Perceived quality in the context of e-commerce was defined and examined in numerous studies, but, to date, there are no consistent definitions and measurement scales. Instruments that measure quality in e-commerce industries primarily focus on website quality or service quality during the transaction and delivery phases. Even though some scholars have proposed instruments from different perspectives, these scales do not fully evaluate the level of quality perceived by customers during the entire decision-making process. This dissertation purports to provide five main contributions for the e-commerce, service quality, and decision science literature: (1) development of a comprehensive instrument to measure how online customers perceive the quality of the shopping channel, website, transaction and recovery based on the customer decision making process; (2) identification of the determinants of customer satisfaction and the key dimensions of customer behavioral intentions in e-commerce; (3) examination of the relationships among perceived quality, customer satisfaction and loyalty intention using empirical data; (4) application of different statistical packages (LISREL and PLS-Graph) for data analysis and comparison of how these methods impact the results; and (5) examination of the moderating effects of control variables. A survey was designed and distributed to a total of 1126 college students in a large southwestern university in the U.S. Exploratory factor analysis, confirmatory factor analysis, and structural equation modeling with both LISREL and PLS-Graph are used to validate the comprehensive instrument and test the research hypotheses. The results provide theoretical and normative guidelines for researchers and practitioners in the e-commerce domain. The research results will also help e-commerce platform providers or e-retailers to improve their business and marketing strategies by providing a better understanding of the most important factors influencing customer behavioral intentions.

Although there is research about the use of enterprise resource planning (ERP) from a management perspective, the research is not clear as to whether the ERP benefits justify the costs, not only in dollars, but also in effort, from the end user's perspective. Using the theory of diffusion of innovation (DOI), the purpose of this quantitative research was to identify the set of postimplementation sustainability factors that maximized ERP user value, which are major issues for management, and measured their relative significance. The study's structural model incorporated the technology-organization-environment (TOE) framework, which is a conceptualization of the theory of diffusion of innovation, to predict the postimplementation sustainability factors from the ERP user's point of view. The partial least squares structure equation modeling (PLS-SEM) approach provided the needed explanatory analysis to test the predictive power of the structural model. The target population was organizational employees who had used an operational ERP system for at least 4 years in the state of Colorado. A convenience sample of 163 cases responded to the online questionnaire. Hypotheses testing indicated that the independent variables of ERP information quality, ERP system quality, ERP knowledge and learning, shared beliefs, job relevance, and coordination significantly impacted the dependent variable ERP user value. The positive social change implications of this study include a better understanding of ERP postimplementation sustainability factors from the users' perspectives and their social impact on organizational performance, which could lead to increased employee effectiveness, productivity, efficiency, and individual satisfaction due to ERP usage.

