IoT is a technology where different devices are equipped with internet connection which makes it possible to control them and exchange data over internet. IoT can be thought of as an umbrella term covering a broad and ever-growing range of services and technologies. One of the segments within IoT is the smart home ecosystem. The tremendous development the last decade within smartphones, wearable devices and broadband has created new ways to connect individual devices in the home (Qasim and Abu-Shanab, 2016; Jeong et al, 2016; Wilson et al, 2017; Hubert et al, 2017). This creates a synergy effect; by connecting multiple devices to a system new value is created. Energy, home controls, security, communication and entertainment services are all included in the smart home (Miller, 2015; Wilson et al, 2017). Even though the concept of smart homes has a large potential it seems like it has not reached its full potential and the diffusion of the innovation among the consumers is still at an early stage (Balta-Ozkan et.al, 2013; Yang et.al 2017). So far, many studies have been performed on the technical aspects of IoT and smart home ecosystems but less attention has been paid on the consumer point of view and what determinants that play a role in the intention to adopt the technology (Yang, Lee, and Zo. 2017). In addition, previous studies have mainly focused of one single device and has not considered the entire ecosystem (Yang, Lee, and Zo. 2017). Therefore, the purpose with this thesis is to study what are the key determinants for the intention to adopt smart homes from an ecosystem point of view. To fulfill the purpose known theoretical models regarding intention to adopt technology have been used to develop a research model. The basis to establish the research model has been the theory of innovation adoption, TRA, TPB, TAM, VAM and UTAUT. Based on the literature four determinants were selected to be included in the model; these were cost, perceived ease of use, perceived usefulness and individualization. The first three are all included in the mentioned theoretical models and have previously been proven to be important for intention to adopt. The last one, individualization is derived from the field of product differentiation. In the literature it is mentioned that the possibility to refine, adjust and modify may be crucial for the user (Dodgson et.al. 2008). With this background it was interested to include individualization as a determinant in the research model and study how it impacts intention to adopt. In addition to the determinants one moderator was included; the composition of the household. In order to collect the empirical data a survey was conducted using the snowball sampling approach via Facebook and LinkedIn. The survey consisted of two sections where the first section aimed to collect background information about the respondent and the second section consisted of questions regarding the determinants. In the second section the respondents were asked to respond according to a 5-point Likert scale. The used questions in the survey was predefined in the literature. Study results show that consumers’ use intention is shaped by individualization, perceived usefulness and perceived ease of use. Cost was found not to be statistically significant. Neither was the composition of the household.

Return on Capital Employed (ROCE) is a popular financial instrument and communication tool for the appraisal of companies. Often, companies management and other practitioners use untested rules and behavioural approach when investigating the key determinants of ROCE, instead of the scientific statistical paradigm. The aim of this dissertation was to identify and quantify key determinants of ROCE of individual companies listed on the Johannesburg Stock Exchange (JSE), by comparing classical multiple linear regression, principal components regression, generalized least squares regression, and robust maximum likelihood regression approaches in order to improve companies decision making. Performance indicators used to arrive at the best approach were coefficient of determination ( ), adjusted ( , and Mean Square Residual (MSE). Since the ROCE variable had positive and negative values two separate analyses were done. The classical multiple linear regression models were constructed using stepwise directed search for dependent variable log ROCE for the two data sets. Assumptions were satisfied and problem of multicollinearity was addressed. For the positive ROCE data set, the classical multiple linear regression model had a of 0.928, an of 0.927, a MSE of 0.013, and the lead key determinant was Return on Equity (ROE),with positive elasticity, followed