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In today’s “new world of work,” knowledge workers are often given considerable flexibility regarding where and when to work (i.e., time-spatial flexibility) and this has become a popular approach to redesigning work. Whilst the adoption of such practices is mainly considered a top-down approach to work design, we argue that successful utilization of time-spatial flexibility requires proactivity on the part of the employee in the form of time-spatial job crafting. Previous research has demonstrated that time-spatial flexibility can have both positive and negative effects on well-being, performance, and work-life balance; yet remains mute about the underlying reasons for this and how employees can handle the given flexibility. Drawing on research from work design, we posit that in order for employees to stay well and productive in this context, they need to engage in time-spatial job crafting (i.e., a context-specific form of job crafting that entails reflection on time and place), which can be considered a future work skill. We propose a theoretical model of time-spatial job crafting in which we discuss its components, shed light on its antecedents, and explain how time-spatial job crafting is related to positive work outcomes through a time/spatial-demands fit.

International audience; We propose a smart warehouse environment where not only inventory items but also the shelves are tracked by an RFID-based system. Both operational activities and warehouse configurations are continually monitored to facilitate real-time response. We study the dynamics of a flexible warehouse scenario where items of any type can be dropped off anywhere within the premises. Unlike existing models, we relax both the location constraint and local (e.g., item-type level) capacity constraints with a periodically renewable fixed global capacity. Dynamic decisions on location and local capacity are made based on the stochastic Markovian demand states. We optimize processing and routing constraints and compare the performance of this flexible storage setup with classical models through multiple levels of real-time decision support. Our results provide corroborating evidence to support the following observations: (1) "free pick-n-drop" combined with fluid warehousing mechanism greatly reduces trip costs and lead time for single trip demand, (2) there exists a lower bound on the performance in such a setup with fixed local capacities, and (3) the lower bound can be further improved when inventory capacity and location are dynamically adjusted according to actual demand patterns.

International audience; This article aims to identify optimal vehicle procurement policies for organizations engaged in humanitarian development programs and to derive general insights on the characteristics of these policies. Toward that end, we follow an inductive approach. First, we study the operations of the International Committee of the Red Cross (ICRC) in three representative countries: Sudan, Afghanistan, and Ethiopia. Using a linear programming (LP) model primed with field data provided by the ICRC, we calculate the optimal vehicle fleet size and compare it with the policies actually implemented. Second, drawing from results of the LP model, we develop a stylized quadratic control model and use it to characterize the general structure of the optimal policy under different demand scenarios and operational constraints. After demonstrating that the results of the control model are consistent with those of the LP model in the specific context analyzed, we discuss the optimal policies and the applicability of the former as a practical tool for strategic asset planning.

Executive committees make critical decisions regarding project selection in different ways. In a committee operating under a democratic governance structure, all members have the right to vote, but the majority of voters ultimately decide which projects to undertake. Alternatively, in a committee operating under an elite governance structure, the decision is made only by a restricted number of voters. Therefore, in an elite governance, there are decision makers or “elite” members, and non-decision makers or “common” members. In this article, we study how committee members under either a democratic or elite governance structure interact and communicate information to each other, and ultimately make a decision about a project with uncertain revenues. We find that the efficient committee governance structure, i.e., the one that maximizes the expected surplus of the committee, can be determined by focusing on one specific communication between elite and common committee members. Further, we establish a sufficient condition on the revenues distribution for each governance structure to be efficient. Finally, when this sufficient condition on the revenues distribution does not hold, we find that governance efficiency depends on the probability of the decision makers to learn the true value of the revenues being sufficiently high or the one of the non-decision makers being sufficiently low or both these two conditions.

Problem definition: We consider the intraday scheduling problem in a group of orthopaedic clinics where the planner schedules appointment times, given a sequence of appointments. We consider patient re-entry—where patients may be required to go for an x-ray examination, returning to the same doctor they have seen—and variability in patient behaviours such as walk-ins, earliness, and no-shows, which leads to inefficiency such as long patient waiting time and physician overtime. Academic/practical relevance: In our data set, 25% of the patients are required to go for x-ray examination. We also found significant variability in patient behaviours. Hence, patient re-entry and variability in behaviours are common, but we found little in the literature that could handle them. Methodology: We formulate the problem as a two-stage optimization problem, where scheduling decisions are made in the first stage. Queue dynamics in the second stage are modeled under a P-Queue paradigm, which minimizes a risk index representing the chance of violating performance targets, such as patient waiting times. The model reduces to a sequence of mixed-integer linear-optimization problems. Results: Our model achieves significant reductions, in comparative studies against a sample average approximation (SAA) model, on patient waiting times, while keeping server overtime constant. Our simulations further characterize the types of uncertainties under which SAA performs poorly. Managerial insights: We present an optimization model that is easy to implement in practice and tractable to compute. Our simulations indicate that not accounting for patient re-entry or variability in patient behaviours will lead to suboptimal policies, especially when they have specific structure that should be considered.

Problem definition : We examine a dynamic assignment problem faced by a large wireless service provider (WSP) that is a Fortune 100 company. This company manages two warranties: (i) a customer warranty that the WSP offers its customers and (ii) an original equipment manufacturer (OEM) warranty that OEMs offer the WSP. The WSP uses devices refurbished by the OEM as replacement devices, and hence their warranty operation is a closed-loop supply chain. Depending on the assignment the WSP uses, the customer and OEM warranties might become misaligned for customer-device pairs, potentially incurring a cost for the WSP. Academic/practical relevance : We identify, model, and analyze a new dynamic assignment problem that emerges in this setting called the warranty matching problem. We introduce a new class of policies, called farsighted policies, which can perform better than myopic policies. We also propose a new heuristic assignment policy, the sampling policy, which leads to a near-optimal assignment. Our model and results are motivated by a real-world problem, and our theory-guided assignment policies can be used in practice; we validate our results using data from our industrial partner. Methodology : We formulate the problem of dynamically assigning devices to customers as a discrete-time stochastic dynamic programming problem. Because this problem suffers from the curse of dimensionality, we propose and analyze a set of reasonable classes of assignment policies. Results : The performance metric that we use for a given assignment policy is the average time that a replacement device under a customer warranty is uncovered by an OEM warranty. We show that our assignment policies reduce the average uncovered time and the expected number of out-of-OEM-warranty returns by more than 75% in comparison with our industrial partner’s current assignment policy. We also provide distribution-free bounds for the performance of a myopic assignment policy and of random assignment, which is a proxy for the WSP’s current policy. Managerial implications : Our results indicate that, in closed-loop supply chains, being completely farsighted might be better than being completely myopic. Also, policies that are effective in balancing short-term and long-term costs can be simple and effective, as illustrated by our sampling policy. We describe how the performance of myopic and farsighted policies depend on the size and length of inventory buildup.

