Quantitative Hypotheses Research Articles

Gameful learning investigating the impact of game elements, interactivity, and learning style on student success

This study explores the effects of the aspects of game components, interactivity and learning-styles towards the main goal of providing an understanding of the factors that contribute towards the improvement of learning. Structured questionnaires were used to gather quantitative data from participants ranging from college going students to post graduate level students. The outcome of the quantitative organized queries also painted the picture consistent with all the quantitative hypotheses where regression models had been applied. The next level of credibility came from the quantitative results that ranged from mildly to strongly positive and supported by an expert indicating that the latter had improved due to the effects of gamification. Gathering the qualitative data was carried out by semi-structured interviews with six experienced educators. These educators provided deep descriptions and complex opinion on the personal experiences in the learning context that involve gamification. Unlike the quantitative analysis that focused on the overall results of gamification, the thematic analysis unfolded into the qualitative aspects regarding motivation, engagement, and the learner’s journey. Combining these quantitative and more especially qualitative data sets offers an alternating view of the gamification environment in education. The findings of the study do not only contribute to the theoretical body of knowledge in gamification but also offer real-life recommendations for the educators and institutions who are eager to bring changes to the traditional learning processes. Therefore, the study shows that there are no hard-liner advantages of gamification and that it can have positive impacts in various perspectives. It shows how game elements can improve the learning application and motivation and their capacity in dealing with different learning types and achieving better academic results. Additionally, the qualitative element strengthens the role of teachers, when describing the conditions and possibilities of using the gamification concept for actual educational teaching context.

The Báa nnilah Program: Results of a Chronic-Illness Self-Management Cluster Randomized Trial with the Apsáalooke Nation.

Indigenous people in Montana are disproportionately affected by chronic illness (CI), a legacy of settler colonialism. Existing programs addressing CI self-management are not appropriate because they are not consonant with Indigenous cultures in general and the Apsáalooke culture specifically. A research partnership between the Apsáalooke (Crow Nation) non-profit organization Messengers for Health and Montana State University co-developed, implemented, and evaluated a CI self-management program for community members. This article examines qualitative and quantitative program impacts using a pragmatic cluster randomized clinical trial design with intervention and waitlist control arms. The quantitative and qualitative data resulted in different stories on the impact of the Báa nnilah program. Neither of the quantitative hypotheses were supported with one exception. The qualitative data showed substantial positive outcomes across multiple areas. We examine why the data sets led to two very different stories, and provide study strengths and limitations, recommendations, and future directions.

Surrogate infection model predicts optimal alveolar macrophage number for clearance of Aspergillus fumigatus infections

The immune system has to fight off hundreds of microbial invaders every day, such as the human-pathogenic fungus Aspergillus fumigatus. The fungal conidia can reach the lower respiratory tract, swell and form hyphae within six hours causing life-threatening invasive aspergillosis. Invading pathogens are continuously recognized and eliminated by alveolar macrophages (AM). Their number plays an essential role, but remains controversial with measurements varying by a factor greater than ten for the human lung. We here investigate the impact of the AM number on the clearance of A. fumigatus conidia in humans and mice using analytical and numerical modeling approaches. A three-dimensional to-scale hybrid agent-based model (hABM) of the human and murine alveolus allowed us to simulate millions of virtual infection scenarios, and to gain quantitative insights into the infection dynamics for varying AM numbers and infection doses. Since hABM simulations are computationally expensive, we derived and trained an analytical surrogate infection model on the large dataset of numerical simulations. This enables reducing the number of hABM simulations while still providing (i) accurate and immediate predictions on infection progression, (ii) quantitative hypotheses on the infection dynamics under healthy and immunocompromised conditions, and (iii) optimal AM numbers for combating A. fumigatus infections in humans and mice.

COMPASS: Computations for Orientation and Motion Perception in Altered Sensorimotor States.