by Debt to Equity (D/E) and Capital Employed (CE), both with negative elasticities. The model showed good validation performance. For the negative ROCE data set, the classical multiple linear regression model had a of 0.666, an of 0.652, a MSE of 0.149, and the lead key determinant was Assets per Capital Employed (APCE) with positive effect, followed by Return on Assets (ROA) and Market Capitalization (MC), both with negative effects. The model showed poor validation performance. The results indicated more and less precision than those found by previous studies. This suggested that the key determinants are also important sources of variability in ROCE of individual companies that management need to work with. To handle the problem of multicollinearity in the data, principal components were selected using Kaiser-Guttman criterion. The principal components regression model was constructed using dependent variable log ROCE for the two data sets. Assumptions were satisfied. For the positive ROCE data set, the principal components regression model had a of 0.929, an of 0.929, a MSE of 0.069, and the lead key determinant was PC4 (log ROA, log ROE, log Operating Profit Margin (OPM)) and followed by PC2 (log Earnings Yield (EY), log Price to Earnings (P/E)), both with positive effects. The model resulted in a satisfactory validation performance. For the negative ROCE data set, the principal components regression model had a of 0.544, an of 0.532, a MSE of 0.167, and the lead key determinant was PC3 (ROA, EY, APCE) and followed by PC1 (MC, CE), both with negative effects. The model indicated an accurate validation performance. The results showed that the use of principal components as independent variables did not improve classical multiple linear regression model prediction in our data. This implied that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Generalized least square regression was used to assess heteroscedasticity and dependences in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the weighted generalized least squares regression model had a of 0.920, an of 0.919, a MSE of 0.044, and the lead key determinant was ROE with positive effect, followed by D/E with negative effect, Dividend Yield (DY) with positive effect and lastly CE with negative effect. The model indicated an accurate validation performance. For the negative ROCE data set, the weighted generalized least squares regression model had a of 0.559, an of 0.548, a MSE of 57.125, and the lead key determinant was APCE and followed by ROA, both with positive effects.The model showed a weak validation performance. The results suggested that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Robust maximum likelihood regression was employed to handle the problem of contamination in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the robust maximum likelihood regression model had a of 0.998, an of 0.997, a MSE of 6.739, and the lead key determinant was ROE with positive effect, followed by DY and lastly D/E, both with negative effects. The model showed a strong validation performance. For the negative ROCE data set, the robust maximum likelihood regression model had a of 0.990, an of 0.984, a MSE of 98.883, and the lead key determinant was APCE with positive effect and followed by ROA with negative effect. The model also showed a strong validation performance. The results reflected that the key determinants are major sources of variability in ROCE of individual companies that management need to work with. Overall, the findings showed that the use of robust maximum likelihood regression provided more precise results compared to those obtained using the three competing approaches, because it is more consistent, sufficient and efficient; has a higher breakdown point and no conditions. Companies management can establish and control proper marketing strategies using the key determinants, and results of these strategies can see an improvement in ROCE. / Mathematical Sciences / M. Sc. (Statistics)