We study price optimization of perishable inventory over multiple, consecutive selling seasons in the presence of demand uncertainty. Each selling season consists of a finite number of discrete time periods, and demand per time period is Bernoulli distributed with price-dependent parameter. The set of feasible prices is finite, and the expected demand corresponding to each price is unknown to the seller, whose objective is to maximize cumulative expected revenue. We propose an algorithm that estimates the unknown parameters in a learning phase, and in each subsequent season applies a policy determined as the solution to a sample dynamic program, which modifies the underlying dynamic program by replacing the unknown parameters by the estimate. Revenue performance is measured by the regret: the expected revenue loss relative to the optimal attainable revenue under full information. For a given number of seasons n, we show that if the number of seasons allocated to learning is asymptotic to $$(n^2\log n)^{1/3}$$ ( n 2 log n ) 1 / 3 , then the regret is of the same order, uniformly over all unknown demand parameters. An extensive numerical study that compares our algorithm to six benchmarks adapted from the literature demonstrates the effectiveness of our approach.

The planning of on-demand services requires the formation of vehicle schedules consisting of service trips and empty trips. This paper presents an algorithm for building vehicle schedules that uses time-dependent demand matrices (= service trips) as input and determines time-dependent empty trip matrices and the number of required vehicles as a result. The presented approach is intended for long-term, strategic transport planning. For this purpose, it provides planners with an estimate of vehicle fleet size and distance travelled by on-demand services. The algorithm can be applied to integer and non-integer demand matrices and is therefore particularly suitable for macroscopic travel demand models. Two case studies illustrate potential applications of the algorithm and feature that on-demand services can be considered in macroscopic travel demand models., Projekt DEAL, Deutsche Forschungsgemeinschaft

Research supporting the Matthew effect demonstrates that high-status actors experience performance benefits due to increased recognition of their work and greater opportunities and resources, but recent research also indicates that high-status actors face a greater risk of negative performance evaluations. In this paper, we seek to contribute to the status literature by reconciling these findings and ask: To what extent does status influence heterogeneity in performance evaluations? We explore how project leader status affects the performance of innovation projects in the video game industry. We hypothesize that there is an inverted U-shaped relationship between project leader status and project performance, and a positive relationship between project leader status and performance extremeness (i.e., performance variation). In order to test our hypotheses, we analysed the performance of video game projects and computed the status of project leaders by applying a project affiliation social network analysis. We find that an intermediate level of status – neither too much nor too little – is positively associated with average project performance. We also reveal more extreme performance effects for high-status leaders: While some achieve superior project performance, others experience significant project failures. We, therefore, provide important theoretical and practical insights regarding how status affects the implementation of innovations. We also discuss the implications of these findings for the literature on middle-status conformity.

We analyse how the introduction of the same renewable energy technology at different parts of the electricity supply chain has different price formation effects on wholesale power markets. We develop a multi-stage competitive equilibrium model to evaluate the effects on short-term price formation of a technology shift from conventional to both large-scale renewable energy production (e.g. wind and solar farms) and distributed renewable energy sources (e.g. rooftop solar). We find that wind and solar technologies oppositely affect the forward risk premium, and this is related to technology-varying, risk-related hedging pressures of producers and retailers. We form a multi-factor propositional framework and empirically validate the model by analyzing data from California and Britain; two markets which recently experienced significant increases of renewable power, in terms of utility scale and distributed sources. The work is innovative in showing theoretically and empirically how different types of renewable technologies influence market price formation differently. This has implications for market participants facing wholesale price risks, as well as regulators and policy-makers.

Microgrids are an increasingly relevant technology for integrating renewable energy sources into electricity systems. Based on a microgrid implementation in California, we investigate microgrid operation under real-world conditions. These conditions have not yet been considered in combination and encompass energy charges, demand charges, export limits, as well as uncertainty about future electricity demand and generation in the microgrid. Under these conditions, we evaluate the performance of two frequently applied groups of strategies for microgrid operation. The first group is composed of proactive strategies that optimize decisions based on forecasts of future electricity generation and demand. The second group includes reactive strategies that make operational decisions based exclusively on the current state of the microgrid. We evaluate the performance of the strategies under varying operational parameters, forecast accuracies, and microgrid configurations—well beyond our Californian showcase. Our results confirm the expectation that proactive strategies outperform reactive ones in the majority of settings. Yet, reactive strategies can perform better under short control intervals or under moderate prediction errors of PV generation or demand. Furthermore, the interplay between real-world conditions and operational strategies reveals several additional insights for research on microgrid operation. First, we find that demand charges and export limits decisively affect microgrid performance. Second, the impact of forecast errors is highly non-linear and non-monotonous. Third, escalating negative interactions between forecast errors and demand charges make proactive strategies benefit from longer control intervals. This result is contrary to existing best practice, which promotes short control intervals to minimize the impact of uncertainty.

International audience; Form postponement means delaying the commitment of inventory to the final configuration of a product as long as possible. Many firms today are striving to redesign their products and/or their manufacturing and supply chain processes to implement form postponement. Opportunities for form postponement, however, are sometimes lost in the companies' production-planning processes. By focusing on the deferring of product mix decisions in the master production scheduling process, this paper shows that form postponement opportunities can be divided into two components: one whose pursuit necessarily requires product and/or transformation process redesign, the other that can be pursued by changing the sales forecasting and master production scheduling process alone. We develop an operational procedure to identify and quantify, for a given product family, all opportunities for form postponement and their two respective components. Then, we discuss and empirically illustrate how the proposed measurement procedure may support companies in changing their decision-making routines to implement form postponement. Finally, we set future research directions on form postponement suggested by our results.