Reliable perception of self-motion and orientation requires the central nervous system (CNS) to adapt to changing environments, stimuli, and sensory organ function. The proposed computations required of neural systems for this adaptation process remain conceptual, limiting our understanding and ability to quantitatively predict adaptation and mitigate any resulting impairment prior to completing adaptation. Here, we have implemented a computational model of the internal calculations involved in the orientation perception system’s adaptation to changes in the magnitude of gravity. In summary, we propose that the CNS considers parallel, alternative hypotheses of the parameter of interest (in this case, the CNS’s internal estimate of the magnitude of gravity) and uses the associated sensory conflict signals (i.e., difference between sensory measurements and the expectation of them) to sequentially update the posterior probability of each hypothesis using Bayes rule. Over time, an updated central estimate of the internal magnitude of gravity emerges from the posterior probability distribution, which is then used to process sensory information and produce perceptions of self-motion and orientation. We have implemented these hypotheses in a computational model and performed various simulations to demonstrate quantitative model predictions of adaptation of the orientation perception system to changes in the magnitude of gravity, similar to those experienced by astronauts during space exploration missions. These model predictions serve as quantitative hypotheses to inspire future experimental assessments.

Customer captivity, negative word of mouth and well-being: a mixed-methods study

PurposeWhen customers feel that they have no choice but to stay with their current provider to obtain a service that they need, they feel captive. This study aims to investigate customer captivity as a type of vulnerability and evaluate its effects on customers’ service evaluation and word-of-mouth (WOM) behavior, as well as to identify solutions that reduce customers’ feelings of captivity and improve their well-being.Design/methodology/approachThis sequential, quantitative–qualitative, mixed-methods study draws from a survey of 1,017 customers and a qualitative analysis of 20 in-depth semi-structured interviews. Moderated mediation analysis is used to test the quantitative hypotheses; a thematic analysis explores the qualitative data.FindingsThe results of the quantitative study show that captivity emotions and price unfairness perceptions are two manifestations of customer captivity, which directly and indirectly affect service evaluations and WOM behavior. The findings of the qualitative study highlight how captive customers use emotional support-seeking negative word-of-mouth (NWOM) as a solution to reduce their captivity emotions and improve their well-being, by reinforcing their social ties and regaining a sense of control.Research limitations/implicationsThis study advances transformative service research by demonstrating how captivity affects customers’ well-being and customer vulnerability literature by investigating captivity as a type of vulnerability. It contributes to service marketing literature by identifying customer captivity as a boundary condition for generic service evaluation models.Practical implicationsCaptive customers seek emotional support and consequently spread NWOM. Therefore, it is critical for service providers to reduce customers’ captivity feelings and implement adequate solutions to prevent NWOM and decrease the risk of negative impacts on their profitability.Originality/valueAny customer can become vulnerable, due to contextual circumstances. This study focuses on customer captivity as a type of vulnerability and proposes adapted solutions to improve customers’ well-being.

A Psychosocial Perspective about Mental Health and League of Legends in Brazil

Every month, millions of users worldwide play the online digital game League of Legends, which also contains a server dedicated to the Brazilian region. Social oppression by race, skin color, sexual orientation, among others, occurs within the game and is reported constantly. In this paper, we analyzed possible indications of depressive disorder by using an online questionnaire as a basis. We used quantitative and qualitative methods, analyzing the relationship between the interactions and the social identities of the players. We define quantitative hypotheses and qualitative syntheses related to different social factors of the game through the analysis of 604 responses. League of Legends has a negative influence on the mental health of socially peripheral players, and the qualitative analysis exposes specific and widespread cases of oppression and discrimination. We present a discussion on ethics, possible collusion with oppression, and proposals for mitigation or solution.

Preliminary study on quantitative simulation of Danxia landform evolution under the action of tectonic uplift and running water erosion

To better understand the influence of lithology, climate and tectonic features on landform evolution of Danxiashan, the research simulate complex Earth systems with Channel-Hillslope Integrated Landscape Development (CHILD) which embodies quantitative hypotheses about those systems. The model verifies the correctness of the concept of developmental stages and divides it quantitatively. The infancy, accounting for the entire evolution of 3%, develops large tracts of Continuous surface; the youth period accounts for 4% in which emerge a large number of negative landforms; the adult period accounting for 52% has longest evolution and there are many vertical cliffs; the late period accounts for 34% when hilltop gradually rounds; the decline phase emerges a chain of undulating hills, accounting for 7%. In geomorphology, mathematical and numerical models provide a crucial link between stratum, climate and tectonics.

