The Role of Task Uncertainty in IT Project Team Advice Networks

Advice seeking is often the most critical success factor in today's IT project teams. To understand how advice seekers are motivated, we integrate the antecedents of advice seeking—as defined by network theory (Granovetter, 1983)—into a cost/benefit model based on expectancy theory (Vroom, 1964). To contribute to the research on advice network formation, we integrate the role of task uncertainty—one of the defining characteristics of IT projects—into that research (Wallace & Keil, 2004). Based on a controlled quasi‐experiment, this study demonstrates that when task uncertainty is low, individuals with attractive personalities and similar demographics will be sought out for advice more frequently, regardless of their knowledge and resources (i.e., the benefits to the advice seekers). However, when task uncertainty is high, individuals with greater knowledge and access to resources are sought out more often in an advice network. These results provide clarity to prior research that has found mixed results concerning the effectiveness of the traditional antecedents to advice seeking (e.g., knowledge, power, and transactive memory) (e.g., Xu, Kim, & Kankanhalli, 2010a). In addition, project managers may choose to alter their team structure in order to optimize the advice network based on the anticipated level of IT project risk or task uncertainty.     

Giving as Good as They Get? Organization and Employee Expectations of Ethical Business Practice

Corporate malpractice and malfeasance on an unprecedented scale have brought ethical issues to the fore and accentuated demands from activists, governments, and the public for greater corporate social responsibility (CSR). The predominant response of researchers and policymakers has been to focus on the external impact of business operations and the merits of regulation or persuasion in achieving more responsible practice in these areas. In this article, we focus on a less well explored aspect of CSR, namely the evaluation of an organization's CSR activities by its internal stakeholders (i.e., employees). Salient CSR literature is reviewed to differentiate between CSR and ethical business practice (EBP), conceptualizing the latter as the internal manifestation of CSR as represented by an organization's values and vision, strategy and policy, systems and procedures, and people management practices. This article assesses organizational espousal of EBP in three ways: how successfully it is communicated to employees, how closely espousal aligns with employee expectation, and how this evaluation impacts on employee commitment. Our research approach aligns with and extends previous work in this area that identifies the likelihood of a “false consensus bias” by managers in assuming congruence between organization espousal of EBP and employee expectation. A conceptual model is offered to explain possible employee responses to an organization's EBP. This relates organization espousal of EBP to employee assessment of its salience to identify three positions on commitment that employees can adopt—abrogated, continuance, and affective commitment—together with their likely behavioral implications. The analysis generates a series of research questions and related areas of exploration to empirically test the conceptual model.     

Formal Infrastructure and Ethical Decision Making: An Empirical Investigation and Implications for Supply Management

Building on Rest's (1986) conceptual model of ethical decision making, we derive and empirically test a model that links an organization's formal ethical infrastructure to individuals’ moral awareness of ethical situations, moral judgment, and moral intention. We contribute to the literature by shedding light on the importance of a multifaceted formal ethical infrastructure—consisting of formal communication, recurrent communication, formal surveillance, and formal sanctions—as a crucial antecedent of moral awareness. In so doing, we discern how these four elements of a formal ethical infrastructure combine to collectively influence moral awareness based on a second‐order factor structure using structural equation modeling. We test our model based on survey data from 805 respondents with significant work experience across three separate ethical scenarios. Our results across the three scenarios provide overall support for our model. We found that a second‐order factor structure for the formal ethical infrastructure explains the variance among the four infrastructure elements and that a multifaceted formal ethical infrastructure significantly increases moral awareness. Our results further suggest a strong positive effect of moral awareness on moral judgment, which in turn was found to have a positive impact on moral intention. These results were substantiated when taking several individual and contextual control variables into account, such as gender, age, religiosity, work satisfaction, and a de facto ethical climate. Implications for theory, practice, and supply management are discussed.     

