Showing papers in "IEEE Transactions on Engineering Management in 2017"

A Systematic Literature Review of Constraint-Based Innovations: State of the Art and Future Perspectives

Abstract: The past two decades have seen a tremendous growth in innovation processes conceived under scarcity conditions with special focus on emerging markets and bottom of the pyramid (BOP) customers. However, evolving literature in this field has unfortunately resulted in a multitude of innovation approaches leading to terminology confusion and fragmented literature. Hence, this study is an attempt to systematically organize and synthesize the research on innovation approaches originated in, for or from emerging markets. An extensive systematic review of the existing literature is carried out to investigate the progress of prior research, and to use the insights to define future research pathways. This review is primarily based on the most frequently used innovation approaches, especially frugal innovation, jugaad, disruptive innovation, Gandhian innovation, catalytic innovation, indigenous innovation, bricolage, blowback innovation, trickle-up innovation, resource-constrained innovation, and BOP innovation. Our analysis finds growing standardization in terminology usage and increasing emphasis on “bottom-up” and structured innovation approaches. De-emphasizing the role of technology transfers and spillovers from the West, the findings exhibit increasing applications of these innovations beyond emerging markets to wider markets. Our research results also shed light on the evolution of the topic and instigate further research explorations in the direction of analyzing the user adoption of these constraint-based innovations and understanding the influence of new technological advancements, such as the Internet, mobile telecommunications, and Web 2.0 on the innovation process, with a special focus on the service industry.

Bot-In-Time Delivery for Robotic Mobile Fulfillment Systems

Abstract: This paper studies a robotic mobile fulfillment system (RMFS) featured by robots transporting movables shelves to order pickers. The RMFS can increase productivity, reduce costs, increase order picking accuracy, and improve operational flexibility. We build queue network models to describe the RMFS with two protocols in sharing robots for pickers, propose the corresponding algorithms, conduct numerical analyses, and evaluate the performance of the RMFS by calculating the throughput time. We then calculate the optimal number and the velocity of robots, and provide the effective design rules for the RMFS.

Multiobjective Discrete Artificial Bee Colony Algorithm for Multiobjective Permutation Flow Shop Scheduling Problem With Sequence Dependent Setup Times

Abstract: The multiobjective permutation flow shop scheduling problem with sequence dependent setup times has been an object of investigations for decades. This widely studied problem from the scheduling theory links the sophisticated solution algorithms with the moderate real world applications. This paper presents a novel multiobjective discrete artificial bee colony algorithm based decomposition, called MODABC/D , to solve the sequence dependent setup times multiobjective permutation flowshop scheduling problem with the objective to minimize makespan and total flowtime. First, in order to make the standard artificial bee colony algorithm to solve the scheduling problem, a discrete artificial bee colony algorithm is proposed to solve the problem based on the perturbation operation. Then, a problem-specific solution builder heuristic is used to initialize the population to enhance the quality of the initial solution. Finally, a further local search method are comprised of a single local search procedures based on the insertion neighborhood structures to find the better solution for the nonimproved individual. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the multiobjective discrete artificial bee colony algorithm-based decomposition is compared against the state of art algorithms from the existing literature in terms of both coverage value and hypervolume indicator.

People, Technologies, and Organizations Interactions in a Social Commerce Era

Abstract: Social commerce, a powerful combination of customer-oriented social computing technologies and new commercial features, is having an increasing impact on e-commerce, potentially generating substantial economic benefits. Drawing on socio-technical theory, this study establishes a research framework to help understand the social and technical factors affecting consumers’ intention to purchase on social commerce sites. Our results demonstrate that familiarity, user experience, learning and training, and social commerce constructs all have a positive effect on consumers’ perceptions of ease of use and usefulness, thereby enhancing their trust and intention to purchase. For systems designers and engineers, our results highlight the importance of social commerce features for building consumers’ trust of social commerce sites and supporting their intention to purchase.