本研究的目的在於結合部份計分模式(partial credit model, PCM)與模糊集合論(fuzzy set theory)，提出評定量表的不同計分方式：模糊部份計分法(fuzzy partial credit scaling, FPCS)。FPCS是根據 PCM 所估計出的梯度參數(step parameters)來建構三角形模糊數，三角形模糊數代表選擇某個特定選項的受試者的能力分配情形。接著，利用中心法(center of gravity method) 將三角形模糊數解模糊化為純量。最後，利用隸屬度當作權重，計算個別受試者的模糊觀察分數，並且用模糊觀察分數當作量表的總分。 本研究採用貝克憂鬱量表(Beck Depression Inventory-II, BDI)中文版為研究工具。本研究的樣本分為憂鬱症病患與非憂鬱症的一般大學生兩大類。240位憂鬱症病患樣本是由台北市立和平醫院精神科門診募集而來；321位大學生則以便利抽樣的方式募集而來。 為了驗証FPCS的有效性，本研究進行三個子研究，來比較FPCS與傳統計分法在信度、效度、集群分析的分類正確性。 子研究一探討FPCS的信度。本研究以Cronbach alpha係數來衡量量表的內部一致性，並且以結構方程式模式(structure equation modeling)進行驗證性因素分析所估計的各試題的變異數被潛在構念解釋的比例當作信度的指標。由研究結果顯示，以量表整體而言，FPCS計分的結果得到較高的內部一致性；以各題而言，量表各試題的變異數被潛在構念解釋的百分比高於傳統的原始分數。此結果顯示FPCS的計分方式可以降低測量誤差，提升信度。 子研究二探討FPCS的效度，本研究以精神科醫師的診斷當作效標，分別以FPCS與原始分數兩種不同的計分法當作自變項，以預測效度當作效度的指標。首先，將是否罹患憂鬱症編碼為二元變數，不同計分法所得到的量表分數當作自變數，進行Logistic迴歸分析。研究結果顯示，相較於原始分數，FPCS預測罹患憂鬱症的正確率由 74.8% 提升到 77.2%。接下來，依照所有樣本的憂鬱程度，區分為一般樣本、憂鬱症且緩解、憂鬱症無緩解三類，進行區別分析。研究結果顯示，相較於原始分數，FPCS分類正確率由 71.2% 提升到 80.7%。上述的研究結果顯示，FPCS具有較高的效度，可以降低誤判憂鬱症的機率。 子研究三比較模糊集群分析(fuzzy c-means, FCM)與傳統明確邏輯的集群分析。首先利用分群效度(clustering validity)指標，決定群數為三群。並以此結果，指定模糊集群、Wald法、k-means法之群數。為了比較分類的效果，將模糊集群之樣本，指定給獲得最大隸屬度之集群。並且以醫師的診斷的憂鬱程度當作評估分類結果之標準。研究結果顯示，相較於傳統明確邏輯的集群分析(Wald法、k-means法)，模糊集群分析得到分群結果，與醫師的診斷的結果有最高的相關。結果顯示模糊集群分析更能夠忠實的反映資料結構。 整體而言，相較於原始分數，FPCS有較高的信度、效度、分類正確性。此實証性研究結果支持了模糊集合論應用於心理學研究的可行性；多值的模糊邏輯比二值明確邏輯更能夠正確反映出人類的思維。 / The aim of this study was to propose and validate the new scaling method, fuzzy partial credit scaling (FPCS), which combines fuzzy set theory with the partial credit model (PCM) to score rating scales. To achieve this goal, the Chinese version of BDI (Beck Depression Inventory-II) was administrated to a depressed sample of patients and a non-depressed sample. The depressed sample consisted of 240 outpatients who were diagnosed as depressed by a psychiatric doctor, while 321 undergraduate students were recruited for the nondepressed sample. In FPCS, triangular fuzzy numbers were generated by step parameters to characterize distributions of each alternative value. Next, the center of gravity (COG) method was applied to “de-fuzzify” the fuzzy number into a scalar. Then, the “observed fuzzy scores” defined in FPCS were calculated as the sums of fuzzy number values weighted by membership degrees for the following analysis. Three studies were performed to compare the differences in reliability, validity and clustering precision between the raw score and FPCS. In Study One, the reliability issue of FPCS was discussed. The results of confirmatory factor analysis demonstrate that the BDI reliability was higher in FCPS than in raw scoring. That is, compared with raw scoring, scoring via FPCS produced fewer measurement errors, meaning that more variances in an item of BDI were explained by depression. In Study Two, the predictive validity issue of FPCS was investigated. First, logistic regression analysis was used to predict the odds of suffering depression based on FPCS and the raw scores. The analytical results showed that, via FPCS, the probability of correct classification of depressed and non-depressed was raised from 74.8% to 77.2%. Next, discrimination analysis was performed to classify the subjects according to the severity of depression into three categories: non-depression, depression with remission and depression without remission. The analytical results exhibited that, via FPCS, the probability of correct classification of severity of depression was raised from 71.2% to 80.7%. These two statistical analyses consistently show that FPCS exhibited higher predictive validity than did the raw score. That is, BDI scoring via FPCS makes more accuracy predictions for depression than raw score. In Study Three, fuzzy c-means (FCM) clustering was applied to partition the sample according to severity of depression. To examine explore whether fuzzy-based clustering methods uncover the information inherent in the latent structure more accurately than crisp clustering, FCM, Wald’s method, and k-means method were performed. The analytical results reveal that the association between the original and classified membership generated by FCM was stronger than that of the Wald and k-means methods. Hence, FCM revealed the data structure most accurately. Overall, FPCS has been consistently shown to be superior to raw scoring in terms of reliability, validity, and clustering accuracy. This study has empirically shown that fuzzy set theory is applicable to psychological research.