Competition and The Operational Performance of Hospitals: The Role of Hospital Objectives

We examine the effect of a hospital's objective (i.e., non‐profit vs. for‐profit) in hospital markets for elective care. Using game‐theoretic analysis and queueing models to capture the operational performance of hospitals, we compare the equilibrium behavior of three market settings in terms of such criteria as waiting times and patient costs from waiting and hospital payments. In the first setting, a monopoly, patients are served exclusively by a single non‐profit hospital; in the second, a homogeneous duopoly, patients are served by two competing non‐profit hospitals. In our third setting, a heterogeneous duopoly, the market is served by one non‐profit hospital and one for‐profit hospital. A non‐profit hospital provides free care to patients, although they may have to wait; for‐profit hospitals charge a fee to provide care with minimal waiting. A comparison between the monopolistic and each of the duopolistic settings reveals that the introduction of competition can hamper a hospital's ability to attain economies of scale and can also increase waiting times. Moreover, the presence of a for‐profit sector may be desirable only when the hospital market is sufficiently competitive. A comparison across the duopolistic settings indicates that the choice between homogeneous and heterogeneous competition depends on the patients' willingness to wait before receiving care and the reimbursement level of the non‐profit sector. When the public funder is not financially constrained, the presence of a for‐profit sector may allow the funder to lower both the financial costs of providing coverage and the total costs to patients. Finally, our analysis suggests that the public funder should exercise caution when using policy tools that support the for‐profit sector—for example, patient subsidies—because such tools may increase patient costs in the long run; it might be preferable to raise the non‐profit sector's level of reimbursement.     

Managers’ Resources for Authentic Leadership – a Multi‐study Exploration of Positive Psychological Capacities and Ethical Organizational Climates

While authentic leadership is highly valued in today's business world, managers do not necessarily have the resources to attain it. Building on conservation of resources theory, we propose a conceptual model to address how personal and contextual resources predict authentic leadership. Study 1 analyses the day‐to‐day variability in managers’ positive psychological capacities as personal resources in relation to changes in authentic leadership. In addition, it tests ethical organizational climates as stable, contextual resources for authentic leadership. In Study 2, we replicate our results on the between‐person level and extend the research model by exploring promotion focus as a link in the relationship between personal resources and authentic leadership. Evidence from an experience sampling study with 89 managers surveyed daily on 10 consecutive working days (Study 1) and a field survey of 130 managers at two points in time (Study 2) supports the hypothesized role of personal resources and promotion focus for authentic leadership. In both studies, only principled but not benevolent ethical organizational climates emerged as a contextual resource for authentic leadership. We discuss the implications for current management research and practice.     

Designing Distribution Systems to Support Vendor Strategies in Supply Chain Management*

The interactions among a firm's distribution strategy, market share, and distribution costs are an important consideration in the design of supply chain networks. However, these interactions are largely ignored by existing distribution system design methodologies, which assume demand is constant regardless of the firm's distribution strategy. This paper describes a multidisciplinary framework that considers these interactions in the design of “profit maximizing” distribution networks. The framework employs two major decision support methodologies: (1) binary logit models for estimating market share considering various demand-influencing parameters such as product price and distribution service, and (2) a mixed-integer programming (MIP) model for finding optimal distribution network designs. We applied the framework to an actual design problem facing a national distributor of industrial chemical products. The test results verify the framework's large-scale capability and the potential benefit of the integrated solution methodology.     

The Global Food Industry and “Creative Capitalism”: The Partners in Food Solutions Sustainable Business Model

Rising global food prices have driven 44 million additional people into extreme poverty—and malnutrition—in developing countries since June 2010. Partners in Food Solutions (PFS), a nonprofit social enterprise affiliated with General Mills, is proposed as the conduit for food industry managers, engineers, and scientists to initially advise small‐ and medium‐sized African mills and food processors—and later other developing countries—on improving supply chain management by addressing manufacturing problems, developing products, improving packaging, extending product shelf, and finding new product markets. In this article, the “creative capitalism” model of sustainability and social and environmental responsibility is applied to the food manufacturing industry's efforts supporting PFS. Furthermore, the evolution of the sustainable business model developed by PFS is thoroughly described, explained, and analyzed as a generic model of social enterprise to be “scaled up” by the global food manufacturing industry. A summary of salient points conclude the article.     

A Modeling Framework for Supply Chain Simulation: Opportunities for Improved Decision Making*