Modelling the consequence of glacier retreat on mixotrophic nanoflagellate bacterivory: a Bayesian approach

Mixotrophic nanoflagellate bacterivory is affected by light; however, in glacially influenced lakes, glacial clay may also interfere with prey uptake. Mechanistic models based on prior quantitative hypotheses and tested with field data are useful for predicting these predator–prey interactions under a changing climate. We modelled the effect of glacial particles on the bacterivory of mixotrophic nanoflagellates, the dominant phytoplankton in mountain lakes in the North Patagonian Andes. Our model equation is based on the classical Michaelis–Menten formulation and Platt's photosynthesis–irradiance curve to account for the interference by glacial particles and the effect of light intensity, respectively. Bayesian inference was applied to estimate model parameters using data from field bacterivory experiments. Light experiments (LE) were performed in lakes with different levels of light penetration without clay particles, while clay experiments (CE) were performed with an experimental gradient of glacial clay concentration at constant light intensity. The LE showed a non‐monotonic effect of light intensity and the CE revealed an exponential negative effect of clay concentration. The obtained model was tested with two independent experiments carried out in a turbid proglacial lake. Our proposed mechanistic model successfully predicted the combined effect of light availability and particle interference on the mixotrophic nanoflagellate–bacteria relationship. The model demonstrated that the effect of light is dampened as clay concentration increases.

Experimental Evolution of Methylobacterium: 15 Years of Planned Experiments and Surprise Findings

Experimental evolution has become an increasingly common approach for studying evolutionary phenomena, as well as uncovering physiological connections in a manner complementary to traditional genetics. Here I describe the development of Methylobacterium as a model system for using experimental evolution to study questions at the intersection of metabolism and evolution. Each experiment was initiated to address a particular question inspired by patterns in natural methylotrophs, such as tradeoffs between single-carbon and multi-carbon growth, or the challenges involved in incorporating novel metabolic pathways or genes with poor codon usage that are acquired via horizontal gene transfer. What I could not have appreciated initially, however, was just how many fortuitous surprise findings would emerge. These have ranged from the repeatability of evolution, complex dynamics within populations, epistasis between beneficial mutations, and even the ability to use simple mathematical models to generate testable, quantitative hypotheses about the fitness landscape.

Mathematical models: An extension of the clinician's mind.

Traditionally, clinicians have used their experience and intuition to diagnose and treat disease states, including neurological disorders. However, the rapid increase in basic knowledge, coupled with a realization that human judgments are often flawed, has made it helpful to approach many clinical disorders by casting them in the form of models (quantitative hypotheses) that can be tested experimentally; in this way the power of the scientific method can be applied. This is especially the case in systems neuroscience, in which the experimental testing of mathematical models has proven an effective approach to understanding a range of clinical problems. Here, we focus on disorders of the neural control of eye movements, which offer many advantages to clinician scientists, providing examples of how thorny clinical mysteries became much clearer once they were formulated as models, and tested experimentally. Such an approach inevitably raises new questions and experimental tests and may suggest novel therapies.

Making our plant modelling community more than the sum of its parts: a personal perspective

The rise of mathematical modelling represents a transition in any scientific area towards quantitative and unequivocal presentation of hypotheses and theory. In Physics, phenomena are now predicted from mathematical models and computer simulations of those models well before observations are made to confirm these predictions. Indeed the largest endeavours in Physics, such as particle physics accelerators and astrophysics observational platforms, are created to test the most profound predictions of such models. Given the huge complexity of living organisms coupled with massive species and even phenotype within genotype diversity, we are far from the same level of advancement, yet need to approach it. Complexity of our systems means that many biological modelling efforts will remain, largely, based on emergent properties and phenomena. Nevertheless, complete models of the full complexity of single-celled organisms are beginning to transition to eukaryotes (Beard et al. 2012; Service 2016). Within narrow areas of gene function, we are already seeing successful projections from gene expression to prediction of growth and development of whole plants (Chew et al. 2014). Modelling provides a framework in which we can precisely organize and test our quantitative knowledge and hypotheses about how a plant process or combination of processes works and then test these against reality. As such, it provides a data-hypothesis-test-learn cycle to improve our understanding of plants and their use. Equally, the rapid growth of high-throughput ‘omics facilities is delivering ever-increasing amounts of data for which our capacity and ability to analyse and interpret lags. Mathematical models coupled with high-performance computing provide a means to deliver this needed acceleration. Simultaneously it should provide the means to predict which data is needed most, so providing feedback and focus for ‘omics approaches. This wealth of data also provides unprecedented opportunities for improving the precision of models by high-speed data to model linkage. Similarly, numerical and text mining knowledge discovery offer much to improving mathematical modelling of plant processes, with opportunities for automated improvement of representation and parameterization (Fer et al. 2018). In parallel, computer simulation of mathematical models has evolved from printouts of numbers to 3D representations of the growth and development of organs, whole plants and even communities of plants that can be indistinguishable from the real thing (Fig. 1). This facilitates identification of emergent phenomena while providing unprecedented opportunities in revolutionizing plant science education (Prusinkiewicz et al. 2007; Prusinkiewicz and Runions 2012; Runions et al. 2017).