Highlights • We develop a framework to assess public-sector prescriptive analytics initiatives. • Six guiding questions link analytics to value, legitimacy, and operational capacity. • We present an illustrative case study for public charge point placement. • Data-driven recommendations are projected to increase utilization by 15%. • Initiatives benefit from scale, entrepreneurial spirit and collaboration., In this work, we investigate the challenges public-sector organizations face when seeking to leverage prescriptive analytics and provide insights into the public value such data-driven tools and methods can provide. Using the strategic triangle of value, legitimacy, and operational capacity as a starting point, we derive a framework to assess public-sector prescriptive analytics initiatives, along with six guiding questions that structure the assessment process. We present a case study applying prescriptive analytics to the placement of charge points in urban areas, a critical challenge many municipalities are currently facing in the transition towards electric mobility. Reflecting on the analytics application as well as its development and implementation process through the guiding questions, we derive key lessons for public-sector organizations seeking to apply prescriptive analytics.

A key step in data-driven decision making is the choice of a suitable mathematical model. Complex models that give an accurate description of reality may depend on many parameters that are difficult to estimate; in addition, the optimization problem corresponding to such models may be computationally intractable and only approximately solvable. Simple models with only a few unknown parameters may be misspecified, but also easier to estimate and optimize. With such different models and some initial data at hand, a decision maker would want to know which model produces the best decisions. In this paper we propose a decision-based model-selection method that addresses this question.

Shop-floor employees play a key role in manufacturing innovation. In some companies, up to 75% of all productivity gains are the result of bottom-up employee ideas. In this paper, we examine how employee interplant assignments—short problem-solving jobs at other manufacturing plants within the same firm—influence employee-driven manufacturing innovation. Using unique idea-level data from a large European car parts manufacturer, we show that interplant assignments significantly increase the value of employees’ improvement ideas due to the short-term transfer of production knowledge and long-term employee learning. Both effects are amplified by assignments to plants that have high functional overlap (i.e., plants producing similar products using similar processes and machinery). One implication is that, for the purpose of employee-driven manufacturing innovation, assignments between peripheral plants with high functional overlap can be more effective than assignments to and from central plants. These findings are robust to several econometric tests. Our study provides novel and detailed empirical evidence of manufacturing innovation, and goes beyond previous research on the learning curve (learning by doing) by investigating how interplant assignments affect the value of employees’ improvement ideas (learning by moving). This paper was accepted by Charles J. Corbett, operations management.

Many intra-logistics systems, such as automated container terminals, distribution warehouses, and cross-docks, observe parallel process flows, which involve simultaneous (parallel) operations of independent resources while processing a job. When independent resources work simultaneously to process a common job, the effective service requirement of the job is difficult to estimate. For modeling simplicity, researchers tend to assume sequential operations of the resources. In this paper, we propose an efficient modeling approach for parallel process flows using two-phase servers. We develop a closed queuing network model to estimate system performance measures. Existing solution methods can evaluate the performance of closed queuing networks that consist of two-phase servers with exponential service times only. To solve closed queuing networks with general two-phase servers, we propose new solution methods: an approximate mean value analysis and a network aggregation dis-aggregation approach. We derive insights on the accuracy of the solution methods from numerical experiments. Although both solution methods are quite accurate in estimating performance measures, the network aggregation dis-aggregation approach consistently performs best. We illustrate the proposed modeling approach for two intra-logistic systems: a container terminal with automated guided vehicles and a shuttle-based compact storage system. Results show that approximating the simultaneous operations as sequential operations underestimates the container terminal throughput on average by 28% and at maximum up to 47%. Similarly, considering sequential operations of the resources in the compact storage system results in an underestimation of the throughput capacity up to 9%.

In research and practice, public transportation planning is executed in a series of steps, which are often divided into the strategic, the tactical, and the operational planning phase. Timetables are normally designed in the tactical phase, taking into account a given line plan, safety restrictions arising from infrastructural constraints, as well as regularity requirements and bounds on transfer times. In this paper, however, we propose a timetabling approach that is aimed at decision making in the strategic phase of public transportation planning and to determine an outline of a timetable that is good from the passengers’ perspective. Instead of including explicit synchronization constraints between train runs (as most timetabling models do), we include the adaption time (waiting time at the origin station) in the objective function to ensure regular connections between passengers’ origins and destinations. We model the problem as a mixed integer quadratic program and linearise it. Furthermore we propose a heuristic to generate starting solutions. We illustrate the type of solutions found by our approach on two case studies based on the Dutch railway network and analyse trade-offs that are made to balance dwell times and regularity of trains.

This article studies the creation and consolidation of a trademark system tantamount to market integration and commercial specialization of Spanish regions from 1850 to 1920. We analyse the first 47,000 registrations, their geographical distribution and the drivers behind this trademark expansion. By using a lineal probability model, we find that knowledge spillovers across regions are associated with their relative trademark specialization and diversification. We incorporate the role played by transport infrastructure by calculating generalized transport costs. The results clarify the origins and evolution of geographical differences in commercial innovation and regional specialization in the first country to institute modern trademark legislation.

The arrival of a ship at a port is a complex process. It has been reported that ships in port can spend 5–10% of their time in unnecessary anchorage. Predicting the accurate time of ship arrivals could support port actors to optimally allocate their resources and reduce this time, to the benefit of port competitiveness. Currently, the rise of real-time automated ship identification data has given ports, shipping lines and other actors the possibility to predict but also to follow the consecutive steps a ship undertakes while entering a port. Much work has been done on the development of advanced estimated time of arrival (ETA) predictors, but little is known about the quality of ETA information that shipping lines provide to ports. We explore, quantitatively, the predictive power of the sequences of ETAs that ships transmit to the Port of Antwerp while arriving. We propose several forecast evaluation measures to assess the quality of a sequence of predictions and their ability to converge to the actual time of arrival. For the Port of Antwerp, we compute and interpret the measures on a dataset of port calls in the year 2019. Our aim is to compare ETA prediction quality across ships. Understanding variations in prediction quality gives port authorities a basis for discussion with individual shipowners, enabling them to improve their predictions and thus contribute to the competitive performance of the port. As an innovation, we cluster the different berths in our data set with the OPTICS algorithm to gain a better understanding of the effects of the river locks a ship must transit on her way to the allocated berth. Our findings indicate that, on average, shipping lines offer poor and optimistic arrival estimates, which they gradually adjust. The adjustments increase as the ship approaches the port and sometimes they overcompensate earlier predictions. In this, we find little evidence that shipping lines are adopting the advanced arrival prediction methods that have been developed in recent years. Our proposed prediction evaluation measures open the door for a uniform and quantitative approach to assessing the quality of prediction sequences of ships entering ports, to the benefit of a miscellany of port actors.