On the Robust and Stable Flowshop Scheduling Under Stochastic and Dynamic Disruptions

Abstract: In this paper, we consider a permutation flowshop scheduling problem with the total flow time as the schedule performance measure. A proactive–reactive approach is designed to simultaneously deal with stochastic disruptions (e.g., machine breakdowns) and dynamic events (e.g., newly arriving jobs and delay in job availability). In the proactive stage, the stochastic machine breakdown is hedged against the construction of a robust and stable baseline schedule. This schedule is either optimized by incorporating uncertainty into two surrogate measures or obtained by simulation. Robustness is measured by the expected schedule performance, while stability is measured by the aggregation of dissatisfactions of manager, shopfloor operator, and customers using the prospect theory. In the reactive stage, we assume that the stochastic and dynamic disruptions concurrently occur. Unlike the simple right-shifting method, a more effective rescheduling approach is proposed to balance the realized schedule performance with stability. A common issue in these two stages is the conflict between objectives. Thus, we propose a hybridization strategy that successfully enhances the classic Non-dominated Sorting Genetic Algorithm (NSGA-II and the hybridized algorithm outperforms NSGA-II, multiobjective evolutionary algorithm based on decomposition, and multiobjective memetic algorithms designed for deterministic scheduling problems. Finally, extensive computational studies on the Taillard flowshop benchmark instances are conducted to illustrate the effectiveness of the proposed proactive–reactive approach and the algorithm hybridization strategy.

Multicriteria Green Supplier Segmentation

Abstract: Supplier segmentation is an important strategic activity for companies. The main purpose of segmenting suppliers is to more easily manage a large number of suppliers by formulating relationship management strategies for subsets of suppliers, which is more efficient than separate unique strategies for each supplier. Existing supplier segmentation approaches have paid limited attention to environmentally related criteria. Given the increased importance of sustainable and green supply chains, this points a large gap in the literature. Thus, a green supplier segmentation model is proposed in this study. A supplier potential matrix is used to evaluate suppliers with respect to two dimensions, capabilities and willingness, with respect to environmental issues. Given the multicriteria nature of this problem, a novel hybrid multicriteria methodology is used to evaluate the problem. Rough set theory is used to calculate the weight of each criterion for suppliers’ capabilities and suppliers’ willingness. VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is then used to determine an overall score for each supplier. Finally, fuzzy C-means is used to segment the suppliers while considering the overall score for each supplier. An application of the proposed model for suppliers of a large chemical company is used to evaluate the feasibility of this technique.

Dynamic Capability and Firm Performance: The Role of Marketing Capability and Operations Capability

Abstract: Scholars have argued that dynamic capability can influence firm performance through a variety of means and mechanisms. However, the empirical test of the relationship between dynamic capability, marketing capability, and operations capability on firm performance has remained scant. We contribute to resolving this issue by proposing a research model that links dynamic capability with marketing and operations capabilities on new product development performance specifically and firm performance generally. First, the model suggests that marketing capability and operations capability act as mediating mechanisms that transmit the positive influences of dynamic capability to new product development performance specifically and firm performance generally. Second, the model proposes the relationship between marketing capability, operations capability, and firm performance (new product development performance) be stronger if firms have adequate dynamic capability. Drawing on a dataset of multiyear longitudinal survey data of Chinese high-tech firms, we find support for the proposed model. The findings help us better explain the different ways in which dynamic capability affect performance.

Novel Two-Phase Approach for Process Optimization of Customer Collaborative Design Based on Fuzzy-QFD and DSM

Abstract: In response to fast-growing and rapidly changing global markets, launching new products faster than competitors does not only assist enterprises in acquiring a larger market share, but also in reducing development lead time. However, owing to the intrinsically uncertain properties of new product development management, manufacturing companies often struggle with the dilemma of whether to increase product variety or control manufacturing complexity. This paper proposes a novel two-phase method to assist an enterprise in achieving a customer collaborative product design. In the first phase, quality function deployment, which is based on fuzzy multicriteria decision making and suppliers’ budget constraints, is presented to maximize customers’ satisfaction. In the second phase, an effective approach is proposed to determine the appropriate sequence of several coupled activities with the minimum total feedback time in a design structure matrix. Finally, a real case is used to illustrate the overall applicability of the approach. The optimization results show the effectiveness and superiority of the proposed method over other reported methods in the literature.