Owing to its inherent modeling flexibility, simulation is often regarded as the proper means for supporting decision making on supply chain design. The ultimate success of supply chain simulation, however, is determined by a combination of the analyst's skills, the chain members' involvement, and the modeling capabilities of the simulation tool. This combination should provide the basis for a realistic simulation model, which is both transparent and complete. The need for transparency is especially strong for supply chains as they involve (semi)autonomous parties each having their own objectives. Mutual trust and model effectiveness are strongly influenced by the degree of completeness of each party's insight into the key decision variables. Ideally, visual interactive simulation models present an important communicative means for realizing the required overview and insight. Unfortunately, most models strongly focus on physical transactions, leaving key decision variables implicit for some or all of the parties involved. This especially applies to control structures, that is, the managers or systems responsible for control, their activities and their mutual attuning of these activities. Control elements are, for example, dispersed over the model, are not visualized, or form part of the time‐indexed scheduling of events. In this article, we propose an alternative approach that explicitly addresses the modeling of control structures. First, we will conduct a literature survey with the aim of listing simulation model qualities essential for supporting successful decision making on supply chain design. Next, we use this insight to define an object‐oriented modeling framework that facilitates supply chain simulation in a more realistic manner. This framework is meant to contribute to improved decision making in terms of recognizing and understanding opportunities for improved supply chain design. Finally, the use of the framework is illustrated by a case example concerning a supply chain for chilled salads.     

Supply Network Structure,Visibility, and Risk Diffusion: A Computational Approach

Understanding and managing supply chain risks is a critical functional competency for today's global enterprises. A lack of this competency can have significant negative outcomes, including costly production and delivery delays, loss of future sales, and a tarnished corporate image. The ability to identify and mitigate risks, however, is complicated as supply chains are becoming increasingly global, complex, and interconnected. Drawing on the complex systems and epidemiology literature, and using a computational modeling and network analysis approach, we examine the impact of global supply network structure on risk diffusion and supply network health and demonstrate the importance of supply network visibility. Our results show a significant association between network structure and both risk diffusion and supply network health. In particular, our results indicate that small‐world supply network topologies consistently outperform supply networks with scale‐free characteristics. Theoretically, our study contributes to our understanding of risk management and supply networks as complex networked systems using a computational approach. Managerially, our study illustrates how decision makers can benefit from a network analytic approach to develop a more holistic understanding of system‐wide risk diffusion and to guide network governance policies for more favorable health level outcomes. The article concludes by highlighting the main findings and discussing possibilities of future research directions.     

Elusive Critical Elements of Transformative Risk Assessment Practice and Interpretation: Is Alternatives Analysis the Next Step?

This article argues that “game‐changing” approaches to risk analysis must focus on “democratizing” risk analysis in the same way that information technologies have democratized access to, and production of, knowledge. This argument is motivated by the author's reading of Goble and Bier's analysis, “Risk Assessment Can Be a Game‐Changing Information Technology—But Too Often It Isn't” (Risk Analysis, 2013; 33: 1942–1951), in which living risk assessments are shown to be “game changing” in probabilistic risk analysis. In this author's opinion, Goble and Bier's article focuses on living risk assessment's potential for transforming risk analysis from the perspective of risk professionals—yet, the game‐changing nature of information technologies has typically achieved a much broader reach. Specifically, information technologies change who has access to, and who can produce, information. From this perspective, the author argues that risk assessment is not a game‐changing technology in the same way as the printing press or the Internet because transformative information technologies reduce the cost of production of, and access to, privileged knowledge bases. The author argues that risk analysis does not reduce these costs. The author applies Goble and Bier's metaphor to the chemical risk analysis context, and in doing so proposes key features that transformative risk analysis technology should possess. The author also discusses the challenges and opportunities facing risk analysis in this context. These key features include: clarity in information structure and problem representation, economical information dissemination, increased transparency to nonspecialists, democratized manufacture and transmission of knowledge, and democratic ownership, control, and interpretation of knowledge. The chemical safety decision‐making context illustrates the impact of changing the way information is produced and accessed in the risk context. Ultimately, the author concludes that although new chemical safety regulations do transform access to risk information, they do not transform the costs of producing this information—rather, they change the bearer of these costs. The need for further risk assessment transformation continues to motivate new practical and theoretical developments in risk analysis and management.     

Complementary effects of clusters and networks on firm innovation: A conceptual model

We develop a conceptual model that explains how a firm's cluster and network complement each other in enhancing the firm's likelihood of technological innovations. We identify critical innovation catalysts-awareness and motivation—and innovation barriers—resource constraints, organizational rigidity, and uncertainty. Our conceptual model explains how various factors in the cluster such as competitive intensity, social interaction intensity, and cluster vitality and network factors such as resource potential, acquisition orientation, co-development orientation, and network vitality impact innovation catalysts and barriers and subsequently the firm's likelihood of generating incremental and breakthrough innovations. We discuss several promising avenues for future research.     