Mathematical models for dynamic, multisensory spatial orientation perception.

Mathematical models have been proposed for how the brain interprets sensory information to produce estimates of self-orientation and self-motion. This process, spatial orientation perception, requires dynamically integrating multiple sensory modalities, including visual, vestibular, and somatosensory cues. Here, we review the progress in mathematical modeling of spatial orientation perception, focusing on dynamic multisensory models, and the experimental paradigms in which they have been validated. These models are primarily "black box" or "as if" models for how the brain processes spatial orientation cues. Yet, they have been effective scientifically, in making quantitative hypotheses that can be empirically assessed, and operationally, in investigating aircraft pilot disorientation, for example. The primary family of models considered, the observer model, implements estimation theory approaches, hypothesizing that internal models (i.e., neural systems replicating the behavior/dynamics of physical systems) are used to produce expected sensory measurements. Expected signals are then compared to actual sensory afference, yielding sensory conflict, which is weighted to drive central perceptions of gravity, angular velocity, and translation. This approach effectively predicts a wide range of experimental scenarios using a small set of fixed free parameters. We conclude with limitations and applications of existing mathematical models and important areas of future work.

An exploratory study of subjective well-being in organizations–A mixed method research approach

ABSTRACTSubjective well-being (SWB) has been widely found to have a profound impact on the individual, yet the process of its occurrence in public organizations remains unexplored. So the primary purpose of this paper is to explore the determining factors affecting the process of employee’s subjective well-being in public organizations using mixed methods research. Qualitative analysis using the grounded theory methodology reveals the effective factors that affect the process of employee’s subjective well-being in public organizations. Quantitative hypotheses are then developed based on a qualitative investigation. Survey data were collected from employees of public organizations in one of the provinces of Iran. Analysis of Moment Structure is used for data analysis. The results of the research revealed that both individual and organizational factors (as causal factors) influence the employees’ subjective well-being process (life satisfaction and positive affect). This phenomenon leads to the use of strategies which are affected by intervening and contextual factors. The consequences of this process are to change the attitude and behavior of individuals and improve the efficiency and effectiveness of the organization. This paper is among the first known to examine subjective well-being in public organizations using mixed methods research. The mixed methods approach offers a better insight to understanding effective factors affecting subjective well-being compared to the use of either a qualitative or quantitative method alone.

An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management

PurposeThe Internet of Things (IoT) envisions a global infrastructure of networked physical objects that render radical transparency to supply chain management. Despite the perceived advantages of IoT, industry has still not widely adopted IoT-enabled logistics and supply chain management. The purpose of this paper is to understand the incentives and concerns behind firms’ decisions to adopt IoT, explore the determinant factors affecting IoT adoption in logistics and supply chain management.Design/methodology/approachThis study uses mixed methods research to explore the determinants of IoT adoption intention in logistics and supply chain management. Qualitative analysis using the Grounded Theory methodology reveals the underlying perceptions regarding logistic innovation with IoT. Quantitative hypotheses are then developed based on qualitative investigation and adoption literature. Survey data were collected from the managerial staff of Taiwanese firms across various industries. Structural equation modeling with partial least square is used for data analysis.FindingsThe results of the qualitative study identify uncertainties and issues regarding firms’ intention to accept or reject IoT technology in logistics and supply chain management, including the benefit and cost aspects of adopting IoT, uncertainties about the trustworthiness of IoT technology, and the external motivating force to embrace IoT. The resulting quantitative model shows that perceived benefits, perceived costs, and external pressure are significant determinants of IoT adoption intention, while technology trust is not. However, technology trust does indirectly influence IoT adoption intention through perceived benefits.Practical implicationsThe empirical findings of this study provide some guidelines for logistics and supply chain managers to evaluate IoT adoption in their firms. Likewise, IoT solution providers can also benefit from this study by improving their solutions to mitigate the IoT adoption concerns addressed herein.Originality/valueThis paper is among the first known to examine IoT adoption intention in logistics and supply chain management using mixed methods research. The mixed methods approach offers a better insight in understanding incentives behind firms’ decisions to adopt IoT vs the use of either a qualitative or quantitative method alone.

