Managing Knowledge Creation in High‐Tech R&D Projects: A Multimethod Study

R&D projects in high‐tech organizations bring together diverse knowledge domains to quickly develop new products and processes. The fast‐paced context of high‐tech organizations makes it challenging to create new knowledge and solve complex problems. Managing these R&D projects requires understanding both the mechanisms and the type of knowledge created to achieve project objectives. This research conducts a two‐phased multimethod study to understand knowledge creation in high‐tech R&D projects. The first phase uses qualitative data to develop a theory on knowledge creation in R&D projects. The second phase involves a survey that collects data from R&D projects to test the theory. Results from the case study find that R&D projects benefit from two types of knowledge – objective and intuitive. The case analyses show that intuitive and objective knowledge creation in high‐tech organizations occurs by creating not only diverse but also psychological safe project teams. The large‐scale survey finds that team diversity positively influences objective knowledge creation while psychological safety affects intuitive knowledge creation. Surprisingly, the results show that team diversity negatively affects intuitive knowledge creation. A post hoc analysis takes a more granular look at diversity and shows that different kinds of diversity have different effects on knowledge creation. This helps to better explain how to manage innovation across boundaries. Finally, the analysis shows that both objective and intuitive knowledge influence R&D project performance. Taken together, these results help explain how to manage innovation across functional boundaries to create knowledge and enhance R&D project performance.      

A Decision Support Tool for Predicting Patients at Risk of Readmission: A Comparison of Classification Trees,Logistic Regression,Generalized Additive Models,and Multivariate Adaptive Regression Splines

The number of emergency (or unplanned) readmissions in the United Kingdom National Health Service (NHS) has been rising for many years. This trend, which is possibly related to poor patient care, places financial pressures on hospitals and on national healthcare budgets. As a result, clinicians and key decision makers (e.g., managers and commissioners) are interested in predicting patients at high risk of readmission. Logistic regression is the most popular method of predicting patient‐specific probabilities. However, these studies have produced conflicting results with poor prediction accuracies. We compared the predictive accuracy of logistic regression with that of regression trees for predicting emergency readmissions within 45 days after been discharged from hospital. We also examined the predictive ability of two other types of data‐driven models: generalized additive models (GAMs) and multivariate adaptive regression splines (MARS). We used data on 963 patients readmitted to hospitals with chronic obstructive pulmonary disease and asthma. We used repeated split‐sample validation: the data were divided into derivation and validation samples. Predictive models were estimated using the derivation sample and the predictive accuracy of the resultant model was assessed using a number of performance measures, such as area under the receiver operating characteristic (ROC) curve in the validation sample. This process was repeated 1,000 times—the initial data set was divided into derivation and validation samples 1,000 times, and the predictive accuracy of each method was assessed each time. The mean ROC curve area for the regression tree models in the 1,000 derivation samples was .928, while the mean ROC curve area of a logistic regression model was .924. Our study shows that logistic regression model and regression trees had performance comparable to that of more flexible, data‐driven models such as GAMs and MARS. Given that the models have produced excellent predictive accuracies, this could be a valuable decision support tool for clinicians (healthcare managers, policy makers, etc.) for informed decision making in the management of diseases, which ultimately contributes to improved measures for hospital performance management.     

SME manager development in virtual learning communities and the role of trust: A conceptual study

Small and medium-sized enterprises (SMEs) are seen as vital to economic development. However, managers in these organizations generally have fewer opportunities for training and development than their counterparts in larger organizations. In this paper, the authors argue the need for development opportunities based around ‘virtual’ learning and develop a conceptual framework. The increasing penetration of broadband technologies offers the opportunity to create learning communities to engage SME managers with peers facing similar challenges. For a meaningful experience to be achieved in a virtual community, the role of the facilitator is established as important; however, an area largely overlooked in prior research into networked learning is that of the role of trust and how facilitators can generate it. In order to develop the framework for this paper, the authors review prior research into trust, learning, facilitation and virtual learning communities – the main aim being the identification of those aspects of the facilitator intervention that are most likely to engender trust and participation. Finally, areas for further research are identified.     

