling problem with assignable job due dates to minimize total late work has recently been introduced by Mosheiov, Oron, and Shabtay (2021). The problem was proved NP-hard in the ordinary sense, and no solution algorithm was proposed. In this note, we present two pseudo-polynomial dynamic programming algorithms and an FPTAS for this problem. Besides, we introduce a new single machine scheduling problem to minimize maximum late work of jobs with assignable due dates. We develop an O(n log n) time algorithm for it, where is the number of jobs. An optimal solution value of this new problem is a lower bound for the optimal value of the total late work minimization problem, and it is used in the FPTAS.
Constraint programming solvers are known to perform remarkably well for most scheduling problems. However, when comparing the performance of different available solvers, there is usually no clear winner over all relevant problem instances. This gives rise to the question of how to select a promising solver when knowing the concrete instance to be solved. In this article, we aim to provide first insights into this question for the flexible job shop scheduling problem. We investigate relative performance differences among five constraint programming solvers on problem instances taken from the literature as well as randomly generated problem instances. These solvers include commercial and non-commercial software and represent the state-of-the-art as identified in the relevant literature. We find that two solvers, the IBM ILOG CPLEX CP Optimizer and Google’s OR-Tools, outperform alternative solvers. These two solvers show complementary strengths regarding their ability to determine provably optimal solutions within practically reasonable time limits and their ability to quickly determine high quality feasible solutions across different test instances. Hence, we leverage the resulting performance complementarity by proposing algorithm selection approaches that predict the best solver for a given problem instance based on instance features or parameters. The approaches are based on two machine learning techniques, decision trees and deep neural networks, in various variants. In a computational study, we analyze the performance of the resulting algorithm selection models and show that our approaches outperform the use of a single solver and should thus be considered as a relevant tool by decision makers in practice.
We investigate the pricing and value creation in private equity-backed buy-and-build (B&B) strategies using a sample of 3399 buyouts between 1997 and 2020 as well as proprietary performance data. We find that private equity firms pay sizable premiums for B&B platforms. The transaction multiples are similar to those paid by strategic acquirers for matched targets. Despite paying high premiums, private equity firms generate above-average equity returns in B&B strategies. This is because of both higher top-line growth and multiple expansion. To back up our empirical results and shed light on decision-making in B&B strategies, we present evidence from the field. Survey results from 32 interviews with private equity managers provide novel insights into B&B rationale, valuation practices, pricing, value creation, acquisition processes and execution.
Organizational purpose has recently gained great popularity in research and practice. However, the development of this nascent research field has struggled with definitional ambiguity, the lack of a measurement instrument and little empirical testing of potential outcomes. In our paper, we first introduce and define the multidimensional construct of perceived organizational purpose, which sheds light on the individual and subjective experiences of organizational purpose. Second, building on our construct definition, we develop and validate a four-dimensional Perceived Organizational Purpose Scale. Third, we disentangle the related yet differentiated concepts of perceived organizational purpose and meaningful work and theorize how substantial knowledge in the field of meaningful work can be transferred to the relatively new and untested field of perceived organizational purpose. Fourth, we critically elaborate and empirically test the relationship of perceived organizational purpose with employee job satisfaction, subjective wellbeing and work-life conflict.
Evaluating investments in international (and particularly in emerging) markets often leads to confusion and controversy among academics and practitioners. Various theories propose competing models, whereas practitioners build their own alternatives. Our study provides an assessment of the most widely used methods of assessing country risk and shows that practitioners should carefully choose their country risk model. Current models produce a wide range of cost of equity estimates that can considerably affect management decisions.
One of the most known results in the machine scheduling is Lawler’s algorithm to minimize the maximum cost of jobs processed by a single machine subject to precedence constraints. We consider an uncertain version of the same min-max cost scheduling problem. The cost function of each job depends on the job completion time and on an additional generalized numerical parameter, which may be a tuple of parameters. For each job, both, its processing time and the additional parameter are uncertain, only intervals of possible values of these parameters are known. We analyse certain classes of cost functions and develop polynomial algorithms which construct min-max regret solutions. The considered problems cover the most general range of studied cases of interval uncertainty. In the only two papers that present algorithms for minimizing the maximum regret for the problem with uncertain job processing times, the algorithms are based on extremal scenarios, where some uncertain parameters take their maximum values, while all others take their minimum possible values. We show that it is impossible to always limit the search to extremal scenarios. Our approach is based on new ideas different from those underlying previous work. Finally, we show that our approach outperforms all known results for constructing min-max regret solutions for the min-max cost scheduling problem under uncertainty of job processing times.