Crop diversity in homegardens of southwest Uganda and its importance for rural livelihoods

Homegardens are traditional food systems that have been adapted over generations to fit local cultural and ecological conditions. They provide a year-round diversity of nutritious foods for smallholder farming communities in many regions of the tropics and subtropics. In southwestern Uganda, homegardens are the primary source of food, providing a diverse diet for rural marginalized poor. However, national agricultural development plans as well as economic and social pressures threaten the functioning of these homegardens. The implications of these threats are difficult to evaluate, because the structure and functions of the homegardens are not well understood. The aim of the study was to identify patterns and influencing factors in the diversity of homegardens by documenting the floristic diversity and its interactions with spatial, environmental and socio-economic factors. A geographically and socially focused assessment of floristic diversity in 102 randomly selected homegardens in three districts of southwest Uganda was conducted along a deforestation gradient following a human ecology conceptual framework and testing multiple quantitative hypotheses regarding the above mentioned factors. A merged mixed-method approach was followed to provide context and feedback regarding quantitative findings. Results show a high total richness of 209 (mean 26.8 per homegarden) crop species (excluding weeds and ornamentals) dominated by food species, which constituted 96 percent of individuals and 44 percent of all species. Forest-edge homegardens maintained higher plant diversity compared to homegardens in deforested areas and near degraded wetlands. Multiple linear regression models indicated elevation, location, homegarden size, distance to market, additional land ownership (outside the homegarden) and livestock ownership as significant predictors of crop diversity. Cluster analysis of species densities revealed four garden types: ‘diverse tree gardens’, ‘small forest-edge gardens’, ‘large, old, species-rich gardens’, and ‘large, annual-dominated herb gardens’, with 98% correct classification. Location, elevation, and garden size were also important determinants in the cluster assignment. We conclude that the diversity of the studied homegardens may be changing as part of adaptive traditional practices and in response to external drivers. The identified patterns illustrate the importance of homegardens for rural livelihoods and may offer some ways to support farmers to maintain these systems as relevant mechanisms for development in Uganda.

Bioelectric gene and reaction networks: computational modelling of genetic, biochemical and bioelectrical dynamics in pattern regulation.

Gene regulatory networks (GRNs) describe interactions between gene products and transcription factors that control gene expression. In combination with reaction-diffusion models, GRNs have enhanced comprehension of biological pattern formation. However, although it is well known that biological systems exploit an interplay of genetic and physical mechanisms, instructive factors such as transmembrane potential (Vmem) have not been integrated into full GRN models. Here we extend regulatory networks to include bioelectric signalling, developing a novel synthesis: the bioelectricity-integrated gene and reaction (BIGR) network. Using in silico simulations, we highlight the capacity for Vmem to alter steady-state concentrations of key signalling molecules inside and out of cells. We characterize fundamental feedbacks where Vmem both controls, and is in turn regulated by, biochemical signals and thereby demonstrate Vmem homeostatic control, Vmem memory and Vmem controlled state switching. BIGR networks demonstrating hysteresis are identified as a mechanisms through which more complex patterns of stable Vmem spots and stripes, along with correlated concentration patterns, can spontaneously emerge. As further proof of principle, we present and analyse a BIGR network model that mechanistically explains key aspects of the remarkable regenerative powers of creatures such as planarian flatworms. The functional properties of BIGR networks generate the first testable, quantitative hypotheses for biophysical mechanisms underlying the stability and adaptive regulation of anatomical bioelectric pattern.

“The Brain Is the Prisoner of Thought”: A Machine-Learning Assisted Quantitative Narrative Analysis of Literary Metaphors for Use in Neurocognitive Poetics

ABSTRACTTwo main goals of the emerging field of neurocognitive poetics are (a) the use of more natural and ecologically valid stimuli, tasks and contexts and (b) providing methods and models allowing to quantify distinctive features of verbal materials used in such tasks and contexts and their effects on readers responses. A natural key element of poetic language, metaphor, still is understudied insofar as relatively little empirical research looked at literary or poetic metaphors. An exception is Katz et al.’s corpus of 204 literary metaphors by authors such as Shakespeare or Dylan Thomas, for which various rating data are available. We reanalyzed their corpus using a combination of quantitative narrative analysis, latent semantic analysis, and machine learning in order to identify relevant features of the metaphors that influenced the ratings. The combined application of computational tools sheds light on surface and affective-semantic features that co-determine the reception of poetic metaphors and successfully predicted the period of origin (i.e., early vs. late), authorship (e.g., Byron vs. Donne) and goodness ratings of the metaphors. The present results can be used for generating quantitative hypotheses or selecting and matching verbal stimuli in empirical studies of literature and neurocognitive poetics.