Return on Capital Employed (ROCE) is a popular financial instrument and communication tool for the appraisal of companies. Often, companies management and other practitioners use untested rules and behavioural approach when investigating the key determinants of ROCE, instead of the scientific statistical paradigm. The aim of this dissertation was to identify and quantify key determinants of ROCE of individual companies listed on the Johannesburg Stock Exchange (JSE), by comparing classical multiple linear regression, principal components regression, generalized least squares regression, and robust maximum likelihood regression approaches in order to improve companies decision making. Performance indicators used to arrive at the best approach were coefficient of determination ( ), adjusted ( , and Mean Square Residual (MSE). Since the ROCE variable had positive and negative values two separate analyses were done. The classical multiple linear regression models were constructed using stepwise directed search for dependent variable log ROCE for the two data sets. Assumptions were satisfied and problem of multicollinearity was addressed. For the positive ROCE data set, the classical multiple linear regression model had a of 0.928, an of 0.927, a MSE of 0.013, and the lead key determinant was Return on Equity (ROE),with positive elasticity, followed by Debt to Equity (D/E) and Capital Employed (CE), both with negative elasticities. The model showed good validation performance. For the negative ROCE data set, the classical multiple linear regression model had a of 0.666, an of 0.652, a MSE of 0.149, and the lead key determinant was Assets per Capital Employed (APCE) with positive effect, followed by Return on Assets (ROA) and Market Capitalization (MC), both with negative effects. The model showed poor validation performance. The results indicated more and less precision than those found by previous studies. This suggested that the key determinants are also important sources of variability in ROCE of individual companies that management need to work with. To handle the problem of multicollinearity in the data, principal components were selected using Kaiser-Guttman criterion. The principal components regression model was constructed using dependent variable log ROCE for the two data sets. Assumptions were satisfied. For the positive ROCE data set, the principal components regression model had a of 0.929, an of 0.929, a MSE of 0.069, and the lead key determinant was PC4 (log ROA, log ROE, log Operating Profit Margin (OPM)) and followed by PC2 (log Earnings Yield (EY), log Price to Earnings (P/E)), both with positive effects. The model resulted in a satisfactory validation performance. For the negative ROCE data set, the principal components regression model had a of 0.544, an of 0.532, a MSE of 0.167, and the lead key determinant was PC3 (ROA, EY, APCE) and followed by PC1 (MC, CE), both with negative effects. The model indicated an accurate validation performance. The results showed that the use of principal components as independent variables did not improve classical multiple linear regression model prediction in our data. This implied that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Generalized least square regression was used to assess heteroscedasticity and dependences in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the weighted generalized least squares regression model had a of 0.920, an of 0.919, a MSE of 0.044, and the lead key determinant was ROE with positive effect, followed by D/E with negative effect, Dividend Yield (DY) with positive effect and lastly CE with negative effect. The model indicated an accurate validation performance. For the negative ROCE data set, the weighted generalized least squares regression model had a of 0.559, an of 0.548, a MSE of 57.125, and the lead key determinant was APCE and followed by ROA, both with positive effects.The model showed a weak validation performance. The results suggested that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Robust maximum likelihood regression was employed to handle the problem of contamination in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the robust maximum likelihood regression model had a of 0.998, an of 0.997, a MSE of 6.739, and the lead key determinant was ROE with positive effect, followed by DY and lastly D/E, both with negative effects. The model showed a strong validation performance. For the negative ROCE data set, the robust maximum likelihood regression model had a of 0.990, an of 0.984, a MSE of 98.883, and the lead key determinant was APCE with positive effect and followed by ROA with negative effect. The model also showed a strong validation performance. The results reflected that the key determinants are major sources of variability in ROCE of individual companies that management need to work with. Overall, the findings showed that the use of robust maximum likelihood regression provided more precise results compared to those obtained using the three competing approaches, because it is more consistent, sufficient and efficient; has a higher breakdown point and no conditions. Companies management can establish and control proper marketing strategies using the key determinants, and results of these strategies can see an improvement in ROCE. / Mathematical Sciences / M. Sc. (Statistics)

