Hubungan antara Kualitas Layanan dan Harga dengan Kepuasan Konsumen Online Shopping pada Mahasiswi Universitas Surabaya

Toko online merupakan toko berbasis internet yang cara pemasarannya menggunakan website. Saat ini banyak masyarakat yang sudah mengenal toko online, toko tersebut menjadi salah satu alternatif transaksi jual beli, tak terkecuali mahasiswi Universitas Surabaya. Toko online memiliki kelebihan dalam hal transaksi pembelian, semua transaksi dilakukan melalui media. Akan tetapi konsumen yang ingin membeli produk online, hanya bisa melihat gambar saja melalui media, tidak bisa merasakan langsung produk yang diinginkan. Hal ini bertolak belakang dengan toko tradisional yang dapat melihat dan merasakan produk. Oleh karena itu, melihat maraknya jual beli online, konsumen dihadapkan pada dua sisi. Maka penelitian ini bertujuan untuk melihat hubungan layanan dan harga dengan kepuasan konsumen online shopping pada mahasiswi Universitas Surabaya. Kepuasan konsumen adalah kesesuaian antara harapan konsumen dengan produk yang sebenarnya. Penelitian ini merupakan penelitian uji hubungan yang melibatkan 200 subjek mahasiswi Universitas Surabaya yang berusia 19 – 22 tahun dan pernah melakukan pembelian online paling sedikit dua kali. Pengambilan sampel pada penelitian ini menggunakan teknik incidental sampling. Hasil penelitian ini menunjukkan terdapat hubungan antara kualitas layanan dengan kepuasan konsumen. Saran untuk penelitian selanjutnya adalah jangan membatasi subjek online dengan kriteria paling sedikit dua kali pembelian, hal ini memicu penyebaran angket penelitian yang kurang merata. Sementara untuk konsumen online jika ingin mendapatkan produk online dan tidak mengeluarkan banyak biaya, hendak membeli di jejaring sosial

INVESTMENT IN ANTIVIRAL DRUGS:A REAL OPTIONS APPROACH

Real options analysis is a promising approach to model investment under uncertainty. We employ this approach to value stockpiling of antiviral drugs as a precautionary measure against a possible influenza pandemic. Modifications of the real options approach to include risk attitude and deviations from expected utility are presented. We show that risk aversion counteracts the tendency to delay investment for this case of precautionary investment, which is in contrast to earlier applications of risk aversion to real options analysis. Moreover, we provide a numerical example using real world data and discuss the implications of real options analysis for health policy. Suggestions for further extensions of the model and a comparison with the expected value of information analysis are put forward. Copyright (C) 2009 John Wiley & Sons, Ltd

Microglia in Alzheimer's disease: a role for ion channels

Alzheimer's disease is the most common form of dementia, it is estimated to affect over 40 million people worldwide. Classically, the disease has been characterized by the neuropathological hallmarks of aggregated extracellular amyloid-β and intracellular paired helical filaments of hyperphosphorylated tau. A wealth of evidence indicates a pivotal role for the innate immune system, such as microglia, and inflammation in the pathology of Alzheimer's disease. The over production and aggregation of Alzheimer's associated proteins results in chronic inflammation and disrupts microglial clearance of these depositions. Despite being non-excitable, microglia express a diverse array of ion channels which shape their physiological functions. In support of this, there is a growing body of evidence pointing to the involvement of microglial ion channels contributing to neurodegenerative diseases such as Alzheimer's disease. In this review, we discuss the evidence for an array of microglia ion channels and their importance in modulating microglial homeostasis and how this process could be disrupted in Alzheimer's disease. One promising avenue for assessing the role that microglia play in the initiation and progression of Alzheimer's disease is through using induced pluripotent stem cell derived microglia. Here, we examine what is already understood in terms of the molecular underpinnings of inflammation in Alzheimer's disease, and the utility that inducible pluripotent stem cell derived microglia may have to advance this knowledge. We outline the variability that occurs between the use of animal and human models with regards to the importance of microglial ion channels in generating a relevant functional model of brain inflammation. Overcoming these hurdles will be pivotal in order to develop new drug targets and progress our understanding of the pathological mechanisms involved in Alzheimer's disease

Efficacy of a Local Isolate of Metarhizium pinghaense Against Females of Margarodes prieskaensis (Homoptera: Coccoidea) in Field Trials

The indigenous soil-dwelling scale insect, Margarodes prieskaensis, can severely damage and even kill grapevines in the northern grape-growing regions of South Africa. There are no registered means of control, and soil applications of insecticides raise environmental concerns. Using local isolates of entomopathogenic fungi (EPF) that are better adapted to local conditions to target female margarodes could add a valuable biocontrol component to an integrated management strategy. The objective of this research was to evaluate the efficacy of a local isolate of Metarhizium pinghaense against M. prieskaensis females under field conditions. Dry conidia suspended in water and 0.05% v/v Tween 20, applied as a soil drench, achieved 19.1% and 17.7% infection of margarode females in the Northern Cape and Limpopo, respectively, in 2021. Conidia stored in canola oil, suspended in water and 0.05% v/v Tween 20 and applied as a soil drench achieved infection rates of 38.5% and 62.8%, respectively, at the same sites in 2022. These results confirm the importance of formulating conidia for protection against adverse environmental conditions to improve EPF efficacy in the field. This study is the first to demonstrate the efficacy of M. pinghaense against margarode females at the soil surface and confirms the potential of this EPF for the biological control of margarodes

Evaluating the Validation Process:Embracing Complexity and Transparency in Health Economic Modelling