Repeated Games Where the Payoffs and Monitoring Structure Are Unknown

This paper studies repeated games with imperfect public monitoring where the players are uncertain both about the payoff functions and about the relationship between the distribution of signals and the actions played. We introduce the concept of perfect public ex post equilibrium (PPXE), and show that it can be characterized with an extension of the techniques used to study perfect public equilibria. We develop identifiability conditions that are sufficient for a folk theorem; these conditions imply that there are PPXE in which the payoffs are approximately the same as if the monitoring structure and payoff functions were known. Finally, we define perfect type‐contingently public ex post equilibria (PTXE), which allows players to condition their actions on their initial private information, and we provide its linear programming characterization.     

Linking Process Quality and Resource Usage: An Empirical Analysis

Motivated by an increasing adoption of evidence‐based medical guidelines in the delivery of medical care, we examine whether increased adherence to such guidelines (typically referred to as higher process quality) is associated with reduced resource usage in the course of patient treatment. In this study, we develop a sample of US hospitals and use cardiac care as our context to empirically examine our questions. To measure a patient's resource usage, we use the total length of stay, which includes any additional inpatient stay necessitated by unplanned readmissions within thirty days after initial hospitalization. We find evidence that higher process quality, and more specifically its clinical (as opposed to its administrative) dimensions, are associated with a reduction in resource usage. Moreover, the standardization of care that is achieved via the implementation of medical guidelines, makes this effect more pronounced in less focused environments: higher process quality is more beneficial when the cardiac department's patient population is distributed across a wider range of medical conditions. We explore the implications of these findings for process‐oriented pay‐for‐performance programs, which tie the reimbursement of hospitals to their adherence to evidence‐based medical guidelines.     

The Role of Project and Organizational Context in Managing High‐tech R&D Projects

High‐tech organizations often struggle to manage different types of R&D projects. Evidence from research and practice suggests that managers frequently categorize and manage projects based on the extent of change triggered in product, process, technology, and market dimensions. However, this can create challenges in high‐tech organizations. This study investigates how high‐tech organizations manage R&D projects based on their learning goals. First, we argue for the benefits of categorizing R&D projects based on the degree of exploration and exploitation learning goals. A qualitative case study from four high‐tech business units involving 10 R&D projects helps understand the different types of projects based on their learning goals. The case study shows that R&D projects in high‐tech organizations typically fall into three categories based on their learning goals: Radical innovation projects, Incremental innovation projects, and Hybrid projects. Second, we iterate between literature and evidence from our qualitative data to theorize how project context and organizational context affect project performance depending on the type of project. The data for the empirical analysis come from a multilevel survey of 110 R&D projects across 34 high‐tech business units. Results show the importance of designing project and organizational context differently for the three types of R&D projects. Collectively, this study offers a new perspective on how to manage high‐tech R&D projects.     

Learning organization,organizational culture,and affective commitment in Malaysia: A person–organization fit theory

ABSTRACT

Based on person–organization fit theory, this article moves beyond theories of motivation and social exchange relationships that have often been used in commitment literature. This article contributes to understanding perceptions of organizational culture (OC) and affective commitment (AC) through perceptions of a learning organization (LO). Such understanding helps to tackle current commitment issues in Malaysia through strategic human resources development (HRD) planning and deploying organization development activities. We examined: (a) the extent to which organizations with an embraced LO mediated OC and AC, (b) the most influential component of OC in promoting employee AC, and (c) the most important component of LO for influencing OC and AC. We obtained 516 respondents (64.5% response rate) in selected Malaysian private organizations. It was concluded that LO mediated the relationship between OC and AC. Respect for people was an influential factor of OC for AC, while empowerment played a significant role in LO culture mediating the relationship between OC and AC. HRD practitioners should strategically plan organizational activities, norms, and policies that promote organizational learning processes and a learning culture to enhance AC.     

Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain

We develop results for the use of Lasso and post‐Lasso methods to form first‐stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p. Our results apply even when p is much larger than the sample size, n. We show that the IV estimator based on using Lasso or post‐Lasso in the first stage is root‐n consistent and asymptotically normal when the first stage is approximately sparse, that is, when the conditional expectation of the endogenous variables given the instruments can be well‐approximated by a relatively small set of variables whose identities may be unknown. We also show that the estimator is semiparametrically efficient when the structural error is homoscedastic. Notably, our results allow for imperfect model selection, and do not rely upon the unrealistic “beta‐min” conditions that are widely used to establish validity of inference following model selection (see also Belloni, Chernozhukov, and Hansen (2011b)). In simulation experiments, the Lasso‐based IV estimator with a data‐driven penalty performs well compared to recently advocated many‐instrument robust procedures. In an empirical example dealing with the effect of judicial eminent domain decisions on economic outcomes, the Lasso‐based IV estimator outperforms an intuitive benchmark. Optimal instruments are conditional expectations. In developing the IV results, we establish a series of new results for Lasso and post‐Lasso estimators of nonparametric conditional expectation functions which are of independent theoretical and practical interest. We construct a modification of Lasso designed to deal with non‐Gaussian, heteroscedastic disturbances that uses a data‐weighted ℓ₁‐penalty function. By innovatively using moderate deviation theory for self‐normalized sums, we provide convergence rates for the resulting Lasso and post‐Lasso estimators that are as sharp as the corresponding rates in the homoscedastic Gaussian case under the condition that logp = o(n^1/3). We also provide a data‐driven method for choosing the penalty level that must be specified in obtaining Lasso and post‐Lasso estimates and establish its asymptotic validity under non‐Gaussian, heteroscedastic disturbances.     

De‐Biasing the Reporting Bias in Social Media Analytics

User‐generated contents (UGC) in social media such as online reviews are inherently incomplete since we do not capture the opinions of users who do not write a review. These silent users may be systematically different than those who speak up. Such differences can be driven by users’ differing sentiments toward their shopping experiences as well as their disposition to generate UGC. Overlooking silent users’ opinions can result in a reporting bias. We develop a method to model users’ UGC generating process and then rectify this bias through an inverse probability weighting (IPW) approach. In the context of users’ movie review activities at Blockbuster.com, our results show that the average probability for a customer to post a review is 0.06 when the customer is unsatisfied with a movie, 0.23 when indifferent, and 0.32 when satisfied. The distribution of user's reporting probability with positive experience first‐order stochastically dominates the one with negative experience. Our approach provides a realistic solution for business managers to properly utilize incomplete UGC.     

A Theory of Disagreement in Repeated Games With Bargaining

This paper proposes a new approach to equilibrium selection in repeated games with transfers, supposing that in each period the players bargain over how to play. Although the bargaining phase is cheap talk (following a generalized alternating‐offer protocol), sharp predictions arise from three axioms. Two axioms allow the players to meaningfully discuss whether to deviate from their plan; the third embodies a “theory of disagreement”—that play under disagreement should not vary with the manner in which bargaining broke down. Equilibria that satisfy these axioms exist for all discount factors and are simple to construct; all equilibria generate the same welfare. Optimal play under agreement generally requires suboptimal play under disagreement. Whether patient players attain efficiency depends on both the stage game and the bargaining protocol. The theory extends naturally to games with imperfect public monitoring and heterogeneous discount factors, and yields new insights into classic relational contracting questions.     

‘Bureaucracy‐lite’ and Continuities in Managerial Work

This paper questions three frequently asserted, interrelated claims about developments in management: that centralized, regulated bureaucratic organizations characterized by hierarchy and rules are inevitably giving way to decentralized and empowered post‐bureaucratic organizations characterized by internal networks and an internal market; that, as a consequence, the traditional managerial role of command and control is being superseded by one of facilitation and coordination; and that, in turn, managerial work as routine administration of work processes is being supplanted by the ‘new managerial work’ of non‐routine leadership and entrepreneurship. It is argued that these claims often rest on caricatures of bureaucracy and network organization and are neither new nor well supported by evidence. Against these claims, the paper adduces case‐study evidence which shows that, despite claims about ‘decentralization’ and ‘empowerment’, organizational change may entail not a radical shift to network organization, but more limited change to a different form of bureaucracy in which hierarchy and rules have been retained but in an attenuated and sharper form –‘bureaucracy‐lite’. Consequently, managerial roles continue to be defined in terms of individual responsibility and vertical accountability for an organizational sub‐unit, and managerial work continues to be preoccupied with monitoring and maintaining work processes, routine direction and control of staff and processing information in order to deal with the ambiguities inherent in the dimensions of managerial ‘responsibility’.     

