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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.
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.
This paper treats the Piggyback Transportation Problem: A large vehicle moves successive batches of small vehicles from a depot to a single launching point. Here, the small vehicles depart toward assigned customers, supply shipments, and return to the depot. Once the large vehicle has returned and another batch of small vehicles has been loaded at the depot, the process repeats until all customers are serviced. With autonomous driving on the verge of practical application, this general setting occurs whenever small autonomous delivery vehicles with limited operating range, e.g., unmanned aerial vehicles (drones) or delivery robots, need to be brought in the proximity of the customers by a larger vehicle, e.g., a truck. We aim at the most elementary decision problem in this context, which is inspired by Amazon’s novel last-mile concept, the flying warehouse. According to this concept, drones are launched from a flying warehouse and – after their return to an earthbound depot – are resupplied to the flying warehouse by an air shuttle. We formulate the Piggyback Transportation Problem, investigate its computational complexity, and derive suited solution procedures. From a theoretical perspective, we prove different important structural problem properties. From a practical point of view, we explore the impact of the two main cost drivers, the capacity of the large vehicle and the fleet size of small vehicles, on service quality.
This research examines the shift from pay secrecy to transparency and seeks to improve the understanding of previously unrecognized negative consequences on job satisfaction. Drawing on undermet expectations research, we propose that shifting toward pay transparency decreases job satisfaction among employees who encounter negative discrepancies between expected and revealed pay standing (undermet pay standing expectations). Using data from field and experimental studies, we tested our hypotheses that episodic envy mediates the effect of undermet pay standing expectations on job satisfaction and that this indirect effect is moderated by victim sensitivity. Study 1 results suggest that undermet pay standing expectations lead to the predicted decrease in job satisfaction through episodic envy. In Study 2, we surveyed employees of a technology company before and after their shift to pay transparency and found partial support for our hypotheses, suggesting that episodic envy mediates the negative effects of undermet pay standing expectations on job satisfaction only for those low in victim sensitivity. Study 3 supported our overall model by illustrating that low victim sensitivity strengthened the negative indirect effects of undermet pay standing expectations on job satisfaction via episodic envy in an experimental study. We then discuss the implications for theory and practice.
Braverman et al. [Math. Oper. Res. 41(1), (2016), pp. 352–376], introduce the problem Provision-after-Wait which is to find a stable (ey free) assignment of n patients to m hospitals, and their waiting times before admission, such that the social welfare is maximized, subject to a limited budget. Chan et al. [ACM Trans. Econ. Comput. 5(2), (2017), Article 12, pp. 12:1–12:36] focus on a natural case of d-ordered preferences, in which patients are ordered according to the differences of their values between consecutive hospitals. For this case, they provide a sophisticated proof of ordinary NP-hardness, reduce it to the problem called Ordered Knapsack, and develop a fully polynomial time approximation scheme for Ordered Knapsack. We present a simple proof that Ordered Knapsack is NP-hard, which implies NP-hardness of a more restrictive case of the original problem, and present an alternative fully polynomial time approximation scheme with a reduced run time by a quadratic factor of n, for a fixed m. A similar algorithm is developed to find a solution for which the social welfare is as high as for the optimal solution of Ordered Knapsack, and the budget limit can be exceeded by at most 1-ε times. We also present polynomial algorithms for the cases of Ordered Knapsack, in which the number of distinct input parameters is fixed.
Online retailers are increasingly using third-party online marketplaces (e.g., Amazon, Taobao) as an alternative sales channel to their website. While cross-channel sales elasticities have been established for many sales channel combinations (e.g., adding bricks to clicks), we lack an understanding of whether the use of third-party marketplaces grows or cannibalizes a retailer's sales. Practitioners argue that firms can build their e-commerce business through acquiring customers by selling on the marketplace. Indeed, a marketplace could complement a retailer's offering (e.g., acquiring new customer segments), although inventory effects might mitigate this complementarity. Alternatively, cannibalization might occur from losing customers from one's website to the online marketplace. The present research investigates which of the two opposing forces prevails using a time series of category sales data from one of the largest global marketplace sellers. The authors use vector autoregressive modeling to show that marketplace sales increase sales on a retailer's website (0.014% for every 1% in marketplace sales). This effect is strongest for categories with large choice and low product prices. Acquiring customers through the marketplace might be cheaper than through other sources (estimated at 24% of initial sales). However, online retailers should be aware that this strategy strengthens the marketplace and may have potential negative long-term consequences (e.g., through marketplace control of the customer relationship).
