Reciprocal relationships between psychosocial work characteristics and sleep problems: A two-wave study

Sleep problems are common and impair the health and productivity of employees. Work characteristics constitute one possible cause of sleep problems, and sleeping poorly might influence wellbeing and performance at work. This study examines the reciprocal associations between sleep problems and psychosocial work characteristics. The participants were 1744 full-time employed individuals (56% women; mean age 38 years in 2007) from the Young Finns study who responded to questionnaires on work characteristics (conceptualised by the demand–control model and effort–reward imbalance model) and sleep problems (Jenkins Sleep Scale) in 2007 and 2012. Cross-lagged structural equation models are used to examine the associations. The results show that low control and low rewards at baseline predicted sleep problems. Baseline sleep problems predicted higher effort, higher effort–reward imbalance, and lower reward. Sleep problems also predicted lower odds for belonging to the low (rather than high) job strain group and active jobs group. The association between work characteristics and sleep problems appears to be reciprocal, with a stressful work environment increasing sleep problems, and sleep problems influencing future work characteristics. The results emphasise the importance of interventions aimed at both enhancing sleep quality and reducing psychosocial risks at work.     

Data-driven operations management: organisational implications of the digital transformation in industrial practice

Abstract

The ongoing digital transformation on industry has so far mostly been studied from the perspective of cyber-physical systems solutions as drivers of change. In this paper, we turn the focus to the changes in data management resulting from the introduction of new digital technologies in industry. So far, data processing activities in operations management have usually been organised according to the existing business structures inside and in-between companies. With increasing importance of Big Data in the context of the digital transformation, the opposite will be the case: business structures will evolve based on the potential to develop value streams offered on the basis of new data processing solutions. Based on a review of the extant literature, we identify the general different fields of action for operations management related to data processing. In particular, we explore the impact of Big Data on industrial operations and its organisational implications.     

The surprising effectiveness of earliest operation due-date sequencing

In make-to order production, schedule reliability is very important but still not sufficiently accomplished in industrial practice by the vast majority of companies. It has been known for long that processing the orders at a workstation in the order of their operation due-dates can compensate for lateness in the arrival at the workstation within certain boundaries. The paper analyses the effectiveness of earliest operation due date (EODD) sequencing by comparing it to an optimistic theoretical boundary. The surprising result is that EODD can nearly fully exploit the theoretical potential. It should therefore be used in practice whenever schedule reliability is important, with only few exceptions. Its effectiveness though is increasing with the workstation’s WIP level and thus is in conflict with the objective to reduce WIP levels and throughput times. A simple forecasting model allows to assess the extent to which lateness can be compensated by EODD sequencing and which schedule compliance can be achieved.     

An integrative model of leadership behavior

Decades of questionnaire and interview studies have revealed various leadership behaviors observed in successful leaders. However, little is known about the actual behaviors that cause those observations. Given that lay observers are prone to cognitive biases, such as the halo effect, the validity of theories that are exclusively based on observed behaviors is questionable. We thus follow the call of leading scientists in the field and derive a parsimonious model of leadership behavior that is informed by established psychological theories. Building on the taxonomy of Yukl (2012), we propose three task-oriented behavior categories (enhancing understanding, strengthening motivation and facilitating implementation) and three relation-oriented behavior categories (fostering coordination, promoting cooperation and activating resources), each of which is further specified by a number of distinct behaviors. While the task-oriented behaviors are directed towards the accomplishment of shared objectives, the relation-oriented behaviors support this process by increasing the coordinated engagement of the team members. Our model contributes to the advancement of leadership behavior theory by (1) consolidating current taxonomies, (2) sharpening behavioral concepts of leadership behavior, (3) specifying precise relationships between those categories and (4) spurring new hypotheses that can be derived from existing findings in the field of psychology. To test our model as well as the hypotheses derived from this model, we advocate the development of new measurements that overcome the limitations associated with questionnaire and interview studies.     

On global integer extrema of real-valued box-constrained multivariate quadratic functions

Determining global integer extrema of an real-valued box-constrained multivariate quadratic functions is a very difficult task. In this paper, we present an analytic method, which is based on a combinatorial optimization approach in order to calculate global integer extrema of a real-valued box-constrained multivariate quadratic function, whereby this problem will be proven to be as NP-hard via solving it by a Travelling Salesman instance. Instead, we solve it using eigenvalue theory, which allows us to calculate the eigenvalues of an arbitrary symmetric matrix using Newton’s method, which converges quadratically and in addition yields a Jordan normal form with \(1 \times 1\)-blocks, from which a special representation of the multivariate quadratic function based on affine linear functions can be derived. Finally, global integer minimizers can be calculated dynamically and efficiently most often in a small amount of time using the Fourier–Motzkin- and a Branch and Bound like Dijkstra-algorithm. As an application, we consider a box-constrained bivariate and multivariate quadratic function with ten arguments.     

The no-wait job shop with regular objective: a method based on optimal job insertion

The no-wait job shop problem (NWJS-R) considered here is a version of the job shop scheduling problem where, for any two operations of a job, a fixed time lag between their starting times is prescribed. Also, sequence-dependent set-up times between consecutive operations on a machine can be present. The problem consists in finding a schedule that minimizes a general regular objective function. We study the so-called optimal job insertion problem in the NWJS-R and prove that this problem is solvable in polynomial time by a very efficient algorithm, generalizing a result we obtained in the case of a makespan objective. We then propose a large neighborhood local search method for the NWJS-R based on the optimal job insertion algorithm and present extensive numerical results that compare favorably with current benchmarks when available.     

Forecast mean squared error reductionin the VAR(1) process

When VAR models are used to predict future outcomes, the forecast error can be substantial. Through imposition of restrictions on the off-diagonal elements of the parameter matrix, however, the information in the process may be condensed to the marginal processes. In particular, if the cross-autocorrelations in the system are small and only a small sample is available, then such a restriction may reduce the forecast mean squared error considerably.

In this paper, we propose three different techniques to decide whether to use the restricted or unrestricted model, i.e. the full VAR(1) model or only marginal AR(1) models. In a Monte Carlo simulation study, all three proposed tests have been found to behave quite differently depending on the parameter setting. One of the proposed tests stands out, however, as the preferred one and is shown to outperform other estimators for a wide range of parameter settings.