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A paradigm shift toward a corporate model of higher educational settings has led to complex and excess work demands, yet the potential long-run ramifications of work overload are still under-examined. Building the arguments on the “spiral of resource loss” corollary of the conservation of resources (COR) theory, the authors have bridged this gap by testing how work overload spills over into career resilience via reduced harmonious passion. In addition, the authors compare how the employees having standardized workloads differ in their harmonious passion and career resilience from those having excessive (non-standardized) workloads.

Through a longitudinal natural field experiment of 402 faculty members [N = 198 in the standardized group (optimal load) and N = 204 in the non-standardized group (overload)] working in higher educational institutions of Pakistan, data were collected in three waves (each six months apart). The group comparison, trend analysis and longitudinal mediation analysis done through SPSS and MPlus affirmed the hypothesized associations.

The results have shown that work overload impacts career resilience through the mediating role of harmonious passion. The faculty members in the standardized workload had more passion and career resilience as compared to the non-standardized workload group. In addition, these impacts intensified overtime for the overloaded faculty members while faculty members with optimal workload sustained their passion and resilience for the teaching profession.

Taking the COR perspective, this study sheds light on how faculty members' work overloads reduce their capability to retain their passion and resilience for teaching from a longitudinal and experimental perspective.

This paper aims to explore why consumers absorb ethical habits into their daily consumption, despite having little interest or understanding of the ethics they are buying into, by looking at the motivation behind mainstream ethical consumption.

Fifty in-depth field interviews at point of purchase capture actual ethical consumption behavior, tied with a progressive-laddering interview technique yields over 400 consumption units of analysis.

Ethical attitudes, values and rational information processing have limited veracity for mainstream ethical consumption. Habit and constrained choice, as well as self-gratification, peer influence and an interpretivist understanding of what ethics are being purchased provide the primary drivers for consumption.

Use of qualitative sampling and analysis limits the generalizability of this paper. However, the quantitative representation of data demonstrates the strength with which motivations were perceived to influence consumption choice.

Ethical brands which focus on explicit altruistic ethical messaging at the expense of hedonistic messaging, or ambiguous pseudo ethics-as-quality messaging, limit their appeal to mainstream consumers. Retailers, however, benefit from the halo effect of ethical brands in store.

The paper highlights the importance of retailer engagement with ethical products as a precursor to normalizing ethical consumption, and the importance of normative messaging in changing habits.

The paper provides original robust critique of the current field of ethical consumption and an insight into new theoretical themes of urgent general interest to the field.

An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

The use of behavioral insights and experimental methods has recently gained momentum among health policy-makers. There is a tendency, however, to reduce behavioral insights applications in health to “nudges,” and to reduce experiments in health to “randomized controlled trials” (RCTs). We argue that there is much more to behavioral insights and experimental methods in health economics than just nudges and RCTs. First, there is a broad and rich array of complementary experimental methods spanning the lab to the field, and all of them could prove useful in health economics. Second, there are a host of challenges in health economics, policy, and management where the application of behavioral insights and experimental methods is timely and highly promising. We illustrate this point by describing applications of experimental methods and behavioral insights to one specific topic of fundamental relevance for health research and policy: the experimental elicitation and econometric estimation of risk and time preferences. We start by reviewing the main methods of measuring risk and time preferences in health. We then focus on the “behavioral econometrics” approach to jointly elicit and estimate risk and time preferences, and we illustrate its state-of-the-art applications to health.

We discuss how experimental analysis can be integrated into strategic human resources management (SHRM) research in Africa so as to develop theory and value principles to guide executives.

The model we propose – experiment-based SHRM – is predicated on the use of experimental approaches to demonstrate the value of SHRM and to derive principles that guide research and practice in Africa.

We illustrate how scholars can conduct experiments from an SHRM perspective.

We discuss the strengths and limitations of the model and suggest ways of maximizing its potential.

The technique is a resource for scholars of SHRM in Africa. They can use it to supplement other approaches for studying SHRM.

This chapter discusses a typology of experimental analysis. The lack of such a typology in the context of Africa makes it a valuable contribution. Thus, it fills a contextual gap in the SHRM research methodology literature. It can therefore help graduate students and junior faculty improve their research.