In this paper, we study the effectiveness of incentives on delivery service time slot choices. In particular, we focus on the use of green labels that specify time slots as environmentally friendly and that intrinsically motivate customers to choose a specific delivery time slot in lieu of price incentives based on extrinsic motivation. We argue that this is important since green labels’ intrinsic nature affects costumer choice in fundamentally different ways than price incentives. We conduct two experiments and two simulation studies to study the effects of using green labels. Our experimental findings suggest that: (i) green labels are an effective tool to steer shoppers toward a certain delivery option, (ii) green labels are more effective for people who are more eco-conscious, (iii) green labels remain effective in the presence of price incentives, while price incentives offer little added value beyond that of just green labels, and (iv) the effectiveness of green labels vs. price discounts remains high when time slots are less appealing (i.e., longer). Our simulation findings suggest that green slots, compared to price incentives or no incentives, offer providers a way to effectively steer consumer time slot choices to yield shorter routes, fewer delivery vehicles used, and more per-customer revenue. We thus conclude that steering individuals to select delivery time slots through intrinsic motivation via green labels may be a promising, no-cost direction for (online) retailers and an important topic for further research.

Research summary: The transaction costs logic suggests that innovators cannot gain value from incumbents' unconditional termination rights in their R&D alliances with them because incumbents use these rights either to protect their own interests or as bargaining chips ex post. By contrast, the real options logic suggests that innovators can gain value from these rights because they provide them with an opportunity to release their resources from their troubled alliances. We reconcile the two logics by showing that incumbents' unconditional termination rights are their exit options in their R&D alliances and they are value-enhancing for innovators; yet, innovators' gains from them depend on the likelihood of their opportunistic uses by incumbents at their exercises. The tests in the biopharmaceutical R&D alliances setting support our hypotheses. Managerial summary: Incumbents form R&D alliances with innovators to explore new technologies and innovators join these alliances to commercialize their technologies. Yet, innovators typically possess alternative commercialization options activated once their alliances are terminated. In such R&D intensive alliances, granting unconditional termination rights to incumbents help innovators gain value from their R&D alliances. This is because incumbents' unconditional termination rights operate as their exit options and they enable easy and timely terminations of R&D alliances. This, in turn, allows innovators not to persist in troubled R&D alliances and to begin with exploring alternative commercialization options. However, the likelihood of opportunism at exit option exercises matters. The innovators' gains from exit options are likely to be higher as partner-specific alliance experience increases because the joint history mitigates opportunism.

Network expansion problems are a special class of multi-period network design problems in which arcs can be opened gradually in different time periods but can never be closed. Motivated by practical applications, we focus on cases where demand between origin-destination pairs expands over a discrete time horizon. Arc opening decisions are taken in every period, and once an arc is opened it can be used throughout the remaining horizon to route several commodities. Our model captures a key timing trade-off: the earlier an arc is opened, the more periods it can be used for, but its fixed cost is higher, since it accounts not only for construction but also for maintenance over the remaining horizon. An overview of practical applications indicates that this trade-off is relevant in various settings. For the capacitated variant, we develop an arc-based Lagrange relaxation, combined with local improvement heuristics. For uncapacitated problems, we develop four Benders decomposition formulations and show how taking advantage of the problem structure leads to enhanced algorithmic performance. We then utilize real-world and artificial networks to generate 1080 instances, with which we conduct a computational study. Our results demonstrate the efficiency of our algorithms. Notably, for uncapacitated problems we are able to solve instances with 2.5 million variables to optimality in less than two hours of computing time. Finally, we provide insights into how instance characteristics influence the multi-period structure of solutions.

Using one field and two online lab experiments, this article shows that congruency between shelf layout and a consumer’s internal product categorization increases the perceived variety of the assortment and reduces the perceived complexity of the shelf layout. These assortment perceptions, in turn, heighten purchase intention and satisfaction toward the chosen item. Results are robust across internal categorization measurements (planogram design vs. sorting tasks), congruency measures (distance- vs. matching-based), and products (biscuits vs. yogurt snacks). In the field study, familiarity—operationalized as either consumption frequency or subjective product knowledge—increased the overall effect of categorization congruency and strengthened its pathway through perceived variety (vs. the one through complexity). The authors show how their research can be exploited to improve shelf layouts by optimizing the external categorization. They demonstrate the value of a unifying Bayesian framework for research on behavioral decision making that uses the same set of posterior parameter draws for parameter inference, moderated mediation analysis, and optimization under uncertainty.

The World Food Programme (WFP) is the largest humanitarian agency fighting hunger worldwide, reaching approximately 90 million people with food assistance across 80 countries each year. To deal with the operational complexities inherent in its mandate, WFP has been developing tools to assist its decision makers with integrating supply chain decisions across departments and functional areas. This paper describes a mixed integer linear programming model that simultaneously optimizes the food basket to be delivered, the sourcing plan, the delivery plan, and the transfer modality of a long-term recovery operation for each month in a predefined time horizon. By connecting traditional supply chain elements to nutritional objectives, we are able to make significant breakthroughs in the operational excellence of WFP’s most complex operations. We show three examples of how the optimization model is used to support operations: (1) to reduce the operational costs in Iraq by 12% without compromising the nutritional value supplied, (2) to manage the scaling-up of the Yemen operation from three to six million beneficiaries, and (3) to identify sourcing strategies during the El Niño drought of 2016.

Online communities have been identified as key platforms for innovation and knowledge sharing. While many studies have consistently identified that social capital and cultural factors are important for online knowledge sharing, their joint effect has to date received less attention. Addressing this gap helps us to move away from a one-size-fits-all approach of managing online communities to one which takes into account that social capital (i.e., trust, reciprocity, and a shared vision) may have differential effects on the sharing of high-quality knowledge. We therefore ask: To what extent does national culture shape the effect of social capital on perceived knowledge quality? We use survey data from two online communities from Germany and the Netherlands and demonstrate that the relationship between social capital and perceived knowledge quality differs in the two different national cultures, in particular for effects of reciprocity and shared vision. Besides practical contributions, we add to the literature by first integrating a social capital and online knowledge sharing lens and highlighting the moderating effect of national culture. Second, we provide a fine-grained understanding of the influence of national culture on knowledge sharing by delving deeper into differences between national cultures often regarded as similar.