Submitted by Manoela Sabbadin Begas (manu.begas@gmail.com) on 2017-03-21T17:15:56Z No. of bitstreams: 1 Manoela_Dissertação.pdf: 1812808 bytes, checksum: c5237fd3e360f7c33edd0f82fc1e73be (MD5) / Approved for entry into archive by Pamela Beltran Tonsa (pamela.tonsa@fgv.br) on 2017-03-21T17:17:07Z (GMT) No. of bitstreams: 1 Manoela_Dissertação.pdf: 1812808 bytes, checksum: c5237fd3e360f7c33edd0f82fc1e73be (MD5) / Made available in DSpace on 2017-03-22T12:14:18Z (GMT). No. of bitstreams: 1 Manoela_Dissertação.pdf: 1812808 bytes, checksum: c5237fd3e360f7c33edd0f82fc1e73be (MD5) Previous issue date: 2017-02-24 / The main objective of this study was to investigate the effect of perception of pressure to buy in the perception of consumer justice in direct sales. In addition, it also sought an approximation/friendship between salespeople and consumers influencing the perceived pressure effect on consumer perception of justice, as well as whether emotions (positive versus negative) mediate a relationship between a perception of justice and behavior (intent of repurchase and word-of-mouth) from salespeople. A quantitative study was carried out through a survey of 411 direct selling custom ers, and the data were analyzed by structural equation modeling (SEM). The results showed that consumers do not feel pressured by resellers, so a perception of fairness perceived by consumers has been high and has led them to the intentions of positive repurchase and buzz behavior in relation to the salespeople. Proximity/friendship between consumers and salespeople increased a perception of consumer justice, and only as positive emotions did they partially mediate a perception of fairness and consumer behavior. / Este estudo teve como objetivo principal investigar o efeito da percepção de pressão para comprar na percepção de justiça do consumidor, na venda direta. Além disso, também buscou compreender se a proximidade/amizade entre revendedores e consumidores influencia o efeito da percepção de pressão na percepção de justiça do consumidor, bem como se as emoções (positivas vs. negativas) mediam a relação entre a percepção de justiça e o comportamento (intenção de recompra e boca-a-boca) dos consumidores em relação aos revendedores. Foi realizado um estudo quantitativo mediante levantamento de dados (survey) com 411 consumidores de venda direta, e os dados foram analisados pela modelagem de equações estruturais (MEE). Os resultados mostraram que os consumidores não se sentem pressionados pelos revendedores, portanto a percepção de justiça percebida pelos consumidores foi alta e os levou a intenções de comportamentos de recompra e boca-a-boca positivos em relação ao revendedor. A proximidade/amizade entre consumidores e revendedores aumentou a percepção de justiça do consumidor, e apenas as emoções positivas mediaram parcialmente a relação entre a percepção de justiça e o comportamento do consumidor.

The systemic model posits that informal social control directly reduces crime victimization and social networks indirectly reduce crime victimization through informal social control. While empirical testing of the systemic model advanced the theory, important analytical issues remain. First, social networks are inconsistently conceptualized and measured. Second, the conceptual relationship between social networks and informal social control remains unclear. This study addresses these issues by testing an alternative to the systemic model, including new constructs and hypotheses. The goal is to develop better indicators for the model and refine the theory, rethinking and deepening the existing theory about neighborhood effects on crime victimization. The data come from the 2002-2003 Seattle Neighborhoods and Crime Survey (N=2,200). Structural equation modeling (SEM), a multivariate statistical technique, was used to analyze these data. The SEM included five latent constructs (neighboring, neighborhood and non-neighborhood social networks, collective efficacy, and crime victimization) and six social structural variables (racially homogeneous neighborhood, resident tenure, household income, family disruption, male, and non-white ethnicity). One of my 9 hypotheses was supported; the remaining hypotheses were partly supported. The results support my argument that the systemic model is too simplistic, but the relationships among the variables are not exactly as I hypothesized. The results provide insight into the complexities of the systemic model and areas for future research.