A Comparison of Selected Artificial Neural Networks that Help Auditors Evaluate Client Financial Viability

This study compares the performance of three artificial neural network (ANN) approaches—backpropagalion, categorical learning, and probabilistic neural network—as classification tools to assist and support auditor's judgment about a client's continued financial viability into the future (going concern status). ANN performance is compared on the basis of overall error rates and estimated relative costs of misclassificaticn (incorrectly classifying an insolvent firm as solvent versus classifying a solvent firm as insolvent). When only the overall error rate is considered, the probabilistic neural network is the most reliable in classification, followed by backpropagation and categorical learning network. When the estimated relative costs of misclassification are considered, the categorical learning network is the least costly, followed by backpropagation and probabilistic neural network.     

A model of e-commerce use by internationalizing SMEs

Globalization and e-commerce pose new challenges and provide new competitive opportunities, especially for small- and medium-sized enterprises (SMEs) seeking to broaden their involvement into new international markets. Yet, SMEs are only beginning to embrace these new opportunities. The authors derive a model describing the use of e-commerce by internationalizing SMEs, developed by integrating findings from case studies of 12 Canadian SMEs exporting to Japan with a broad range of management theories. The cases represent two very different sectors: hospitality/tourism and high tech. The research first describes how the firms use e-commerce internationally, by examining resource commitment, web function and cultural adaptation. It then explains why the firms use e-commerce, by relating two types of environmental variables — market changes and industry norms — and three firm factors — technical capability, cultural capability and firm size — to the firms' adoption of e-commerce. Implications for SME managers are discussed and nine propositions are put forth to guide and focus future research.     

Axiomatic Based Decomposition for Conceptual Product Design

This paper describes a structured methodology for decomposing the conceptual design problem in order to facilitate the design process and result in improved conceptual designs that better satisfy the original customer requirements. The axiomatic decomposition for conceptual design method combines Alexander's network partitioning formulation of the design problem with Suh's Independence Axiom. The axiomatic decomposition method uses a cross‐domain approach in a House of Quality context to estimate the interactions among the functional requirements that are derived from a qualitative assessment of customer requirements. These interactions are used in several objective functions that serve as criteria for decomposing the design network. A new network partitioning algorithm is effective in creating partitions that maximize the within‐partition interactions and minimize the between‐partition interactions with appropriate weightings. The viability, usability, and value of the axiomatic decomposition method were examined through analytic comparisons and qualitative assessments of its application. The new method was examined using students in engineering design capstone courses and it was found to be useable and did produce better product designs that met the customer requirements. The student‐based assessment revealed that the process would be more effective with individuals having design experience. In a subsequent assessment with practicing industrial designers, it was found that the new method did facilitate the development of better designs. An important observation was the need for limits on partition size (maximum of four functional requirements.) Another issue identified for future research was the need for a means to identify the appropriate starting partition for initiating the design.     

Supply Chain Inventory Replenishment: The Debiasing Effect of Declarative Knowledge

Previous experimental research demonstrates that inefficient replenishment decision making in the supply chain can be caused by specific judgment and decision biases. Based on the literature we use controlled experiments involving both student subjects and supply chain managers to test debiasing interventions that provide declarative knowledge, which is theorized to enhance the acquisition of procedural knowledge. We first investigate the effects of three debiasing components in a single‐echelon setting: knowledge of bullwhip, inventory position (IP), and use of a target order‐up‐to quantity. Experiment 1 (N = 1,608 decisions by 67 student subjects) using a 2 × 2 × 2 factorial design for the three components finds that the conceptual understanding of IP is salient for efficient replenishment decisions. We next examine the effects of the components in a simulated, multi‐echelon, serial supply chain, which introduces the additional complexity of coordination risk. Experiment 2 (N = 3,072 decisions by 128 student subjects) using a 2 × 2 × 2 factorial design finds that although subjects benefit from training components, there is evidence of cognitive overload with an increased quantity of information. Finally we test whether these debiasing components may be an effective training program for practicing supply chain managers who can be expected to have higher levels of procedural knowledge through experience gained in the field. Experiment 3 (N = 864 decisions by 36 supply chain managers) using a 2 × 1 design investigates the effects of an instructional training intervention which includes all three debiasing components and finds the intervention to reduce costs by 14%. We provide avenues for future research and successful practice.     