Warehouses are an inevitable component in any supply chain and a vividly investigated object of research. Much attention, however, is absorbed by warehousing systems dedicated to the special needs of online retailers in the business-to-consumer segment. Due to the ever increasing sales volumes of e-commerce this focus seems self-evident, but a much larger fraction of retail sales are still realized by traditional brick-and-mortar stores. The special needs of warehouses servicing these stores are focused in this paper. While e-commerce warehouses face low-volume-high-mix picking orders, because private households tend to order just a few pieces per order from a large assortment, distribution centers of retail chains rather have to process high-volume-low-mix orders. We elaborate the basic requirements within both business segments and identify suited warehousing systems for brick-and-mortar stores (e.g., fully-automated case picking). The setup of each identified warehousing system is described, elementary decision problems are discussed, and the existing literature is surveyed. Furthermore, we identify future research needs.

In this study we explore the drivers and consequences of micro-level instances of knowledge sharing for innovation. We do so by focusing on the temporally bounded conversations that colleagues have about new ideas and we study specifically how the strength of ties between these colleagues influences the duration and breadth of knowledge sharing in the idea-related conversations they have over time. A 14-month on-site field study in a multinational company, in which we mapped 496 dyadic relationships regarding 17 new product ideas, shows that knowledge sharing can be explained by the ties between people being either strong or weak, rather than intermediate. We also discover that characteristics of the idea itself shape how tie strength influences the duration and breadth of knowledge sharing in idea conversations. Finally, we provide initial evidence to show how important conversations are for the success of an idea. Our study sheds light on micro-level instances of knowledge sharing for innovation and provides important insights into how managers can foster an environment in which weak and strong ties can be utilised optimally for sharing knowledge about ideas.

Multimodal carriers are third-party logistics providers who utilize multiple modes of transportation to deliver timely door-to-door services to their customers. Notwithstanding frequent service disruptions, customers have ever-increasing expectations for on-time deliveries, posing significant challenges to tactical planning. Inspired by the operations of actual carriers, we study the tactical decision planning of multimodal freight networks, considering operational disruptions, timetabling restrictions, and explicit customer service requirements. Despite our focus on tactical planning, our models integrate long-term commitments by penalizing deviations from strategic goals with last-minute operational planning decisions, which are recourse actions aiming to accommodate service disruptions. After improving the dual bound of our initial formulation, we provide reduced-sized models for the special cases of nonpropagating delays and nonreactive last-minute decisions. This latter formulation is utilized to construct scenario-based and trip-based heuristics, which we combine in a hybrid search loop. Our computational study illustrates that our approach attains high-quality solutions for real-sized instances that are otherwise unsolvable by off-the-shelf optimization software, and a significant improvement over a nontrivial rolling horizon benchmark.

Timetabling for railway services often aims at optimizing travel times for passengers. At the same time, restricting assumptions on passenger behavior and passenger modeling are made. While research has shown that discrete choice models are suitable to estimate the distribution of passengers on routes, this has not been considered in timetabling yet. We investigate how to integrate a passenger distribution into an optimization framework for timetabling and present two mixed integer linear programs for this problem. Both approaches design timetables and simultaneously find a corresponding passenger distribution on available routes. One model uses a linear distribution model to estimate passenger route choices. The other model uses an integrated simulation framework to approximate a passenger distribution according to the logit model, a commonly used route choice model. We compare both new approaches with three state-of-the-art timetabling methods and a heuristic approach on a set of artificial instances and a partial network of Netherlands Railways (NS). Our experiments provide insights into the impact of considering multiple routes instead of a single route, and of integrated route choice versus predetermined route assignment with respect to the solution quality.

The evolution of the technology standard alliance (TSA) is examined using complex adaptive system (CAS) theory. Taking TSA as a dynamic CAS, an echo model is constructed to depict the mechanism of its evolution, and a model is simulated on the NetLogo platform. The echo model includes a basic model, an extended model, and a three-layer echo model. The adhesive aggregation of agents is explained, and the three evolutionary stages of agents’ entry, migration, and exit are analyzed. Moreover, the adaptability of agents in TSA is quantified. The results of simulation show the evolution of the TSA in relation to the two aspects of agent adhesion aggregation and agent resource interaction, and they demonstrate the dynamic and complex hierarchical structure of the TSA system. It is proposed that greater matching ability, moderate behavior income, and lower behavior cost are more conducive to the evolution and development of TSA. Additionally, the echo model is reconstructed to expand the range of application of CAS theory.

The sharing economy has radically reshaped marketing thought and practice, and research has yet to examine whether and how platform-level buyer protection insurance (PPI) affects buyers and sellers in this economy. The authors exploit a natural experiment involving an unexpected system glitch during a PPI launch and estimate difference-in-differences models using over 5.4 million data points from a food sharing platform. Results suggest that PPI significantly increases buyer spending and seller revenue, affirming the benefits of this platform-level insurance in the sharing economy. The authors also uncover multifaceted buyer-side and seller-side responses that enable such benefits. PPI increases buyer spending by boosting product orders and variety-seeking behavior. Furthermore, it enhances seller revenue by increasing customer retention and acquisition. This work contributes to the literature by (1) putting a spotlight on the topic of PPI, a platform governance policy that reduces consumer risks and improves the efficacy of sharing platforms; (2) accounting for how PPI alters buyer and seller behaviors on a platform; (3) addressing what types of buyers and sellers benefit more or less from PPI; and (4) offering guidance for managers to improve platform reputation, marketplace efficiency, and consumer welfare in the context of the sharing economy.

