Knowledge Management in Strategic Alliances: A Review of Empirical Evidence

Knowledge‐related and organizational learning processes in alliances have received much attention throughout the last 25 years. The field has generated a rapidly growing body of empirical evidence on how knowledge is managed in alliances. However, the sphere is highly complex, fragmented, incoherent, and heterogeneous in terms of the theoretical approaches applied. This paper presents an integrative and organizing framework for the empirical literature on knowledge management in strategic alliances. It illustrates how the knowledge management outcomes of knowledge creation, transfer and application are determined by four distinct sets of factors: knowledge characteristics, partner characteristics, partner interaction, and active knowledge management. Based on this framework, this review analyses and integrates empirical evidence in order to identify where findings converge and where results conflict. So far, research has focused strongly on singular interrelations between these four sets of factors and the transfer of knowledge. Conversely, the questions of how knowledge is created, retained, retrieved and applied and how the interplay of the different factors affects knowledge management in strategic alliances remain widely unexplored. The review concludes with a summary of the current state of the art in empirical research and discusses some promising avenues for future investigation.     

Effects of data breaches from user-generated content: A corporate reputation analysis

A threat for a growing number of firms in various industries is the occurrence of different breaches of sensitive corporate data. These critical events represent vulnerability for firms' corporate reputations, whose multiple dimensions are affected by customers negative perceptions in various ways. Further, in the Industry 4.0 era, the redundancy of scandals on social media can exacerbate negative effects. This paper reports an investigation into the effects of data breaches on corporate reputation dimensions. In this context, the study conducted latent Dirichlet allocation analysis on social media user-generated content (UGC) for a sample of 35 firms in nine industries that suffered a data breach incident between 2013 and 2016. Incidents have been categorized into three categories: “intentional and internal,” “unintentional and internal,” and “intentional and external” data breaches. The aim of the study was to discover how reputational dimensions changed after these critical events and to identify the differences among the types of data breaches.Results reveal that after critical events, more reputational dimensions appear to be relevant. While before critical events, users typically discuss the “perceived quality” of a firm's offer, after all three types of data breaches, users also pay attention to “customer orientation” and “corporate performance” dimensions. Another key reputation dimension, especially after intentional and internal data breaches, is the “firm as employer,” particularly in the context of a lack of investment in training regarding these events.These findings provide key insights for academics and practitioners to understand large-scale data breaches effects and reputational drawbacks after such incidents.     

Workload Control and Order Release: A Lean Solution for Make‐to‐Order Companies

Protecting throughput from variance is the key to achieving lean. Workload control (WLC) accomplishes this in complex make‐to‐order job shops by controlling lead times, capacity, and work‐in‐process (WIP). However, the concept has been dismissed by many authors who believe its order release mechanism reduces the effectiveness of shop floor dispatching and increases work center idleness, thereby also increasing job tardiness results. We show that these problems have been overcome. A WLC order release method known as “LUMS OR” (Lancaster University Management School order release) combines continuous with periodic release, allowing the release of work to be triggered between periodic releases if a work center is starving. This paper refines the method based on the literature (creating “LUMS COR” [Lancaster University Management School corrected order release]) before comparing its performance against the best‐performing purely periodic and continuous release rules across a range of flow directions, from the pure job shop to the general flow shop. Results demonstrate that LUMS COR and the continuous WLC release methods consistently outperform purely periodic release and Constant WIP. LUMS COR is considered the best solution in practice due to its excellent performance and ease of implementation. Findings have significant implications for research and practice: throughput times and job tardiness results can be improved simultaneously and order release and dispatching rules can complement each other. Thus, WLC represents an effective means of implementing lean principles in a make‐to‐order context.     

Housing Conditions and Risk: Reporting on a European Study of Housing Quality and Risk of Accidents for Older People

The declining capacities in older people often lead to limitations in activities of daily living and a simultaneous rising demand for care services. Many of these limitations in daily activities are to a varying degree caused, enhanced or facilitated by disadvantageous housing and environmental conditions. Home modifications have been credited as an important part of the solution, allowing for an extended and safer use of the home. In order to examine this issue in more detail, this paper provides empirical evidence on the type and extent of risks associated with accidents around the home. It reports on findings from a European case study on the difficulties using the home and risks of accidents which may lead to injury. It is argued that healthy housing conditions for older persons are important to maximize the supportive capacity of one's home, to stimulate an active and healthy lifestyle, and to avoid unnecessary institutionalization.     

Media orientation of German university decision makers and the executive influence of public relations

This study investigated the executive influence of public relations managers in the German higher education system. The study is based on a whole-population survey of German university decision makers (N = 1619). It provides evidence that the mediatization of German higher education offers an important opportunity for the empowerment of university PR departments. They can benefit from the New Public Management reforms in recent years as power shifts to management-oriented administrators and the public image of universities turns into a competitive asset. Nevertheless, an indispensable precondition for PR departments’ organizational advancement resides in professionalization. PR workers need to claim a status of expert boundary spanners between their universities and the public stakeholders.     

Efficient Semiparametric Estimation of the Fama–French Model and Extensions

This paper develops a new estimation procedure for characteristic‐based factor models of stock returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving as time‐varying weights and a set of univariate nonparametric functions relating security characteristic to the associated factor betas. We use a time‐series and cross‐sectional pooled weighted additive nonparametric regression methodology to simultaneously estimate the factor returns and characteristic‐beta functions. By avoiding the curse of dimensionality, our methodology allows for a larger number of factors than existing semiparametric methods. We apply the technique to the three‐factor Fama–French model, Carhart's four‐factor extension of it that adds a momentum factor, and a five‐factor extension that adds an own‐volatility factor. We find that momentum and own‐volatility factors are at least as important, if not more important, than size and value in explaining equity return comovements. We test the multifactor beta pricing theory against a general alternative using a new nonparametric test.     

Multivariate Real-Time Signal Extraction by a Robust Adaptive Regression Filter

We propose a new regression-based filter for extracting signals online from multivariate high frequency time series. It separates relevant signals of several variables from noise and (multivariate) outliers.

Unlike parallel univariate filters, the new procedure takes into account the local covariance structure between the single time series components. It is based on high-breakdown estimates, which makes it robust against (patches of) outliers in one or several of the components as well as against outliers with respect to the multivariate covariance structure. Moreover, the trade-off problem between bias and variance for the optimal choice of the window width is approached by choosing the size of the window adaptively, depending on the current data situation.

Furthermore, we present an advanced algorithm of our filtering procedure that includes the replacement of missing observations in real time. Thus, the new procedure can be applied in online-monitoring practice. Applications to physiological time series from intensive care show the practical effect of the proposed filtering technique.