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Traditional microbiological methods to monitor the growth or survival of microbes are very labour‐intensive and rather expensive and the knowledge acquired is not cumulative. Predictive microbiology as an alternative approach has been developed utilizing mathematical models to predict the microbial inactivation, survival or growth during food processing. The purpose of this paper is to review the evolutions and limitations of primary mathematical models in predictive microbiology.

Primary models deal with the variation of microbial populations against time under particular environmental and cultural conditions. According to the behaviour of micro organisms during food processing and storage, primary models can be divided into inactivation/survival models and growth models. Literature is reviewed to assess the performance of these mathematical models.

In order to predict microbial survival or growth curves, some empirical mathematical models have been used. Most of them have no or little microbiological or physiological basis, which make the interpretation of some model parameters difficult and their performances do not match observed microbiological outcomes. To produce a more accurate mathematical model, more mechanisms are necessary to interpret model parameters with a biological basis.

The paper reviews the evolution and limitations of primary mathematical models, which may help future model development.

This paper aims to present a forecast for renewable energy production in the USA. Growth curves are used to conduct the forecasts.

The analysis is based upon a literature review, supplemented by collection of secondary data. The study then focuses on applying the Pearl growth curve.

The authors' results show that biomass energy production is growing the fastest followed by geothermal and wind. Additionally, the forecast for solar energy production shows little to no growth over the next two decades.

If the US government hopes to achieve its goals in renewable energy, considerable funding and incentives will have to be put forth to accelerate the growth of renewable energy. Since the biomass technology is already growing nicely it makes sense to put the additional resources behind the other three technologies to close the 10.3 percent gap being forecasted. The government also needs to put more funding into dual renewable plants such as wind or solar combines with pumped hydro, this will ensure environmental and reliability are both maintained. Finally, for renewable energies to be competitive in the long term, considerable research needs to go into driving down the cost so there is not a need for subsidies.

This study provides value in providing a forecast for expected future growth for renewable energy sources.

Motivated with the ever growing number of bibliometric trend extrapolation studies, the purpose of this paper is to demonstrate through two technologies how the selection of an upper limit of growth affects the correlation and causality of technology development measured with bibliometric data.

The paper uses Gompertz and Fisher-Pry curves to model the technological development of white light emitting diodes and flash memory, and show with extrapolation results from several bibliometric sources how a typical bias is caused in trend extrapolations.

The paper shows how drastic an effect the decision to set an upper bound has on trend extrapolations, to be used as a reference for applications. The paper recommends carefully examining the interconnection of actual development and bibliometric activity.

Despite increasing interest in modelling technological data using this method, reports rarely discuss basic assumptions and their effects on outcomes. Since trend extrapolations are applied more widely in different disciplines, the basic limitations of methods should be explicitly expressed.

This paper aims to introduce latent growth curve modeling (LGCM) as a statistical technique to analyze repeated measures of longitudinal data to researchers in hospitality management.

First, the basics and extensions of LGCM are explained. Second, this paper reviews three existing empirical hospitality research studies that could have benefitted from LGCM but did not use this methodology. Third, this paper provides an overview of two specific illustrative examples of how the current authors have already used LGCM for hospitality research.

Based on explaining the basics of LGCM, delineating two examples using LGCM method and presenting new research avenues that would use LGCM to advance theoretical knowledge, this paper shows how LGCM represents a leap forward in the promotion of more rigorous research in hospitality management.

This paper is the first in hospitality to call for research based on LGCM and provide hands-on demonstrations and an agenda for this methodology.

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 article reviews the extensive history of dynamic performance research, with the goal of providing a clear picture of where the field has been, where it is now, and where it needs to go. Past research has established that job performance does indeed change, but the implications of this dynamism and the predictability of performance trends remain unresolved. Theories are available to help explain dynamic performance, and although far from providing an unambiguous understanding of the phenomenon, they offer direction for future theoretical development. Dynamic performance research does suffer from a number of methodological difficulties, but new techniques have emerged that present even more opportunities to advance knowledge in this area. From this review, I propose research questions to bridge the theoretical and methodological gaps of this area. Answering these questions can advance both research involving job performance prediction and our understanding of the effects of human resource interventions.