When striving to meet goals, individuals monitor their progress towards achieving them. The discrepancy between their current performance and their goal determines task (dis)satisfaction, and thus whether they will make greater effort. We propose and test a theoretical extension of goal-setting theory, namely that different types of goal standards (minimal or maximal) fundamentally change this monitoring process. Through four experiments we demonstrate that with maximal goals (“ideal” standards), individuals experience greater task satisfaction the nearer their current performance comes to the goal. In contrast, with minimal goals (“at least” standards), their satisfaction level remains low, regardless of how close their performance is to the goal. When goals are exceeded, the reverse applies: with maximal goals, satisfaction remains high regardless of the level of overperformance, while with minimal goals, satisfaction is determined by the level of overperformance. We also demonstrate that task satisfaction levels influence subsequent decisions on goal striving.

Global firms often attempt to utilize modular product architectures to ease the distribution of project work among subsidiaries. However, empirical findings on the effectiveness of this approach are mixed. Seeking to clarify this matter, we investigate the conditions under which modular product platform use is associated with cross‐subsidiary distribution of project work. Through an in‐depth case study of a global corporation, we find that a firm’s ability to leverage modular product platforms for distributing project work is positively associated with project interface match (i.e., the extent to which meeting the requirements of a derivative project does not entail modifications of the interfaces embedded in the firm’s modular product platforms). However, this association is attenuated, and eventually muted, by the project technical manager’s related technical experience (i.e., the manager’s personal experience with the technical solution addressing a focal project’s customer requirements). Such attenuation effect originates because lower levels of project interface match do not necessarily imply greater expected project coordination effort. Such expectation is reduced to the extent that the technical project manager has related technical experience. Triangulation of qualitative insights from four embedded cases with a proprietary database of 97 projects supports our contention. These findings contribute to the literatures on product and organizational modularity, distributed work, and project management.

Scholars generally assume that consumer ratings reflect consumer satisfaction, but ratings can be influenced by the design of the rating system. We examine two rating designs—single-dimensional rating systems, which elicit overall ratings only, and multidimensional (MD) rating systems, which elicit both dimensional and overall ratings—and how they impact overall ratings. Drawing on the accessibility–diagnosticity framework, we argue that dimensional ratings in MD systems influence overall ratings based on how the dimensions have been rated. We support this explanation with seven experiments. Our results suggest that across various experimental settings, rating objects, dimensions, and numbers of dimensions, overall ratings are systematically influenced by the design of the rating system. This paper was accepted by Chris Forman, information systems.

The mechanisms by which social networks and organizational vocabularies combine jointly to affect communication patterns across organizational boundaries remain largely unexplored. In this paper, we examine the mutually constitutive relation between the network ties through which organizational members communicate with each other and the vocabularies that they use to describe their organization. We suggest that the dynamic structure of social networks and organizational vocabularies is contingent on the formal design of organizational subunits. Within subunit boundaries, members who interact with each other are more likely to develop similar vocabularies over time. Interestingly, between subunits, the more two members share similar organizational vocabularies, the more likely they are to form a tie over time. We find empirical evidence for these arguments in a longitudinal study conducted among the managers of a multiunit organization. Organizational vocabularies, we suggest, may sustain communication patterns across organizational boundaries, thus bridging cultural holes within organizations.

__Problem definition:__ We consider a shipper transporting and selling a short-life-cycle product to a destination market. Customers in the destination market obtain higher utility if they receive the product earlier but their time preferences are heterogeneous. Two transportation service providers (i.e., carriers) offer distinct speeds and competing freight rates. This study analyzes the shipper’s optimal shipping strategy under carrier competition. __Academic/Practical relevance:__ Perishable products are commonly shipped via multiple means of transport. The faster the mode of transport is, the more expensive it is, but speed enables the product to reach the market with higher quality. In addition to the trade-off between speed and cost, the competition between carriers can also influence the shipper’s transportation procurement strategies. Our model highlights the implications of carrier competition in a dual sourcing problem. __Methodology:__ We study a two-stage game-theoretical framework: Carriers first compete on freight rates, and then the shipper determines the shipping schedule. __Results:__ The shipper may benefit from product differentiation via dual-mode shipping, in which the shipment that arrives earlier is sold at a premium price. In equilibrium, the shipper’s profit can be U-shaped in the speed difference between carriers. Dual sourcing may be inferior to simply restricting a single shipping service in a winner-take-all fashion. __Managerial implications:__ This study reveals an underlying trade-off between the operational advantage from product differentiation and the cost advantage from carrier competition. To benefit from either of these advantages, a shipper should use two carriers with either very distinct or very similar speeds. Single sourcing may bring an additional cost advantage that outweighs the value of production differentiation through dual sourcing.

Humanity faces ongoing and contemporaneous grand challenges. Occasionally, abrupt shocks escalate a grand challenge’s salience over others. Prior research has advocated forming partnerships to address grand challenges via responsible innovation. Yet, it remains unclear how temporal changes in the salience of a grand challenge impact innovation performances of partnerships. We address this research gap by bridging the literature on issue salience, responsible innovation and interorganizational relationships. We argue that shocks either aid or harm the performance of partnerships for responsible innovation depending on whether their domains are directly or indirectly affected. The Ebola outbreak in 2014 sets the empirical context to test our theory. We find that while the innovation performance of Ebola partnerships formed after the outbreak rose eleven-fold, the performance of partnerships treating Influenza fell by 84.9 percent. Our theory and findings have immediate implications for today’s COVID-19 outbreak, cautioning against salience shifts among concurrent grand challenges.

Drawing from visionary leadership and strategy process research, we theorize and test the mechanism through which middle and lower-level managers’ visionary leadership affects their teams’ strategic commitment. The management literature extols the virtues of visionary leadership. In contrast to this positive stance, we reveal a dark side to visionary leadership. Our theoretical framework suggests that team manager visionary leadership harms team strategic consensus when the manager is not strategically aligned with the CEO, which in turn diminishes team commitment to the strategy. In contrast, when a team manager is strategically aligned with the CEO, team manager visionary leadership is positively related to team strategic consensus and subsequently to team strategic commitment. Data from 136 teams from two organizations support our moderated mediation model. A supplemental analysis of the content of strategic consensus and additional qualitative interviews with managers and employees in one of these organizations provide additional insights concerning the meaning of the theorized relations in practice.

