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The purpose of this paper is to describe the introduction of simple assembly line balancing problems (SALBPs) under four joint combinations of two learning effects and two deterioration effects.

In this paper, effects of learning and deterioration are considered simultaneously. By the effects of learning and deterioration, it is meant that the task time of a task is defined by increasing function of its execution start time and position in the sequence.

It was shown that polynomial solutions can be obtained for SALBP under four joint combinations of two learning effects and two deterioration effects. Furthermore, this paper also discusses the adaptations of the proposed models by using known ALB problem.

In this paper, SALBPs were introduced under four joint combinations of two learning effects and two deterioration effects. Position dependent learning effect and time dependent learning effect were considered for learning effect when linear deterioration effect and non‐linear deterioration effect were taken up.

As staff are vital to successful re‐branding, particularly in the charity sector where restricted budgets limit reliance on external marketing, it is important to understand the impact of re‐branding on staff. This study aims to examine the effect of time on staff knowledge, attitudes and behaviour and, in addition, the interaction of time with seniority, tenure and level of support for re‐branding.

The paper first explores the literature from both the for‐profit and non‐profit sectors. A quantitative study was undertaken in nine leading, UK charities that had re‐branded two, three and four years ago; n=345. The data were analysed using one‐way and two‐way ANOVAs.

A negative relationship was found between time since re‐branding and the three constructs of knowledge, attitudes and behaviour. But this consistency was not mirrored by a consistency in the impact of interaction effects.

Re‐branding is not a one‐off event. To sustain its benefits, organizations need to re‐visit its outcomes on a regular basis to ensure staff retain new knowledge, remain positively motivated and maintain their recently adapted behaviours over time.

Thought to be the first empirical paper to explore the effects of re‐branding over time. Furthermore, the findings contradict those from the extant literature that claim that organizational change requires a “settling in” period. By contrast these findings suggest that the positive effects of re‐branding are best felt in the immediate wake of re‐branding and thereafter fade over time.

The role of money and monetary policy of the central bank in pursuing macroeconomic stability has significantly changed over the period since the end of World War II. Globalization, liberalization, integration, and transition processes generally shaped the crucial milestones of the macroeconomic development and substantial features of economic policy and its framework in Europe. Policy-driven changes together with variety of exogenous shocks significantly affected the key features of macroeconomic environment on the European continent that fashioned the framework and design of monetary policies.

This chapter examines the key basis of the central bank’s monetary policy on its way to pursue and preserve the internal and external stability of the purchasing power of money. Substantial elements of the monetary policy like objectives and strategies are not only generally introduced but also critically discussed according to their accuracy, suitability, and reliability in the changing macroeconomic conditions. Brief overview of the Eurozone common monetary policy milestones and the past Eastern bloc countries’ experience with a variety of exchange rate regimes provides interesting empirical evidence on origins and implications of vital changes in the monetary policy conduction in Europe and the Eurozone.

While digital media changed the nature of communication in service contexts, often allowing customers to interact instantly with service providers, the implications and opportunities for managing service employees are widely unknown. This is surprising, given that service employees are an important determinant of service firms’ success. This article examines the effects of real-time performance feedback on employees’ service performance and investigates both how and under what conditions timely feedback encourages employees’ engagement.

Two experiments test the conceptual model and the proposed hypotheses. A field experiment uses real customer feedback gathered after interaction with the app-chat of a large telecommunications provider. It tests the effect of feedback timing on service employees’ performance and also examines the effect of feedback timing on their engagement. A subsequent scenario-based experiment then investigates the influence of selected moderators on the feedback timing–engagement relationship.

This article finds that real-time feedback leads to greater service performance than subsequent feedback. Furthermore, real-time feedback positively affects service employee engagement through the perceived controllability of the feedback and the service situation. Finally, feedback valence, task goals, individuals’ need for closure (NCL), and gender interact with feedback timing to influence employee engagement.

This research investigates the potential of real-time performance feedback for service firms, combines and extends a variety of literature streams, and provides recommendations for the future management of service employees.

Measures are important to healthcare outcomes. Outcome changes result from deliberate selective intervention introduction on a measure. If measures can be characterized and categorized, then the resulting schema may be generalized and utilized as a framework for uniquely identifying, packaging and comparing different interventions and probing target systems to facilitate selecting the most appropriate intervention for maximum desired outcomes. Measure characterization was accomplished with multi-axial statistical analysis and measure categorization by logical tabulation. The measure of interest is a key provider productivity index: “patient visits per hour,” while the specific intervention is “patient schedule manipulation by overbooking.” The paper aims to discuss these issues.