Addressing occupational stress and fostering employee wellness helps meet a host of organizational stakeholder expectations including high quality of work life (employees), reasonable return on investment (investors), increased productivity (management), and competitiveness (owners). Despite being dynamic in nature, stress and wellness are often studied using a static perspective. One reason for the scarcity of dynamic empirical research is the limited knowledge and use of the tools available to assess change over time. To address this limitation, four tools used to assess change and dynamics of occupational stress and well-being are described: growth models, latent change score models, spectral analysis, and computational modeling. First, we begin by discussing growth curve models and then transition to latent change score models. We then expand into spectral analysis, a tool used to determine cycles of ups and downs that repeat regularly. Last, computational modeling is discussed, where computers and simulations are used to understand a dynamic process. For each tool, we give examples of how they have been used, make recommendations for future use, and provide readers with suggestions and references for how to complete analyses in software and programs, most of which are freely available (i.e., R, Vensim).

Purpose: The death of a child can elicit enduring and intense parental grief. Additionally, as parents are both confronted with the loss of their child, interpersonal processes come into play. This study aimed to examine the change in reported levels of grief among bereaved parents individually and at a couple-level. The authors examined the differences in grief trajectories between mothers and fathers and whether the reported level of grief of one partner predicts the other partner’s reported level of grief.

Design/methodology/approach: Our longitudinal study included 229 bereaved couples who completed the Inventory of Complicated Grief at 6, 13, and 20 months post-loss.

Findings: A latent growth curve analysis showed that parents reported consistently high average grief levels, mothers reported higher grief levels than fathers, and all parents reported a similar small decline in grief. A cross-lagged panel analysis showed that the grief of one parent affected the grief of the other parent with similar strength. Our results held regardless of the child’s gender and age, but an expected loss was associated with a lower grief level 6 months post-loss and a smaller decline in reported levels of grief.

Originality/value: These findings highlight bereaved parents as a particularly vulnerable population, increase our understanding of change in parental grief over time and of the interdependence of grieving in bereaved couples.

In this paper, an analysis is presented of the research funding towards nanotechnology at the National Nanotechnology Initiative (NNI) and its relationship to the research output in Nanoscope, an application area of nanotechnology.

The paper analyzes the data collected from 1997 till 2006 and derives a definitive time lag between the allocation of research funds and issued patents and published journals. This assessment is achieved by identifying growth trends in patents, funds and publications and doing a curve‐fit analysis using the Fisher‐Pry model. Linear regression analysis is used to show the correlation between the funding and research outputs. Alongside, non‐linear programming objective function optimization technique is used to derive the time lag in years for each of the research outputs from the year of funds granted.

This paper demonstrated that there is a strong correlation between research funding and different research outputs. The time lag between funding and patents issued is evident from the patent trend analysis and Bibliometric analysis. In the case of Nanoscope, the patent time lag was found to be approximately five to six years, for journal article it was approximately two to three years and conference presentations happened right after the funding. The research outputs showed similar trends and were found to be interdependent as evident from our mathematical analysis.

While this study has shown that lag times exist within the chosen example of Nanoscope, and furthermore can be calculated to a precise degree, further data points in terms of additional emerging technologies would support the hypothesis in a more general term. A future study can look at developing technology roadmaps of the future based on the funding happening today.

The work takes bibliometric analysis to a further intelligence and establishes key linkages between these indicators.

Over the last several decades, businesses have faced mounting pressures from diverse stakeholders to alter their corporate operations to become more socially and environmentally responsible. In turn, many firms appear to have responded by implementing more sustainable practices — measuring, documenting, and publishing annual CSR or sustainability reports to showcase how they are addressing important issues in this area, including: resource stewardship, waste management, greenhouse gas emission reductions, fair and safe labor practices, amongst other stakeholder concerns. And yet, research in this domain has not yet systematically examined whether businesses have, on the whole, changed their practices in tandem with the important changes in its institutional context over time. Have corporate CSR initiatives, in fact, been growing over the last 25 years or has the increased attention to CSR actually been much ado about nothing? In this chapter, we review the empirical literature on CSR to uncover that common measures of CSR such as the KLD do not support the concept that CSR practices have increased substantively over the last 25 years. We supplement this historical review by modeling the growth curves of CSR implementation in practice and find that the pace of positive change has indeed been glacial. More alarmingly, we also look at corporate social irresponsibility (CSiR) and find that, contrary to expectations, businesses have become more, not less, irresponsible during this same time period. Implications of these findings for theory are presented as are suggestions for future research in this domain.