Reimbursement decisions and price negotiation of healthcare interventions often rely on health economic model results. Such decisions affect resource allocation, patient outcomes and future healthcare choices. To ensure optimal decisions, assessing the validity of health economic models may be crucial. Validation involves much more than identifying (and hopefully correcting) errors in the model implementation. It also includes assessing the conceptual validity of the model and validation of the model input data, and checking whether the model’s predictions align sufficiently well with real-world data. In the context of health economics, validation can be defined as “the act of evaluating whether a model is a proper and sufficient representation of the system it is intended to represent in view of an application”, meaning that the model complies with what is known about the system and its outcomes provide a robust basis for decision making.[...]Validation of health economic models should be seen as a critical component of evidence-based decision making in healthcare. However, as of today, it still faces several important challenges, including the lack of consensus guidance and standardised procedures, the need for greater rigour or the question of who should oversee the validation process. To address these challenges, we encourage model developers, agencies requiring models for their decision making and editors of journals that publish models to recommend the use of state-of-the-art tools for reporting (and conducting) validations of health economic models, such as those mentioned in this editorial

Evaluating the Validation Process:Embracing Complexity and Transparency in Health Economic Modelling

Reimbursement decisions and price negotiation of healthcare interventions often rely on health economic model results. Such decisions affect resource allocation, patient outcomes and future healthcare choices. To ensure optimal decisions, assessing the validity of health economic models may be crucial. Validation involves much more than identifying (and hopefully correcting) errors in the model implementation. It also includes assessing the conceptual validity of the model and validation of the model input data, and checking whether the model’s predictions align sufficiently well with real-world data. In the context of health economics, validation can be defined as “the act of evaluating whether a model is a proper and sufficient representation of the system it is intended to represent in view of an application”, meaning that the model complies with what is known about the system and its outcomes provide a robust basis for decision making.[...]Validation of health economic models should be seen as a critical component of evidence-based decision making in healthcare. However, as of today, it still faces several important challenges, including the lack of consensus guidance and standardised procedures, the need for greater rigour or the question of who should oversee the validation process. To address these challenges, we encourage model developers, agencies requiring models for their decision making and editors of journals that publish models to recommend the use of state-of-the-art tools for reporting (and conducting) validations of health economic models, such as those mentioned in this editorial

Mapping Chronic Disease Prevalence based on Medication Use and Socio-demographic variables: an Application of LASSO in healthcare in the Netherlands

BACKGROUND: Policymakers generally lack sufficiently detailed health information to develop localized health policy plans. Chronic disease prevalence mapping is difficult as accurate direct sources are often lacking. Improvement is possible by adding extra information such as medication use and demographic information to identify disease. The aim of the current study was to obtain small geographic area prevalence estimates for four common chronic diseases by modelling based on medication use and socio-economic variables and next to investigate regional patterns of disease. METHODS: Administrative hospital records and general practitioner registry data were linked to medication use and socio-economic characteristics. The training set (n = 707,021) contained GP diagnosis and/or hospital admission diagnosis as the standard for disease prevalence. For the entire Dutch population (n = 16,777,888), all information except GP diagnosis and hospital admission was available. LASSO regression models for binary outcomes were used to select variables strongly associated with disease. Dutch municipality (non-)standardized prevalence estimates for stroke, CHD, COPD and diabetes were then based on averages of predicted probabilities for each individual inhabitant. RESULTS: Adding medication use data as a predictor substantially improved model performance. Estimates at the municipality level performed best for diabetes with a weighted percentage error (WPE) of 6.8%, and worst for COPD (WPE 14.5%)Disease prevalence showed clear regional patterns, also after standardization for age. CONCLUSION: Adding medication use as an indicator of disease prevalence next to socio-economic variables substantially improved estimates at the municipality level. The resulting individual disease probabilities could be aggregated into any desired regional level and provide a useful tool to identify regional patterns and inform local policy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10754-4

Dynamic effects of smoking cessation on disease incidence, mortality and quality of life: The role of time since cessation

<p>Abstract</p> <p>Background</p> <p>To support health policy makers in setting priorities, quantifying the potential effects of tobacco control on the burden of disease is useful. However, smoking is related to a variety of diseases and the dynamic effects of smoking cessation on the incidence of these diseases differ. Furthermore, many people who quit smoking relapse, most of them within a relatively short period.</p> <p>Methods</p> <p>In this paper, a method is presented for calculating the effects of smoking cessation interventions on disease incidence that allows to deal with relapse and the effect of time since quitting. A simulation model is described that links smoking to the incidence of 14 smoking related diseases. To demonstrate the model, health effects are estimated of two interventions in which part of current smokers in the Netherlands quits smoking.</p> <p>To illustrate the advantages of the model its results are compared with those of two simpler versions of the model. In one version we assumed no relapse after quitting and equal incidence rates for all former smokers. In the second version, incidence rates depend on time since cessation, but we assumed still no relapse after quitting.</p> <p>Results</p> <p>Not taking into account time since smoking cessation on disease incidence rates results in biased estimates of the effects of interventions. The immediate public health effects are overestimated, since the health risk of quitters immediately drops to the mean level of all former smokers. However, the long-term public health effects are underestimated since after longer periods of time the effects of past smoking disappear and so surviving quitters start to resemble never smokers. On balance, total health gains of smoking cessation are underestimated if one does not account for the effect of time since cessation on disease incidence rates. Not taking into account relapse of quitters overestimates health gains substantially.</p> <p>Conclusion</p> <p>The results show that simulation models are sensitive to assumptions made in specifying the model. The model should be specified carefully in accordance with the questions it is supposed to answer. If the aim of the model is to estimate effects of smoking cessation interventions on mortality and morbidity, one should include relapse of quitters and dependency on time since cessation of incidence rates of smoking-related chronic diseases. A drawback of such models is that data requirements are extensive.</p