The Bullwhip Effect in an Online Retail Supply Chain: A Perspective of Price-Sensitive Demand Based on the Price Discount in E-Commerce

Abstract: This paper investigates the difference in bullwhip effects in online and offline retail supply chains, offering insights into how frequent price discounts in e-commerce influence the bullwhip effect in the online retail supply chain. We consider a two-level online retail supply chain with a manufacturer and an online retailer in which the demand faced by the retailer is price sensitive and based on the price discount. Assuming that the online retailer employs an optimal order-up-to inventory policy with an optimal minimum mean-squared error forecasting technique, we derive the expression of the bullwhip effect in the online retail supply chain and make analysis and comparison. Finally, we develop a dual-channel supply chain model to directly observe the impact of price discounts in e-commerce on the bullwhip effect. The results suggest that price discounts in the online retail market generally amplify the bullwhip effect in the online retail supply chain, but in certain conditions, the bullwhip in the online supply chain may be smaller than that in the offline supply chain. We also find that the relationship between the lead time and the bullwhip effect in the online supply chain presents a distinctive feature contrary to the conclusions of previous studies. Based on the analysis, we develop important managerial insights regarding online retail supply chains.

Alignment Between Internal and External IT Governance and Its Effects on Distinctive Firm Performance: An Extended Resource-Based View

Abstract: Only a few firms possess all of the information technology (IT) resources required to compete effectively in today's dynamic business environment. Consequently, firms face critical challenges in developing their IT governance by integrating, building, and reconfiguring IT resources available internally and externally to achieve a competitive advantage. However, prior studies have mostly examined IT governance only from an internal (e.g., IT organization design) or external (e.g., IT outsourcing) perspective. Therefore, how the internal and external IT governance of firms simultaneously lead to firm performance remains unclear. To address this research gap, we conceptualize internal and external IT governance from the extended resource-based view, propose three alignments between both types of governance, and hypothesize their effects on distinctive firm performance. We then empirically test these hypotheses using 213 Korean firms that have implemented IT outsourcing. Results show that a hierarchy-based alignment (i.e., between internal and external hierarchy governance) optimizes the operational efficiency of firms, a market-based alignment (i.e., between internal and external market governance) offers the greatest advantage in terms of market growth, and a network-based alignment (i.e., between internal and external network governance) leads to the best innovation performance. Furthermore, our post hoc test also reveals some unexpected alignments (e.g., between internal market and external hierarchy governance) that positively influence firm performance, which call for new areas of discussion with alternative theoretical perspectives. This study guides practitioners in choosing outsourcing alignments by comprehensively analyzing the effects of various alignments on distinctive outcomes.

Economic, Environmental, and Social Impact of Remanufacturing in a Competitive Setting

Abstract: This paper studies the environmental and social trade-offs of remanufacturing for product+service firms under competition. We use an analytical model and a behavioral study that together incorporate demand cannibalization from multiple customer segments across the competing firms’ product lines. We measure firms’ profits, consumer surpluses, environmental impacts, and environmental costs along the products lifecycles in the resultant equilibria with and without remanufacturing. We show that competition intensifies the tension between increased profit and worsened environmental impact from market expansions caused by remanufacturing identified by prior research in the case of monopoly. However, bringing in the social dimension leads to an overall positive assessment: remanufacturing creates additional consumer surplus, which compensates for the cost of the environmental impact. In other words, we found strong support that remanufacturing is beneficial for the society.

How Do Informal Ties Drive Open Innovation? The Contingency Role of Market Dynamism

Abstract: Scholars and practitioners have recently shifted their attention from traditional closed internal innovation to open innovation (OI). Building on both the resource-based view and the network perspective, we explore the roles of three types of informal ties (i.e., business, government, and university) in driving inbound and outbound OI, and further examine such effects contingent on market dynamism. Empirical results from our study of 260 Chinese firms show that all three types of informal ties positively affect inbound innovation openness, whereas only business ties facilitate outbound innovation openness. In addition, market dynamism strengthens the relationship between university ties and inbound innovation openness, but weakens the effect of business ties on inbound innovation openness. These findings indicate the salience of informal ties in increasing innovation openness and the contingent role of external market conditions. The findings contribute to the understanding of the drivers of innovation openness, and help clarify the differences between inbound and outbound OI.