Product Life‐Cycle Management of Packaged Software

A software product becomes less valuable for its consumers over time due to technological and economic obsolescence. As a result, firms have an opportunity to introduce and sell upgrades that provide higher utility to consumers compared to an older and out‐of‐date software product. In a market that is growing and consists of homogeneous customers, we prove that the optimal upgrade intervals are monotonically increasing throughout the product's life cycle solely because of demand and cost considerations. This finding is in conformity with empirical evidence, thus validating our theoretical model. We then present comparative statics results to show that increase in the rate of obsolescence or network externalities may sometimes increase upgrade intervals for early upgrades and decrease these for later upgrades in the product's life cycle, but increase in market growth rate always decreases these intervals. Further, when successive software upgrades are forward compatible, upgrade intervals are longer than when they are not. Finally, we present three separate extensions of our model to showcase the robustness of our results. Since upgrade development costs depend on upgrade intervals, these insights help managers understand how costing for upgrades changes over the product's life cycle.     

A Purchasing Framework for B2B Pricing Decisions and Risk‐sharing in Supply Chains*

This paper presents a common modelling structure for (i) the implementation of operational policies by individual purchasing managers of risk‐sharing agreements among supply‐chain partners, and (ii) the integration of brick and click purchasing policies in a B2B. The problem of price uncertainty created within these two environments is modelled as a stochastic repetitive‐sales problem, applicable to any probability distribution. The model identifies sufficient conditions for regenerative ordering cycles, which allows for the use of the renewal reward theorem. The end result is a two‐price purchasing policy, which may substantially ease implementation problems across a global corporation's purchasing managers world‐wide and across B2B markets.     

Why Competition from a Multi‐Channel E‐Tailer Does Not Always Benefit Consumers*

Empirical studies have delivered mixed conclusions on whether the widely acclaimed assertions of lower electronic retail (e‐tail) prices are true and to what extent these prices impact conventional retail prices, profits, and consumer welfare. For goods that require little in‐person pre‐ or postsales support such as CDs, DVDs, and books, we extend Balasubramanian's e‐tailer‐in‐the‐center, spatial, circular market model to examine the impact of a multichannel e‐tailer's presence on retailers' decisions to relocate, on retail prices and profits, and consumer welfare. We demonstrate several counter‐intuitive results. For example, when the disutility of buying online and shipping costs are relatively low, retailers are better off by not relocating in response to an e‐tailer's entry into the retail channel. In addition, such an entry—a multichannel strategy—may lead to increased retail prices and increased profits across the industry. Finally, consumers can be better off with less channel competition. The underlying message is that inferences regarding prices, profits, and consumer welfare critically depend on specifications of the good, disutility and shipping costs versus transportation costs (or more generally, positioning), and competition.     

Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?

Integrated assessment models offer a crucial support to decisionmakers in climate policy making. For a full understanding and corroboration of model results, analysts ought to identify the exogenous variables that influence the model results the most (key drivers), appraise the relevance of interactions, and the direction of change associated with the simultaneous variation of uncertain variables. We show that such information can be directly extracted from the data set produced by Monte Carlo simulations. Our discussion is guided by the application to the well‐known DICE model of William Nordhaus. The proposed methodology allows analysts to draw robust insights into the dependence of future atmospheric temperature, global emissions, and carbon costs and taxes on the model's exogenous variables.     

Dual Objective Segmentation to Improve Targetability: An Evolutionary Algorithm Approach**

Cluster‐based segmentation usually involves two sets of variables: (i) the needs‐based variables (referred to as the bases variables), which are used in developing the original segments to identify the value, and (ii) the classification or background variables, which are used to profile or target the customers. The managers’ goal is to utilize these two sets of variables in the most efficient manner. Pragmatic managerial interests recognize the underlying need to start shifting from methodologies that obtain highly precise value‐based segments but may be of limited practical use as they provide less targetable segments. Consequently, the imperative is to shift toward newer segmentation approaches that provide greater focus on targetable segments while maintaining homogeneity. This requires dual objective segmentation, which is a combinatorially difficult problem. Hence, we propose and examine a new evolutionary methodology based on genetic algorithms to address this problem. We show, based on a large‐scale Monte Carlo simulation and a case study, that the proposed approach consistently outperforms the existing methods for a wide variety of problem instances. We are able to obtain statistically significant and managerially important improvements in targetability with little diminution in the identifiability of value‐based segments. Moreover, the proposed methodology provides a set of good solutions, unlike existing methodologies that provide a single solution. We also show how these good solutions can be used to plot an efficient Pareto frontier. Finally, we present useful insights that would help managers in implementing the proposed solution approach effectively.