Reducing Boarding in a Post‐Anesthesia Care Unit

When operating room schedules in hospitals are produced, the constraints and preferences of surgeons and hospital workers are a primary consideration. The downstream impact on post‐operative bed availability is often ignored. This can lead to the boarding of patients overnight in the post‐anesthesia care unit (PACU) because intensive care unit beds are unavailable. In this paper, we apply integer programming and simulation to develop improved surgical scheduling assignments. We want to balance new surgeries with hospital discharges in order to reduce the variability of occupied beds from one day to the next and, as a result, to reduce boarding in the PACU.     

Leveraging Big Data Analytics to Improve Quality of Care in Healthcare Organizations: A Configurational Perspective

Big data analytics (BDA) is beneficial for organizations, yet implementing BDA to leverage profitability is fundamental challenge confronting practitioners. Although prior research has explored the impact that BDA has on business growth, there is a lack of research that explains the full complexity of BDA implementations. Examination of how and under what conditions BDA achieves organizational performance from a holistic perspective is absent from the existing literature. Extending the theoretical perspective from the traditional views (e.g. resource‐based theory) to configuration theory, the authors have developed a conceptual model of BDA success that aims to investigate how BDA capabilities interact with complementary organizational resources and organizational capabilities in multiple configuration solutions leading to higher quality of care in healthcare organizations. To test this model, the authors use fuzzy‐set qualitative comparative analysis to analyse multi‐source data acquired from a survey and databases maintained by the Centres for Medicare & Medicaid Services. The findings suggest that BDA, when given alone, is not sufficient in achieving the outcome, but is a synergy effect in which BDA capabilities and analytical personnel's skills together with organizational resources and capabilities as supportive role can improve average excess readmission rates and patient satisfaction in healthcare organizations.     

Learning in Public Sector Organizations: A Theory of Action Approach

The importance of learning has since long been acknowledged for both business and public sector organizations. However, learning theory and research have tended to neglect the differences between these organizations, and to develop in separate and unrelated directions. To address these developments, this paper purports, first, to develop a theoretical framework that integrates various concepts from the fields of public policy learning and organizational learning, and that is specifically directed at public sector organizations. Second, the paper purports to illustrate the utility of this framework by applying it to policy-making in a Dutch municipality, the Pegasustown prostitution case.     

Competing on Time: An Integrated Framework to Optimize Dynamic Time‐to‐Market and Production Decisions

This study develops a comprehensive framework to optimize new product introduction timing and subsequent production decisions faced by a component supplier. Prior to market entry, the supplier performs process design activities, which improve manufacturing yield and the chances of getting qualified for the customer's product. However, a long delay in market entry allows competitors to enter the market and pass the customer's qualification process before the supplier, reducing the supplier's share of the customer's business. After entering the market and if qualified, the supplier also needs to decide how much to produce for a finite planning horizon by considering several factors such as manufacturing yield and stochastic demand, both of which depend on the earlier time‐to‐market decision. To capture this dependency, we develop a sequential, nested, two‐stage decision framework to optimize the time‐to‐market and production decisions in relation to each other. We show that the supplier's optimal market entry and qualification timing decision need to be revised in real time based on the number of qualified competitors at the time of market‐entry decision. We establish the optimality of a threshold policy. Following this policy, at the beginning of each decision epoch, the supplier should optimally stop preparing for qualification and decide whether to enter the market if her order among qualified competitors exceeds a predetermined threshold. We also prove that the supplier's optimal production policy is a state‐dependent, base‐stock policy, which depends on the time‐to‐market and qualification decisions. The proposed framework also enables a firm to quantify how market conditions (such as price and competitor entry behavior) and operating conditions (such as the rate of learning and inventory/production‐related costs) affect time‐to‐market strategy and post‐entry production decisions.     