Crossing the Valley of Death—An Integrated Framework and a Value Chain for Emerging Technologies

Abstract: The paper develops an integrated technology-push and market-pull framework, a value chain model for crossing the valley of death (VOD—the gap between laboratory and market) for emerging technologies based on primary and secondary data analyses, and a survey conducted on European research and development projects. The study uses a case of micro- and nano-manufacturing technology (MNT), and confirms the existence of the VOD through the survey data analysis. A mixed-methods approach was adopted that investigated the business and technical challenges to the commercialization of MNT. A notable finding is that the emerging MNT often does not have a direct link with market demand, and the result suggests that an intermediary role between advanced technology and market demand should be integrated to act as a co-ordinator for overcoming the VOD. The paper also examines how an intermediary is crucial to escape the VOD within the value chain of the interdependent relationships between actors.

Supply Chain Security: A Classification of Practices and an Empirical Study of Differential Effects and Complementarity

Abstract: Supply chain security (SCS) breaches (a form of supply chain risk) are distressing supply chains and they have the potential to engender acute pain on the society at large. To counteract such breaches, international bodies, nations, societies, industries, and firms have instituted several countermeasures in the form of standards and respective practices. Given that not all incidences/breaches can be averted, the risk management literature advocates that firms should adopt practices that can thwart incidences/breaches and practices that can provide a swift response once an incident/breach is detected in order to contain damages, ease the pain, and restore operations. Resting on the risk management literature and interactions with professionals, we classify SCS management practices into four categories based on their intent (i.e., detection, prevention, response, and mitigation) and operationalize each via multiple indicators. We then test the relative efficacy of these practices to explain SCS performance using responses from 462 firms operating in the United States and Italy.

In-Store Pickup and Returns for a Dual Channel Retailer

Abstract: The Internet and technology have changed how products are sold and delivered to consumers. Today, the most significant growth in online retailing comes from multichannel retailers that sell products both in stores and over the Internet. Recently, these retail/e-tail organizations have attempted to leverage their “brick” locations by allowing customers to pick up or return orders purchased online at retail store locations. Such options let online customers avoid both long carrier lead times and high shipping costs. However, these options come at a cost to the retailer. This paper develops a mathematical model for analytically examining the cost and value of providing in-store pickup and return options in multi-echelon retail/e-tail organizations. In this light, the model determines the optimal subset of a retailer/e-tailer's stores that should be set up to handle in-store pickups and online returns under stochastic channel demands. Computational results show that optimizing the set of pickup and return locations can reduce system cost by up to 20% on average over arbitrarily enabling all stores with Internet pickup/return capabilities, and firms can substantially increase customer value while maintaining cost minimization as an important selection criterion in choosing pickup and return locations.

Information Seeking in Online Healthcare Communities: The Dual Influence From Social Self and Personal Self

Abstract: The use of online healthcare communities to acquire health-related information and reduce uncertainty over illnesses is currently hampered by the lack of understanding of how health information-seeking behavior can be stimulated in such environments. By drawing upon the theoretical notion of social self and personal self, and conducting a field survey among 101 online healthcare community users, this study investigates how social identity in online healthcare communities and individual users’ perceived disease severity jointly influence the health information-seeking propensity. This study contributes to the literature on health information seeking by investigating the influence of social self (social identity), personal self (perceived disease severity), and their interplay in online communities. The findings can guide healthcare providers and community managers in formulating strategic plans for promoting health information-seeking behavior.

A New Methodology for Mining Frequent Itemsets on Temporal Data

Abstract: Temporal data contain time-stamping information that affects the results of data mining. Traditional techniques for finding frequent itemsets assume that datasets are static and the induced rules are relevant across the entire dataset. However, this is not the case when data is temporal. In this paper, we are trying to improve the efficiency of mining frequent itemsets on temporal data. Since patterns can hold in either all or some of the intervals, we propose a new algorithm to restrict time intervals, which is called frequent itemset mining with time cubes. Our focus is developing an efficient algorithm for this mining problem by extending the well-known a priori algorithm. The notion of time cubes is proposed to handle time hierarchies. This is the way by which the patterns that happen periodically, during a time interval or both, are recognized. A new density threshold is also proposed to solve the overestimating problem of time periods and also make sure that discovered patterns are valid. We evaluate our algorithms via experiments.