A transition to electric vehicles (EVs) is widely assumed to be an important step along the road to environmental sustainability. However, large-scale adoption of EVs may put electricity grids under critical strain, since peaks in electricity demand are likely to increase radically. Efforts to manage demand peaks through pricing schemes may create new peaks at low-price periods, if large numbers of EV owners use smart charging to benefit from low prices. This effect is expected to be amplified when EV owners adopt smart decision support to assist them with optimal charging decisions. Therefore, energy policymakers are interested in advanced pricing schemes that can smooth demand or induce demand that comes as close as possible to a desired profile. We show, through simulations calibrated with real-world data, that current approaches to electricity pricing are limited in their ability to induce desired demand profiles. To address this challenge, we present adaptive pricing, a method to learn from EV owner reactions to prices and adjust announced prices accordingly. Our method draws on the Green Information Systems principles and can assist grid operators in ensuring the reliable operation of the grid. We evaluate our results in simulations, where we find that adaptive pricing outperforms current electricity pricing schemes, yielding results close to the theoretically optimal ones. We test our method in inducing both flat and extremely volatile demand profiles, and we see that in both cases it manages to induce EV charging close to the ideal scenario under perfect information.

This paper studies how to schedule medical appointments with time-dependent patient no-show behavior and random service times. The problem is motivated by our studies of independent datasets from countries in two continents that unanimously identify a significant time-of-day effect on patient show-up probabilities. We deploy a distributionally robust model, which minimizes the worst-case total expected costs of patient waiting and service provider’s idling and overtime, by optimizing the scheduled arrival times of patients. This model is challenging because evaluating the total cost for a given schedule involves a linear program with uncertainties present in both the objective function and the right-hand side of the constraints. In addition, the ambiguity set considered contains discrete uncertainties and complementary functional relationships among these uncertainties (namely, patient no-shows and service durations). We show that when patient no-shows are exogenous (i.e., time-independent), the problem can be reformulated as a copositive program and then be approximated by semidefinite programs. When patient no-shows are endogenous on time (and hence on the schedule), the problem becomes a bilinear copositive program. We construct a set of dual prices to guide the search for a good schedule and use the technique iteratively to obtain a near-optimal solution. Our computational studies reveal a significant reduction in total expected cost by taking into account the time-of-day variation in patient show-up probabilities as opposed to ignoring it. This paper was accepted by David Simchi-Levi, optimization.

Human resource professionals increasingly enhance their assessment tools with game elements—a process typically referred to as “gamification”—to make them more interesting and engaging for candidates, and they design and use “serious games” that can support skill assessment and development. However, commercial, off-the-shelf video games are not or are only rarely used to screen or test candidates, even though there is increasing evidence that they are indicative of various skills that are professionally valuable. Using the strategy game Civilization, this proof-of-concept study explores if strategy video games are indicative of managerial skills and, if so, of what managerial skills. Under controlled laboratory conditions, we asked forty business students to play the Civilization game and to participate in a series of assessment exercises. We find that students who had high scores in the game had better skills related to problem-solving and organizing and planning than the students who had low scores. In addition, a preliminary analysis of in-game data, including players’ interactions and chat messages, suggests that strategy games such as Civilization may be used for more precise and holistic “stealth assessments,” including personality assessments.

Visual-based social media are growing exponentially and have become an integrated part of the customer engagement strategy of many brands. Prior work points to the textual message content as a driver of customer engagement behavior. So far, little is known about the impact of visual message content, specifically visual emotional and informative appeals. We extract emotional and informative appeals from Instagram posts using machine learning models and use a Negative Binomial model to explain customer engagement. We test our model on 46.9 K Instagram posts from 59 brands in six sectors. Our results show that visual emotional and informative appeals encoded in brand-generated content influence customer engagement in terms of likes and comments. Specifically, we demonstrate that positive high and negative low arousal images drive customer engagement. Informative appeals do not drive customer engagement with the exception of informative brand-related appeals. These findings help brand managers in developing an effective customer engagement strategy on visual social media.

textabstractLearning by doing is a fundamental driver of productivity among knowledge workers. As workers accumulate experience working on certain types of tasks (i.e., they become specialized), they also develop proficiency in executing these tasks. However, previous research suggests that organizations may struggle to leverage the knowledge workers accrue through specialization because specialized workers tend to lose interest and reduce effort during task execution. This study investigates how organizations can improve specialized workers’ performance by mitigating the dysfunctional effects of specialization. In particular, we study how other sources of task experiences from the worker’s immediate manager as well as the organization itself help manage the relationship between worker specialization and performance. We do so by analyzing a proprietary dataset that comprises of 39,162 software service tasks that 310 employees in a Fortune 100 organization executed under the supervision of 92 managers. Results suggest that the manager role experience (i.e., the manager’s experience supervising workers) is instrumental in mitigating the potential negative effect of worker specialization on performance, measured as task execution time. Such influence, however, is contingent on cases in which organizational task experience (i.e., the organization’s experience in executing tasks of the same substantive content as the focal task) is limited. Taken together, our research contributes to multiple streams of research and unearths important insights on how multiple sources of experience beyond the workers themselves can help capture the elusive benefits of worker specialization.

Public transport operators (PTOs) increasingly face a challenging problem in switching from conventional diesel to more sustainable battery electric buses (BEBs). In this study, we optimize the opportunity fast-charging schedule of transit BEB networks in order to minimize the charging costs and the impact on the grid. Two mixed-integer linear programming (MILP) formulations that use different discretization approaches are developed and compared. Discrete-Time Optimization (DTO) resembles a time-expanded network that discretizes the time and decisions to equal discrete slots. Discrete-Event Optimization (DEO) discretizes the time and decisions into nonuniform slots based on arrival and departure events in the network. In addition to the DEO’s higher practicability, the comparative computational study carried out on the transit-bus network in the city of Rotterdam, Netherlands, shows that the DEO is superior to the DTO in terms of computational performance. To show the potential benefits of the optimal schedule, it is compared with two reference common-sense greedy strategies: First-in-First-Served and Lowest-Charge-Highest-Priority.