Optimization of the Aflatoxin Monitoring Costs along the Maize Supply Chain

An optimization model was used to gain insight into cost‐effective monitoring plans for aflatoxins along the maize supply chain. The model was based on a typical Dutch maize chain, with maize grown in the Black Sea region, and transported by ship to the Netherlands for use as an ingredient in compound feed for dairy cattle. Six different scenarios, with different aflatoxin concentrations at harvest and possible aflatoxin production during transport, were used. By minimizing the costs and using parameters such as the concentration, the variance of the sampling plan, and the monitoring and replacement costs, the model optimized the control points (CPs; e.g., after harvest, before or after transport by sea ship), the number of batches sampled at the CP, and the number of samples per batch. This optimization approach led to an end‐of‐chain aflatoxin concentration below the predetermined limit. The model showed that, when postharvest aflatoxin production was not possible, it was most cost‐effective to collect samples from all batches and replace contaminated batches directly after the harvest, since the replacement costs were the lowest at the origin of the chain. When there was aflatoxin production during storage, it was most cost‐effective to collect samples and replace contaminated batches after storage and transport to avoid the duplicate before and after monitoring and replacement costs. Further along the chain a contaminated batch is detected, the more stakeholders are involved, the more expensive the replacement costs and possible recall costs become.     

Generalized Reduced‐Form Auctions: A Network‐Flow Approach

We develop a network‐flow approach for characterizing interim‐allocation rules that can be implemented by ex post allocations. Our method can be used to characterize feasible interim allocations in general multi‐unit auctions where agents face capacity constraints, both ceilings and floors. Applications include a variety of settings of practical interest, ranging from individual and group‐specific capacity constraints, set‐aside sale, partnership dissolution, and government license reallocation.     

Land‐Price Dynamics and Macroeconomic Fluctuations

We argue that positive co‐movements between land prices and business investment are a driving force behind the broad impact of land‐price dynamics on the macroeconomy. We develop an economic mechanism that captures the co‐movements by incorporating two key features into a DSGE model: We introduce land as a collateral asset in firms' credit constraints, and we identify a shock that drives most of the observed fluctuations in land prices. Our estimates imply that these two features combine to generate an empirically important mechanism that amplifies and propagates macroeconomic fluctuations through the joint dynamics of land prices and business investment.     

Knowledge Building: Developing Employees Through Education

Is it enough for an organization to develop the total individual? Beyond technical expertise and skills necessary to perform jobs, employees who are encouraged to learn are more willing and able to meet the needs, goals, and objectives of the organization. These employees who are will-rounded transition what they have learned into the workings of the organization and for the sake of the growth and development of the organization. Allowing employees to learn, organizations create a workforce capable of applying all the benefits of learning to the growth of the organization. Whether a corporation develops its own university, creates an alliance with traditional educational institutions or creates a virtual university, it gives the employees the opportunity to learn and develop. As Peter Drucker said in a 1994 interview, the growth industry of the future is the education of adults. The education and the development of executives and the workforce are a new commodity and organizations that support education will be placed at a distinct competitive advantage over organizations that do not. Imagine a workplace filled with employees who have realized self-satisfaction, employees who can of gather information, analyze information and make decisions. Imagine a learning organization consisting of employees with greater understanding of corporate politics, employees who are motivated and employees who are persistent in pursuing goals. Is it worth the expense to increase employees' knowledge through education? Is it worth the expense to develop employees to increase productivity and obtain financial goals for the shareholders and stakeholders? I think any organization not willing to invest in an employees' education is an organization that is `running a fever' and an organization that may have difficulties competing globally in the next millennium.     

Why Total Quality Management Programs Do Not Persist: The Role of Management Quality and Implications for Leading a TQM Transformation*

Top‐down total quality management (TQM) programs often fail to create deep and sustained change in organizations. They become a fad soon replaced by another fad. Failure to institutionalize TQM can be attributed to a gap between top management's rhetoric about their intentions for TQM and the reality of implementation in various subunits of the organization. The gap varies from subunit to subunit due to the quality of management in each. By quality of management is meant the capacity of senior team to (1) develop commitment to the new TQM direction and behave and make decisions that are consistent with it, (2) develop the cross‐functional mechanisms, leadership skills, and team culture needed for TQM implementation, and (3) create a climate of open dialogues about progress in the TQM transformation that will enable learning and further change. The TQM transformations will persist only if top management requires and ultimately institutionalizes an honest organizational‐wide conversation that surfaces valid data about the quality of management in each subunit of the firm and leads to changes in management quality or replacement of managers.