Equilibrium Analysis of Markup Pricing Strategies Under Power Imbalance and Supply Chain Competition

Abstract: This study uses a game-theory-based analytical model to examine the consequences of two markup pricing schemes, fixed-dollar markup and percentage markup, in a supply chain-to-chain competition setting with power imbalance between dominant retailers. Our results show that the equilibrium pricing strategy depends on the level of supply chain-to-chain competition. Specifically, if the level of supply chain competition is not sufficiently high, the equilibrium pricing strategy for the two retailers will be the one in which both retailers choose to adopt the percentage markup pricing policy, i.e., the [PP] strategy. Unfortunately, this equilibrium will lead the retailers into the prisoner's dilemma in some common situations. However, if the level of competition is extremely high, the equilibrium pricing strategy will be that where the leader (Retailer 1) chooses the percentage markup pricing policy and the follower (Retailer 2) selects the fixed-dollar markup pricing policy, i.e., the [PF] strategy. This equilibrium pricing strategy always hurts Manufacturer 1, but might benefit Manufacturer 2 when the level of competition exceeds a certain threshold. From the perspective of the end-consumers, they prefer the level of competition to be kept below a certain threshold such that the [PP] strategy becomes the equilibrium pricing strategy because this equilibrium leads to the lowest retail prices. Furthermore, our results show that Retailer 1 suffers a first-mover disadvantage even under a linear demand curve, and this situation cannot be alleviated by abandoning its price-leader position to share power equally with Retailer 2.

Improving Quality of Care Through Integration in a Hospital Setting: A Human Systems Integration Approach

Abstract: While many studies suggest that integration is positively associated with improved quality of care, others assert that this may not be so. The inconsistent success of integration to improve performance is not limited to healthcare operations, but is prevalent in operations and engineering management in general. We suggest that this inconsistency exists because many integration studies examine technical components of integration, but not human components of integration. We use recent works on the theory of human systems integration to explain how the technical components of a system, examined through formal integrative practices and informal integrative practices, and the human components of a system, examined through belief in integration and understanding of integration, interact to influence quality of care. In a study of 34 hospital departments, we found strong support for the interaction between the technical and human components, such that formal integrative practices are associated with higher quality of care when understanding of integration is high rather than low as we theoretically suggest. Unexpectedly, our results also suggest that not all integration practices influence quality of care; we discuss the implications of these findings for practice and future research applications.

A New Optimization Model for Reliable Team Formation Problem Considering Experts’ Collaboration Network

Abstract: The objective of this study is to propose a new optimization model for the formation of a reliable team of experts, who have a certain number of skills and best collaboration with each other. The proposed mathematical model maximized team reliability by considering the probability of unreliable experts that may leave the team with the probability (1-Q) and proposed a backup for each unreliable member. In this paper, a new model was developed for simultaneously forming a team with three key factors: 1) expert's skills; 2) expert's collaboration network; and 3) expert's reliability. This model was evaluated by two numerical studies on both artificial and real-life datasets with small and big data. The optimum combination of team members with regard to skills, collaboration network, and reliability will help managers in performing their projects or operations. Therefore, the new optimization model, called the reliable team formation problem, proposed team members in two sets consisting of main and backup members.

A Method for Improving Overlapping of Testing and Design

Abstract: Testing is a critical activity in product development. The academic literature provides limited insight about overlapping between upstream testing and downstream design tasks, especially in considering the qualitative differences between activities that are overlapped. In general, the existing literature treats two overlapped sequential activities as similar, and suggests optimal overlapping policies, techniques, and time–cost assessment. However, this case study-based research identifies that the overlapping of upstream testing with downstream design activities has different characteristics than the overlapping of two design activities. This paper first analyzes the characteristics that affect the overlapping of upstream testing and downstream design activities, and then proposes a method to reduce the time of rework in cases where the upstream testing is overlapped with subsequent redesign phases.