寬頻擴散可促進國家之生產力、就業、經濟成長及國家競爭力等，若能精準找出促進寬頻擴散之關鍵影響因素，將有利於政府集中資源有效率地推動寬頻發展。本研究提出研究問題與假說，以Rogers (2003)及Hall (2006)所提出影響創新擴散速率之社經因素、採用成本、採用效益、網路效應、資訊及不確定性及產業環境等6大因素面向為基礎，蒐集OECD國家及台灣等31國家相關資料，挑選Gompertz模型進行固定寬頻擴散Panel資料迴歸分析，發現各因素在全期及不同擴散階段有不同之顯著性與影響程度，表示分階段分析有其必要性。擴散初期之關鍵影響因素為收入、教育水準、平台競爭程度、人口密度及實施LLU累積年度等5項，而擴散後期之關鍵影響因素為寬頻價格、網際網路內容、決定採用時固定寬頻用戶比例、撥接用戶比例及擁有PC家庭比例等5項，可作為政府及業者於不同擴散階段精準投入資源以有效推動寬頻擴散之參考。 本研究續以前述分析結果選取日本、南韓、美國、丹麥、瑞士及台灣進行實際擴散比較，確認前述關鍵影響因素挑選之有效性。擴散初期，台灣有高人口密度優勢，若能提早推動寬頻並推動促進競爭措施，可促進初期之快速擴散。擴散後期，台灣國際排名退步，原因為寬頻價格過高，故此階段政府及業者應特別確保寬頻價格能夠使潛在採用者有能力並願意付費採用，才能促使寬頻持續有效擴散。 最後，本研究採用與固定寬頻相同迴歸分析模型對FTTX及行動寬頻分別進行分析後，有關行動寬頻，教育水準、寬頻價格、決定採用時行動寬頻用戶比例、人口密度、網際網路內容、使用固定寬頻語音服務比例、決定採用時FTTX用戶比例及使用網際網路家庭比例等8項變數有顯著效應；有關行動寬頻，收入、寬頻價格、網際網路內容、決定採用時行動寬頻用戶比例、使用網際網路家庭比例及人口密度等6項變數有顯著效應。因此，政府及業者若擬促進特定寬頻服務發展，仍須針對其服務特性推動特定之政策或策略。其中，寬頻價格、網際網路內容、網路效應及使用網際網路家庭比例等4項因素對FTTX及行動寬頻服務之影響類似，而此4個因素與固定寬頻後期擴散之關鍵影響因素較相近，因此，對於已存在市場的服務，即使是後來以較佳品質或功能之新服務型式提供，新服務之關鍵影響因素仍較接近已存在市場服務關鍵因素。 總之，本研究不同於過去文獻，以創新擴散理論為基礎，以國家層級資料量化分析與探討寬頻擴散之關鍵影響因素，除分別提供政策及管理建議供政府及業者參考外，亦補強Rogers (2003)及Hall (2006)所提出創新擴散理論未釐清與比較創新擴散影響因素在不同擴散階段影響之不足。 / Broadband diffusion may enhance innovation, productivity, employment, economic growth, and, ultimately, national competitiveness. If key determinants for broadband diffusion are identified, governments can align its resources with them to effectively promote the diffusion. Based on the determinants of the diffusion rate identified by Rogers (2003) and Hall (2006), this research compiled data available about OECD countries as well as Taiwan to implement overall and staged panel regressions on fixed broadband diffusion by adopting Gompertz model. The findings indicate that the significance of the determinants varies between overall and staged analysis, which consequently justifies the necessity of a staged analysis. The key determinants in the early stage are income, education level, platform competition, population density, and the accumulated years of implementing LLU policy; however, in the late stage they are broadband price, Internet content, network effect, the penetration of dial-up users, and percentage of household with computer. Governments may more accurately promote broadband diffusion according to different key determinants in different stages. This research further compared the real fixed broadband diffusion of Japan, South Korea, USA, Denmark, Switzerland, and Taiwan based on the previous analysis results. The findings generally justify the choice of key determinants in the previous analysis. In the early stage, Taiwan had the advantage of high population density. If the government could have promoted fixed broad banded services and market competition earlier, the penetration would have grown much faster. In the late stage, since the broadband price was too high in Taiwan, its international ranking of fixed broadband penetration declined. Therefore, in order to further promote the diffusion of fixed broadband, the government should have ensured that the price was low enough to convince the potential adoptors to purchase broadband services. Finally, this research adopted the same approach as that of previous fixed broadband to analyze the diffusion of FTTX and mobile broadband, respectively. Education level, broadband price, network effect of FTTX, network effect of mobile broadband, Internet content, population density, percentage of household with computer, and the penetration of fixed VOIP users have significant effect on FTTX diffusion. However, income, broadband price, network effect of mobile broadband, Internet content, population density, and percentage of household with computer have significant effect on mobile broadband diffusion. Therefore, governments or operators should tailor their policies or strategies for specific services. The effects of broadband price, Internet content, network effect, and percentage of household with computer are similar in both FTTX and mobile broadband, and they are also similar to the key determinants of fixed broadband diffusion in the late stage. Therefore, even though a new service with better quality or function is introduced in an existing market, its key determinants are more similar to those of the existing service depending on its diffusion stage. In conclusion, different from previous research, this one applied national-level data to quantatively analyzed and explore the key determinants of broadband diffusion based on innovation diffusion theory. The research findings not only propose policy and management suggestions to governments and service providers, but also supplement the the theory proposed by Rogers (2003) and Hall (2006), which did not identify and compare the determinants of innovation in different diffusion stages.