This chapter describes methodological issues that are relevant for research on recovery. We aim to provide an overview of methodological approaches that have been or can be used in recovery research, and to provide methodological guidelines that researchers may use in assessing the process of recovery. We argue that studies on recovery must be explicit about recovery settings, recovery processes (i.e., activities and experiences) and recovery outcomes. We describe typical operationalizations of these three perspectives and focus in more detail on potential measures of recovery outcomes. We give an overview of research designs including experiments and quasi-experiments, diary studies, and longitudinal field studies. We conclude by pointing to remaining challenges for researchers in the area of recovery.

The purpose of this paper is to investigate the roles that employee-initiated Lean improvement projects play in health care. Lean ideas are introduced to improve flow in health care. Although variability is detrimental to flow performance, it is unclear whether Lean initiatives set out to reduce this variability and the associated buffers.

Longitudinal field research is combined with an exploratory field-quasi-experiment. First, a large set of Lean interventions were explored and their focus classified. Semi-structured interviews with practitioners supported the initial findings regarding the focus. Second, this study investigated whether a knowledge deficiency could explain the identified focus through a quasi-experiment in which the authors’ stimulated knowledge on the roles of variability and buffers and then classified subsequent interventions.

The results reflected a narrow application of Lean, with most interventions directed at reducing direct waste. A quasi-experiment demonstrated that a small investment in knowledge enables the focus to shift toward buffers and variability issues – i.e. toward a more complete Lean approach.

This research supports the commonly held view that there is a tendency to focus on waste. Furthermore, a lengthy experience of Lean does not guarantee interventions will focus on buffers and variability, issues with arguably a higher complexity compared to obvious waste. However, small investments in knowledge can broaden the focus of practitioners’ interventions.

This study is one of the first to research the focus of Lean interventions through a data set spanning several years. The results are based on a unique data set covering a large number of documented Lean interventions.

The purpose of this paper is to assess the benefits of integrating IT tracking and routing systems into last-mile distribution operations. The paper also demonstrates the role of field experiments as a valid approach for improving the rigour of logistics research.

The study employs a field experiment approach. Data were collected before and after the experimental treatment from 16 participating vehicles, which were used as inputs and outputs to calculate vehicles' efficiencies using data envelopment analysis.

Through employing manipulation and random assignment to investigate causality in naturally occurring contexts, the study results show statistical evidence for the role of vehicle tracking and routing systems in enhancing fleet efficiency. Furthermore, results show that field experiment is an appropriate method for capital budgeting of deploying IT systems in the distribution function.

Distribution managers can use a field experiment setup to assess the potential impact of installing IT solutions prior to large-scale implementation or prior to purchasing.

The study fills a gap in the literature through the application of a field experiment approach to establish causality relationships in distribution and logistics research. This study should encourage new research on the role of field experimentation in evaluating the benefits gained from, and the capital budgeting of, the modern disruptive technologies in supply chains.

While qualitative work has a long tradition in the strategy field and has recently regained popularity, we have not paused to take stock of how such work offers contributions. We address this oversight with a review of qualitative studies of strategy published in five top-tier journals over an extended period of 15 years (2003–2017). In an attempt to organize the field, we develop an empirically grounded organizing framework. We identify 12 designs that are evident in the literature, or “designs-in-use” as we call them. Acknowledging important similarities and differences between the various approaches to qualitative strategy research (QSR), we group these designs into three “families” based on their philosophical orientation. We use these designs and families to identify trends in QSR. We then engage those trends to orient the future development of qualitative methods in the strategy field.

This paper aims to set up a natural experiment as action research and to develop a framework of cognitive distance of informants to improve the initiation of service encounter-based innovation.

Natural experiment as action research in one Scandinavian case company is used. This paper includes a longitudinal study over 10 month’s duration.

An organisational support system and an improved front-office innovation climate were set up which generated a new system for information gleaning. A framework of cognitive distance was induced from transcriptions of interviews and transcriptions.

Only one Scandinavian company and a limited number of informants were activated. Also, the time period only included the initiation phase of service encounter-based innovation.

Three different strategies are suggested to respond to different types of word-of-business.

Cognitive distance is a generic concept which relates to all human interaction and communication. It can explain why it is so difficult to “get things across”.

This paper presents a new theory in an emerging innovation field, open/user-driven innovation. Theory from business marketing, service encounter and innovation is also used.