Optimization for the Integrated Operations in an Uncertain Construction Supply Chain

Abstract: Large construction and infrastructure projects are a billion-dollar business, but few studies have addressed the integrated operations in this unique domain of the construction supply chain (CSC). The comparison between the CSC and a conventional supply chain enables us to examine its framework and establish a quantitative optimization model for the CSC. To introduce the integrated operations concept into the CSC, many uncertainties need to be first dealt with, for which a multiobjective uncertain optimization model is developed. As the optimization of the owner and fabricator's costs and the service level are the main objectives, a hybrid genetic algorithm with fuzzy-random method is developed to solve the optimization model. An integrated multiobjective purchasing and production planning model is then constructed and applied to a hydropower construction project in Southwest China. The results illustrate that efficient integrated operations are critical for the CSC performance. The optimization results also indicate that considering of uncertain rush orders and delay times can be vital for optimum CSC performance. With this proposed method, construction managers can quickly respond to changing uncertain demand. This paper has highlighted that project managers need to collaborate with other stakeholders to ensure optimal CSC performance.

Measuring Modularity: Engineering and Management Effects of Different Approaches

Abstract: While a variety of “product” modularity measures have been proposed and empirically used, little comparative research has been conducted on the characteristics and efficacy of such measures. This study explores how the use of diverse modularity measures affects the analysis of the “mirroring” hypothesis. Particularly, this study analyzes the relationship between “product” modularity measures and the degree of: 1) buyer–supplier integration in new product development; 2) supply chain configuration. The empirical results suggest which of the analyzed measures of modularity are preferable, question the overall utility of such measures to really understand the organizational implications of complex technological systems, and point to alternative measurement approaches.

Affinely Adjustable Robust Model for Multiperiod Production Planning Under Uncertainty

Abstract: Demand forecasting is an important factor in production planning, but future demand is not easy to forecast in practice. We consider a multiperiod, multiproduct production planning problem under demand uncertainty with constrains for raw materials, manufacturing capacity, and inventory. Under the assumption that probability distribution of demand is not available, two types of robust optimization models are proposed. First, a robust counterpart is developed to determine the here-and-now decision. Next, an affinely adjustable robust counterpart is developed to determine the wait-and-see decisions by approximating a robust solution with a linear decision rule. The robust models find an optimal solution that is always feasible and less sensitive against all realized demand within a given uncertainty set, in order to minimize production, procurement, inventory, and lost sales costs even in the worst case. Numerical studies demonstrated that, without knowing probability distribution of future demand, the affinely adjustable robust counterpart approach could outperform the robust counterpart and deterministic model in terms of the average cost, the standard deviation of the realized cost, and the worst-case scenario cost. The proposed method is much better than the others, especially when penalty cost due to lost sales is high and unknown demand is left skewed.

Green Supply Chain Formation Through By-Product Synergies

Abstract: Many firms across the world are discovering and benefiting from the ability to identify, recover, and reuse industrial by-products from other firms in traditionally unrelated industries. We examine how the formation of a by-product synergy between two firms, in different industries, and its environmental impact, are influenced by factors such as the by-product trading price, the fixed costs of synergy formation (e.g., innovation cost), and the distinct characteristics of the two markets in which potential partners operate. We show that an incentive compatible region, which ensures a profit increase for both firms, can be characterized by an interior region of the by-product trading price, and the incentive compatible region may enlarge or shrink with the firms share of the fixed cost. Second, we find that when the firms are willing to share the synergy formation cost, higher volatility of either market could better incentivize the formation of the by-product synergy. Third, we find conditions when there exists a set of prices that are both incentive compatible and environmentally efficient.

Information Sharing in the Supply Chains of Products With Seasonal Demand

Abstract: This study considers a two-echelon supply chain with one supplier and one retailer for products with seasonal demand. The effects that information sharing has on coordination and benefits of the supply chain are investigated. In order to better forecast the seasonal demand, the supplier initiates the information sharing process by offering incentives to the retailer which are proportional to the degree of information sharing. Since the variance in the supplier's inventory would marginally decrease as the degree of information sharing increases, the benefits gained by the supplier due to information sharing are thus a convex function. This can be used to obtain the optimal degree of information sharing with the aim of maximizing profits, which is a tradeoff between the benefits gained and costs incurred by information sharing. In this study, the seasonal demand is described by a SARMA time series model. Bayesian analysis is employed to investigate the value of information and determine the optimal degree of information sharing. Constructive properties are derived to provide managerial insight for effective decision making. The results of sensitivity analyses show that the correlations of demand for successive periods and estimation errors would both have great effects on the benefits gained by information sharing.