For statistical analysis, interrupted time series (ITS), robust-ITS and outlier detection models were applied to an 18-month data set that included patient visits per hour and intervention introduction time. A statistically significant change-point was determined, resulting in pre-intervention, transitional and post-effect segmentation. Linear regression modeling was used to analyze pre-intervention and post-effect mean change while a triangle was used to analyze the transitional state. For categorization, an “intervention moments” table was constructed from the analysis results with: time-to-effect, pre- and post-mean change magnitude and velocity; pre- and post-correlation and variance; and effect decay/doubling time. The table included transitional parameters such as transition velocity and transition footprint visualization represented as a triangle.

The intervention produced a significant change. The pre-intervention and post-effect means for patient visits per hour were statistically different (0.38, p=0.0001). The pre- and post-variance change (0.23, p=0.01) was statistically significant (variance was higher post-intervention, which was undesirable). Post-intervention correlation was higher (desirable). Decay time for the effect was calculated as 11 months post-effect. Time-to-effect was four months; mean change velocity was +0.094 visits per h/month. A transition triangular footprint was produced, yielding 0.35 visits per hr/month transition velocity. Using these results, the intervention was fully profiled and thereby categorized as an intervention moments table.

One limitation is sample size for this time series, 18 monthly cycles’ analysis. However, interventions on measures in healthcare demand short time cycles (hence necessarily yielding fewer data points) for practicality, meaningfulness and usefulness. Despite this shortcoming, the statistical processes applied such as outliers detection, t-test for mean difference, F-test for variances and modeling, all consider the small sample sizes. Seasonality, which usually affects time series, was not detected and even if present, was also considered by modeling.

Obtaining an intervention profile, made possible by multidimensional analysis, allows interventions to be uniquely classified and categorized, enabling informed, comparative and appropriate selective deployment against health measures, thus potentially contributing to outcomes optimization.

The inevitable direction for healthcare is heavy investment in measures outcomes optimization to improve: patient experience; population health; and reduce costs. Interventions are the tools that change outcomes. Creative modeling and applying novel methods for intervention analysis are necessary if healthcare is to achieve this goal. Analytical methods should categorize and rank interventions; probe the measures to improve future selection and adoption; reveal the organic systems’ strengths and shortcomings implementing the interventions for fine-tuning for better performance.

An “intervention moments table” is proposed, created from a multi-axial statistical intervention analysis for organizing, classifying and categorizing interventions. The analysis-set was expanded with additional parameters such as time-to-effect, mean change velocity and effect decay time/doubling time, including transition zone analysis, which produced a unique transitional footprint; and transition velocity. The “intervention moments” should facilitate intervention cross-comparisons, intervention selection and optimal intervention deployment for best outcomes optimization.

In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

This paper aims to identify the relative contribution of the business cycle and structural factors to the development of part-time employment in the 15 Member States of the European Union before the 2004 enlargement (EU-15) over the 1980s and 1990s. To do so, it exploits both cross-sectional and time series variations in available data over the past two decades.

Key results include the business cycle that is found to exert a short-term negative effect on part-time employment developments, although this effect fades away over the two-decade period considered. This finding is consistent with firms utilising part-time employment as a means of adjusting their labour force to economic conditions. Correspondingly, involuntary part-time employment is found to be counter-cyclical, being higher in troughs of economic activity. Splitting our sample reveals a very significant effect of the business cycle on the rate of part-time work for young and male prime-age workers. Conversely, the effect is very weak for women and insignificant for older workers.

Institutions and other structural factors are also found to be significant, longer run determinants of the rate of part-time employment. Changes in legislation affecting part-time employment are found to have a strong and positive impact on part-time employment developments. Moreover, employment protection legislation is positively correlated with the part-time employment rate (PTR), which is consistent with the use of part-time work as a tool for enhancing flexibility in the presence of rigid labour markets. Less robust evidence suggests the presence of unemployment traps for some potential part-time workers. Cross-country evidence also indicates that the lower labour costs borne by firms when employing part-time workers have a large and positive influence on the PTR. Overall, a contribution analysis shows that the main structural and institutional variables generally explain the development in the part-time rate in the EU countries fairly well, while this is obviously not the case in the United States.

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

We investigate the reasons why income inequality is so high in Spain in the EU context. We first show that the differential in inequality with Germany and other countries is driven by inequality among households who participate in the labor market. Then, we conduct an analysis of different household income aggregates. We also decompose the inter-country gap in inequality into characteristics and coefficients effects using regressions of the Recentered Influence Function for the Gini index. Our results show that the higher inequality observed in Spain is largely associated with lower employment rates, higher incidence of self-employment, lower attained education, as well as the recent increase in the immigration of economically active households. However, the prevalence of extended families in Spain contributes to reducing inequality by diversifying income sources, with retirement pensions playing an important role. Finally, by comparing the situations in 2008 and 2012, we separate the direct effects of the Great Recession on employment and unemployment benefits, from other more permanent factors (such as the weak redistributive effect of taxes and family or housing allowances, or the roles of education and the extended family).