Propagation of Reactions in Inhomogeneous Media

AbstractConsider reaction‐diffusion equation ut=Δ u + f(x,u) with and general inhomogeneous ignition reaction f ≥ 0 vanishing at u = 0,1. Typical solutions 0 ≤ u ≤ 1 transition from 0 to 1 as time progresses, and we study them in the region where this transition occurs. Under fairly general qualitative hypotheses on f we show that in dimensions d ≤ 3, the Hausdorff distance of the superlevel sets {u ≥ ε } and {u ≥ 1‐ε} remains uniformly bounded in time for each ε ∊ (0,1). Thus, u remains uniformly in time close to the characteristic function of in the sense of Hausdorff distance of superlevel sets. We also show that each {u ≥ ε} expands with average speed (over any long enough time interval) between the two spreading speeds corresponding to any x‐independent lower and upper bounds on f. On the other hand, these results turn out to be false in dimensions d ≥ 4, at least without further quantitative hypotheses on f. The proof for d ≤ 3 is based on showing that as the solution propagates, small values of u cannot escape far ahead of values close to 1. The proof for d ≥ 4 is via construction of a counterexample for which this fails. Such results were before known for d=1 but are new for general non‐periodic media in dimensions d ≥ 2 (some are also new for homogeneous and periodic media). They extend in a somewhat weaker sense to monostable, bistable, and mixed reaction types, as well as to transitions between general equilibria of the PDE and to solutions not necessarily satisfying . © 2016 Wiley Periodicals, Inc.

Studying long‐term, large‐scale grassland restoration outcomes to improve seeding methods and reveal knowledge gaps

Summary Studies are increasingly investigating effects of large‐scale management activities on grassland restoration outcomes. These studies are providing useful comparisons among currently used management strategies, but not the novel strategies needed to rapidly improve restoration efforts. Here we illustrate how managing restoration projects adaptively can allow promising management innovations to be identified and tested. We studied 327 Great Plains fields seeded after coal mining. We modelled plant responses to management strategies to identify the most effective previously used strategies for constraining weeds and establishing desired plants. Then, we used the model to predict responses to new strategies our analysis identified as potentially more effective. Where established, the weed crested wheatgrass (Agropyron cristatum L.) increased through time, indicating a need to manage establishment of this grass. Seeding particular grasses reduced annual weed cover, and because these grasses appeared to become similarly abundant whether sown at low or high rates, low rates could likely be safely used to reduce seeding costs. More importantly, lower than average grass seed rates increased cover of shrubs, the plants most difficult to restore to many grassland ecosystems. After identifying grass seed rates as a driver, we formulated model predictions for rates below the range managers typically use. These predictions require testing but indicated atypically low grass seed rates would further increase shrubs without hindering long‐term grass stand development. Synthesis and applications. Designing management around empirically based predictions is a logical next step towards improving ecological restoration efforts. Our predictions are that reducing grass seed rates to atypically low levels will boost shrubs without compromising grasses. Because these predictions derive from the fitted model, they represent quantitative hypotheses based on current understanding of the system. Generating data needed to test and update these hypotheses will require monitoring responses to shifts in management, specifically shifts to lower grass seed rates. A paucity of data for confronting hypotheses has been a major sticking point hindering adaptive management of most natural resources, but this need not be the case with degraded grasslands, because ongoing restoration efforts around the globe are providing continuous opportunities to monitor and manage processes regulating grassland restoration outcomes.

Antecedents and outcomes of acceptance of performance appraisal system in Korean non-profit organizations

ABSTRACTThis study identifies the antecedents of non-profit public service motivation (NPSM) and employee outcomes using Korean non-profit acceptance of performance appraisal system (APAS) survey data. We test an NPSM model and examine the concept through public service motivation (PSM) in the non-profit sector. We then apply findings on work motivation, PSM, and performance appraisal systems to quantitative research and test several hypotheses concerning how (1) the non-profit sector’s APAS affects and uniquely shapes non-profit workers’ motivation, (2) NPSM relates to organizational trust, and (3) organizational characteristics affect Korean non-profit employees. We discuss our findings’ implications for non-profit theory and practice.