Technological Uncertainty and Firm Boundaries: The Moderating Effect of Knowledge Modularity

Abstract: One fundamental challenge of technology and innovation management is for firms to decide which future technologies to develop in-house versus buying them outside. This challenge is particularly pertinent when industries emerge, typically a time of high levels of technological uncertainty. It has been long understood that technological uncertainty functions as important stimulus for firms to manage their boundaries. However, two to some extent competing strategy perspectives—governance and competence—predict that firms faced with uncertainty would either increase or decrease their scope of activities, respectively. To reconcile these conflicting positions, we propose a model in which knowledge modularity moderates the effect of technological uncertainty on firms’ research and development (R&D) scope decisions. We develop new measures for R&D scope, knowledge modularity, and technological uncertainty, drawing on population ecology, network theory, and technology management. We test our model empirically using data on patenting activity and firm boundary location in the emerging automotive air bag industry. Our results generally support our model and show that in case of knowledge-generating activities such as R&D, scope decisions under technological uncertainty are more driven by concerns about the risk of obsolescence than the risk of opportunistic behavior. We discuss implications for managerial practice and future research.

Project Quality: The Achilles Heel of Offshore Technology Projects?

Abstract: Technology projects today are being increasingly executed in project sourcing structures that span country boundaries. However, the performance implications of such offshoring efforts are often ambiguous vis-a-vis those that are executed within country boundaries. Further, little is known about project execution capabilities that may be central to improving the performance of offshore technology projects. This study attempts to shed light on the above issues using data from technology projects distributed within and across country boundaries. The study findings are twofold. First, a comparative analysis of project performance across 702 technology projects reveals that projects executed offshore realize significantly lower performance related to quality and technical goals compared with projects executed within country boundaries. Second, a subsequent in-depth analysis of the offshore technology projects in the sample highlights the enabling but differential performance effects of project execution capabilities. Specifically, we find that while risk management planning capability is central to improving project adherence to schedule and budgetary goals, agile project management capability is central to improving project adherence to quality and technical goals in technology projects executed offshore. The study concludes with a discussion of the findings, implications for technology research and practice, limitations, and directions for future research.

Heterogeneous Networked Cooperative Scheduling With Anarchic Particle Swarm Optimization

Abstract: This paper proposes a mathematical model and new solving algorithm for scheduling of a distributed production network with heterogeneous parallel factories distributed in the different geographical places. In this problem, two subproblems must be solved, i.e., 1) assigning jobs to appropriate factory and 2) scheduling jobs on parallel machines in each factory. We also assume that after initial assignment, for better balancing in machines’ loading in the different factories, each job can be shifted among factories. After modeling the problem as mixed integer linear programming, with proposing a new method for solution representation, we propose a novel solving algorithm namely anarchic particle swarm optimization to minimize makespan of jobs. This algorithm is inspired by an anarchic society whose members behave anarchically to improve their situations. By such anarchic particles, the algorithm can prevent falling in local optimum traps. The obtained results of mixed integer linear programming solved by CPLEX are compared with the proposed algorithm, a genetic algorithm and a noncooperative local scheduling for small-sized instances. At the end, the effectiveness of anarchic particle swarm optimization, standard particle swarm optimization, and genetic algorithm are examined on the test problems which contained up to 500 jobs.

Uncertainty, Adaptation, and Alliance Performance

Abstract: We conceptualize alliance adaptation as a bundle of governance-based change practices in ongoing alliances, including contractual alterations, ownership change, board change, monitoring mechanism change, and key personnel turnover. Leveraging a transaction cost perspective, we investigate how changing environmental conditions (i.e., demand uncertainty and technological uncertainty) and unpredictable partner actions (i.e., behavioral uncertainty) trigger ex post governance adaptations in alliances, and how these adaptations in turn affect alliance performance. Using data collected from 178 partner firms in China, we find that the partner firms will undertake more extensive alliance adaptations as demand uncertainty and behavioral uncertainty increase. However, while the extent of alliance adaptations increases as technological uncertainty increases, there is a threshold level of technological uncertainty beyond which the extent of alliance adaptations decreases. The results also suggest that although alliance adaptations enhance alliance performance, this positive impact may diminish after alliance adaptations reach a certain threshold level. Overall, we contribute to the alliance evolution literature by focusing on why partner firms undertake alliance adaptations and how they benefit from these ex post governance adaptations.