In any product search process, consumers encounter multiple products. In today's online retail eironment, such product encounters are increasingly serial, such that consumers review products back-to-back (e.g., through swiping in mobile apps). The present research shows that for the serial evaluation of products, the serial position matters through an interaction of a leadership bias, conceptual fluency and tedium. During a product search, evaluations follow an S-shaped pattern consisting of (1) a primacy phase of declining evaluations (caused by a leadership bias towards the first product), (2) a wear-in phase of increasing evaluations (caused by increasing conceptual fluency after a recognition of the shared attributes of the product category), and (3) a wear-out period of declining evaluations (caused by tedium). Ten empirical iestigations in an e-commerce setting granularly trace these three phases, establishing conceptually related overall boundary conditions (matching vs. searching products) and moderators (conditional vs. unconditional searching, product similarity, conceptual category knowledge, product image complexity) of the S-shaped pattern of serial product evaluations. "Keywords: serial evaluation, leadership effects, fluency, order effects, repeated exposure
We address an optimization problem that arises at seaports where containers are transported between stacking areas and small buffer areas of restricted capacity that are located within the reach of quay cranes. The containers are transported by straddle carriers that have to be routed such that given unloading and loading sequences of the containers at the quay cranes are respected. The objective is to minimize the turnaround times of the vessels. We analyze the problem’s computational complexity, present an integer program, and propose a heuristic framework that is based on decomposing the problem into its routing component and a component that handles the time variables and buffer capacities. The framework is analyzed in computational tests that are based on real-world data. Based on these tests, we analyze the question of whether or not it pays off to deviate from the approach of permanently assigning a fixed number of straddle carriers to each quay crane, which is the strategy that is currently implemented at the port.
The more the merrier?
(2022)
This paper explores how diversity among lead partner teams (LPTs) of private equity (PE) funds affects buyout performance. We argue that there is a trade-off between the ‘bright side’ of diversity (i.e. improved decision-making due to a broader set of perspectives) and the ‘dark side’ (i.e. deteriorated decision-making due to a potential for clashes and a lack of cooperation). Our theoretical framework suggests that the net effect on performance depends on whether LPTs are diverse in socio-demographic or occupational aspects. To test this hypothesis, we develop a comprehensive index that measures LPT diversity along six dimensions. Using a sample of 241 buyouts and 547 involved PE partners, we find that higher scores in the socio-demographic component (gender, age, nationality) are associated with higher deal returns and multiple expansions. The opposite is true for higher scores in the occupational component (professional experience, educational background, university affiliation). Further results suggest that the ‘bright side’ of diversity gets relatively more important in case of complex buyouts and uncertain deal environments.
This paper examines the relationship between Americanization and CEO pay levels in Europe and how this relationship is moderated by CEO power. Based on neo-institutional theory, our study provides empirical support for a link between Americanization and CEO pay levels. Drawing on a sample of large listed European firms, our results suggest that various dimensions of Americanization, i.e., Americanization of the CEO, of the firm and of the industry, can be associated with higher CEO pay. Combining neo-institutional approaches with managerial power perspectives, we show that Americanization can have an even stronger effect on pay when the CEO is powerful.
We derive population dynamics from finite cooperative games with transferable utility, where the players are interpreted as types of individuals. We show that any asymptotically stable population profile is characterized by a coalition: while the types in the coalition have the same positive share, the other types vanish. The average productivity of such a stable coalition must be greater than the average productivity of any proper sub- or supercoalition. In simple monotonic games, this means that exactly the minimal winning coalitions are stable. Possible applications are the analysis of the organizational structure of businesses or the population constitution of eusocial species.
We pinpoint the position of the (symmetric) Shapley value within the class of positively weighted Shapley values to their treatment of symmetric versus mutually dependent players. While symmetric players are equally productive, mutually dependent players are only jointly (hence, equally) productive. In particular, we provide a characterization of the whole class of positively weighted Shapley values that uses two standard properties, efficiency and the null player out property, and a new property called superweak differential marginality. Superweak differential marginality is a relaxation of weak differential marginality (Casajus and Yokote, J Econ Theory 167, 2017, 274-284). It requires two players' payoff for two games to change in the same direction whenever only their joint productivity changes, i.e., their individual productivities stay the same. In contrast, weak differential marginality already requires this when their individual productivities change by the same amount. The Shapley value is the unique positively weighted Shapley value that satisfies weak differential marginality.