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Improving the commercial success rate of innovations requires alternative approaches based on social science methodologies for identifying subtle, emerging changes in consumer needs and behaviors. The purpose of this paper is to address this call by proposing trend spotting to guide innovation researchers and service managers towards innovations that are more in accordance with emerging consumer needs.

The authors develop, describe, and employ a methodology for trend spotting to derive eight consumer trends that will have a strong influence on their choices. To provide further insights into these trends, the authors label and describe three customer segments as a function of life-cycle. The goal is to provide a framework for identifying innovations that are of higher value consumers.

The authors identified eight consumer trends, i.e. Always on the go, Always logged-in, Quality information faster, Nowism, Look at me now, Privacy, Sustainable living, and return on time (RoT), present across the three life-stage segments, i.e. Young free and single, Chaos in my life, and Got my life back.

For illustration purpose, the authors elaborate on the trend RoT and employ their findings and framework to illustrate how the airline industry may derive ideas for valuable innovations.

To the best of the authors’ knowledge, this is the first time trend spotting has been employed in the field of service marketing and service innovations.

The purpose of this paper is to examine the time series behavior of Greek labor market series by providing an empirical perspective on trend breaks and unit roots. Trend breaks represent aggregate behavior responses to “infrequent” changes in economic fundamentals, including changes in fiscal or labor market conditions, as have been perceived in Greece during the last years. Unit roots reveal whether “regular” shocks have significant effects on the level of the series over a specified finite horizon.

The authors employ recent procedures that deal with the “circular testing problem” between tests on the parameters of the trend function and unit root tests that often arises in empirical applications. These techniques assess trend function stability and are robust regardless of whether the noise component is stationary or having a unit root. Then, conditional on the presence of breaks, the authors test whether the series can be characterized by a stochastic trend.

The analysis provides evidence of “infrequent” trend breaks that appear to coincide with the recent global economic crisis and the implementation of the counteraction (fiscal) measures to the Greek debt crisis. Allowing for trend breaks does not lead to a rejection of the unit root hypothesis, which might reflect the low flexibility of the country’s labor market operation.

The procedures employed can be viewed as new tools that might help empirical researchers to explore more accurately the characteristics of individual time series and to find reasonable approximations to the true processes of the time series examined.

The paper provides new information on the presence of structural changes in the Greek labor market, and on whether the “aggressive” and “occasional” nature of fiscal measures can be approximated by infrequent changes in the slope of the trend function.

This paper aims to aims to extend emotional labor research by exploring whether the impact of emotional labor on customer satisfaction depends on the order in which different emotional labor strategies are used by employees. Specifically, the authors explore how the order effects of two emotional labor strategies – deep and surface acting – impact customer satisfaction.

The authors conducted two experimental studies in which participants interacted with service employees who systematically switched between surface and deep acting strategies during the service episode. In Study 1, participants watched a video clip depicting a service encounter in a bookstore. In Study 2, participants partook in a simulated career-counseling session.

The four different emotional labor strategy order effects differentially impact customer satisfaction. Consistent with theories of gain–loss effects, improvement and decline trends positively or negatively impact customers, respectively. Furthermore, results show that these trends impact customer satisfaction growth differently over time.

The authors only focused on two emotional labor strategies, and future research may benefit from extending the research to additional regulation strategies and/or specific discrete emotions.

The results suggest that managers may train employees in recognizing that customer satisfaction is not just driven by customers’ overall assessment of the interaction but also by their experience at different stages of the interaction.

Service marketing and management scholars have largely explored emotional labor from a between-person or within-person perspective, with little empirical attention paid to within-episode processes that focus on how employee behavior varies within a single service episode. To the best of the authors’ knowledge, this study is one of the first to demonstrate that surface and deep acting can be used simultaneously and dynamically over the course of a single service interaction in impacting customer satisfaction.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

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.

The role of trend inflation shocks for the U.S. macroeconomic dynamics is investigated by estimating two DSGE models of the business cycle. Policymakers are assumed to be concerned with a time-varying inflation target, which is modeled as a persistent and stochastic process. The identification of trend inflation shocks (as opposed to a number of alternative innovations) is achieved by exploiting the measure of trend inflation recently proposed by Aruoba and Schorfheide (2011). Our main findings point to a substantial contribution of trend inflation shocks for the volatility of inflation and the policy rate. Such contribution is found to be time dependent and highest during the mid-1970s to mid-1980s.

In large parts of the world, income inequality has been rising in recent decades. Other regions have experienced declining trends in income inequality. This raises the question of which mechanisms underlie contrasting observed trends in income inequality around the globe. To address this research question in an empirical analysis at the aggregate level, we examine a global sample of 73 countries between 1981 and 2010, studying a broad set of drivers to investigate their interaction and influence on income inequality. Within this broad approach, we are interested in the heterogeneity of income inequality determinants across world regions and along the income distribution. Our findings indicate the existence of a small set of systematic drivers across the global sample of countries. Declining labour income shares and increasing imports from high-income countries significantly contribute to increasing income inequality, while taxation and imports from low-income countries exert countervailing effects. Our study reveals the region-specific impacts of technological change, financial globalisation, domestic financial deepening and public social spending. Most importantly, we do not find systematic evidence of education’s equalising effect across high- and low-income countries. Our results are largely robust to changing the underlying sources of income Ginis, but looking at different segments of income distribution reveals heterogeneous effects.

The purpose of this paper is to re‐examine the effect of population ageing on private saving, taking into account the fact that ageing is brought about by not only rising old‐aged dependency but also expanding longevity.

The study uses panel data of 22 OECD countries from 1961 to 2010. Linear and non‐linear panel regression methods are used. The study takes into account the time series characteristic of the data, such as the deterministic trend present in old‐age dependency ratio.

Longevity consistently has a significant positive impact on savings, while old‐aged dependency rate has no discernible impact once country‐specific time trends in the data are accounted for. The general finding within the literature where old‐age dependency exerts a negative impact on savings is sensitive to the manner in which the data is handled and/or the sample selected.

First, the authors jointly consider rising old‐aged dependency and expanding longevity on savings, thus avoiding potential omitted variable bias in previous studies. Second, they explore non‐linearity in the savings‐ageing relationship which was ignored previously. Third, they identify whether saving rate and demographic measures are sharing common stochastic trends or driven by individual deterministic trends to avoid spurious regression results.

Measurement of diminishing or divergent cross section dispersion in a panel plays an important role in the assessment of convergence or divergence over time in key economic indicators. Econometric methods, known as weak σ-convergence tests, have recently been developed (Kong, Phillips, & Sul, 2019) to evaluate such trends in dispersion in panel data using simple linear trend regressions. To achieve generality in applications, these tests rely on heteroskedastic and autocorrelation consistent (HAC) variance estimates. The present chapter examines the behavior of these convergence tests when heteroskedastic and autocorrelation robust (HAR) variance estimates using fixed-b methods are employed instead of HAC estimates. Asymptotic theory for both HAC and HAR convergence tests is derived and numerical simulations are used to assess performance in null (no convergence) and alternative (convergence) cases. While the use of HAR statistics tends to reduce size distortion, as has been found in earlier analytic and numerical research, use of HAR estimates in nonparametric standardization leads to significant power differences asymptotically, which are reflected in finite sample performance in numerical exercises. The explanation is that weak σ-convergence tests rely on intentionally misspecified linear trend regression formulations of unknown trend decay functions that model convergence behavior rather than regressions with correctly specified trend decay functions. Some new results on the use of HAR inference with trending regressors are derived and an empirical application to assess diminishing variation in US State unemployment rates is included.