This paper considers a Cram\'er-Lundberg risk setting, where the components of the underlying model change over time. These components could be thought of as the claim arrival rate, the claim-size distribution, and the premium rate, but we allow the more general setting of the cumulative claim process being modelled as a spectrally positive L\'evy process. We provide an intuitively appealing mechanism to create such parameter uncertainty: at Poisson epochs we resample the model components from a finite number of $d$ settings. It results in a setup that is particularly suited to describe situations in which the risk reserve dynamics are affected by external processes (such as the state of the economy, political developments, weather or climate conditions, and policy regulations). We extend the classical Cram\'er-Lundberg approximation (asymptotically characterizing the all-time ruin probability in a light-tailed setting) to this more general setup. In addition, for the situation that the driving L\'evy processes are sums of Brownian motions and compound Poisson processes, we find an explicit uniform bound on the ruin probability, which can be viewed as an extension of Lundberg's inequality; importantly, here it is not required that the L\'evy processes be spectrally one-sided. In passing we propose an importance-sampling algorithm facilitating efficient estimation, and prove it has bounded relative error. In a series of numerical experiments we assess the accuracy of the asymptotics and bounds, and illustrate that neglecting the resampling can lead to substantial underestimation of the risk.

When a disaster strikes an area, often international assistance is requested to help responding to and recovering from the disaster. The response is often characterized by the dispatch of relief items to the affected regions via depots from Humanitarian Logistics Service Providers (HLSPs). One of the challenges an HLSP faces is how to organize its network of depots. Important questions are how many depots should be opened and what should their locations be? We develop a method that uses historical data to determine depot locations that minimize the cost of transportation and minimize the maximum response time to a disaster. We examine the trade-off between the two objectives using the Pareto front. Furthermore, we use robust optimization to find solutions that are robust against uncertainty in the location and scale of future disasters. We provide a case study of the United Nations Humanitarian Response Depot (UNHRD), a large globally operating HLSP. Among other things, we compute potential cost savings that could be obtained when expanding the network with additional depots. Also, we show the need for using robust optimization when looking for solutions that are robust against the location and scale of future disasters. We generalize the approach by applying it to the independent disaster database EM-DAT, in which we focus on examining the added value of using a robust optimization framework. The first result holds for all Uncapacitated Facility Location Problems (UFLP) with uncertain demand, which are used to find the optimal number of depots. We conclude that incorporating the likely advantage of larger networks having less uncertainty in the expected cost of transportation, enhances the decision-making regarding the optimal number of depots. Even if at some point increasing the number of depots allowed to be opened may not decrease the expected cost of transportation significantly, it is likely to reduce the uncertainty in this expected cost of transportation. For the second result, we consider UFLP used for finding optimal locations of a given number of depots. We conclude that solutions based on nominal and robust optimization may perform similar on individual years that are not included in the optimization, but robust solutions guarantee a smaller cost of transportation for worst case scenarios. This uncertainty reduction is especially valuable when the solution has to be robust against extreme scenarios.

We assess the case of the abrupt discontinuation of the three-in-one policy, a high-occupancy vehicle (HOV) restriction, in Jakarta, with the objective of mapping potential interdependencies in the transportation system. Statistical investigation of the passenger volume in the bus rapid transit (BRT) system in the whole city before and after the policy change revealed a significant increase in the number of passengers during peak hours, especially in the evening period. The extent of the increase, however, depended on whether the area had been subject to the initial policy restriction. The case of sudden discontinuation of the three-in-one policy in Jakarta illustrates how a change in policy aimed at a single transportation mode may spill over to alternative transportation modes. The importance of acknowledging the systemic nature of urban transportation systems when altering policies intended to discourage the use of a single transportation mode within the larger transportation network is discussed.

We contribute to the literature on the assessment of innovation systems by relating the amount of inputs available to the system and its performance through the concept of returns to scale (increasing, constant or decreasing). We study to what extent the size or scale of innovation systems relates to their performance, which is estimated through frontier Data Envelopment Analysis-TOPSIS methods, which overcome several limitations of the standard DEA approach. Using the same data provided by the European Innovation Scoreboard (EIS) for years 2010, 2013 and 2016, our results indicate that countries with a high innovation scale tend to overinvest in innovation inputs. This results into scale inefficiencies stemming from decreasing returns, leading to lower productivity levels. Thanks to DEA-TOPSIS we identify the best and worst performing innovation systems. This provides helpful information by setting suitable reference benchmarks for policy analysis and decision-making. Our results question the current allocation of resources and call for a reconsideration of how innovation policies are designed in many European countries. We conclude that for the EIS to become a useful instrument for the definition of innovation policies, it should consider the nature of returns to scale. This would allow policymakers to identify problems and limitations related to the size of their respective innovation systems, and hence, design holistic innovation policies to act upon them, The authors acknowledge financial support from the Comunidad de Madrid Government, under grant H2019/HUM-5761 (INNJOBMAD-CM). Prof. Jos´e L. Zofío acknowledges financial support from the Spanish Ministry of Science and Innovation and the State Research Agency under grants EIN 2020- 112260/AEI/10.13039/501100011033 and PID2019-105952GB-I00/ AEI/10.13039/501100011033. Assoc. Prof. Jon Mikel Zabala- Iturriagagoitia acknowledges financial support from the Basque Government, Department of Education, Language Policy and Culture, under grant IT 885–16

We study the task of recognizing named datasets in scientific articles as a Named Entity Recognition (NER) problem. Noticing that available annotated datasets were not adequate for our goals, we annotated 6000 sentences extracted from four major AI conferences, with roughly half of them containing one or more named datasets. A distinguishing feature of this set is the many sentences using enumerations, conjunctions and ellipses, resulting in long BI+ tag sequences. On all measures, the SciBERT NER tagger performed best and most robustly. Our baseline rule based tagger performed remarkably well and better than several state-of-the-art methods. The gold standard dataset, with links and offsets from each sentence to the (open access available) articles together with the annotation guidelines and all code used in the experiments, is available on GitHub.

In this study we investigate how consumers in The Netherlands can be persuaded to adopt sustainable practices when purchasing, using and disposing of clothes. This study investigates the attitude-behavior gap for the sustainable choices for purchase, use and disposing of clothes. For each consumption phase we ran a two-step multiple regression. The findings showed that the importance of the factors vary in the three consumption phases. For purchasing and disposal decisions, the core motivator social motivation predicts sustainable practices best, while it has no role in the usage phase. The factor ability appeared to have a significant role in the disposal phase, but not in the other phases. Finally, the trigger appears to lower the consumers’ ability in the purchasing phase, while it enhances the core motivator social evaluation in the disposal phase.