In the preceding chapter we used a variety of independent variables to predict dress sales. All t... more In the preceding chapter we used a variety of independent variables to predict dress sales. All the trait values for sales (dependent variable) and for catalogue image size (independent variable) were recorded over the same period of time. Studies like these are called cross-sectional analyses. When the data is measured at successive time intervals, it is called a time series analysis or a longitudinal study. This type of study requires a time series in which data for independent and dependent variables are observed for specific points of time (t = 1,…, n). In its simplest version, time is the only independent variable and is plotted on the x-axis. This kind of time series does nothing more than link variable data over different periods. Figure 6.1 shows an example with a graph of diesel fuel prices by year.
This chapter uncovers the most important insights into the challenges encountered by fashion comp... more This chapter uncovers the most important insights into the challenges encountered by fashion companies that want to realize eco-innovation to market a justified, sustainable brand. This chapter examines the opportunities and risks and highlights the major changes that companies should make within the fashion industry to become more sustainable. To support the main objective, an interview questionnaire was designed to compare sustainability experts’ perceptions with current fashion brand attitudes towards social responsibility. The results indicate that while the current fashion industry is taking steps to become more sustainable, this is a slow process. This delay is mainly due to a repetitive pattern: preserving the fashion industry is a vicious cycle where stakeholders do not cooperate to change. Among many others, the first challenge is for governments, businesses, and consumers to become more aware of the importance of sustainability to break the current pattern in which “fast fashion” dominates “slow fashion” and to speed up developments. Based on the interview results, recommendations are made for the fashion industry for its eco-innovation in processes and products.
This chapter uncovers the most important insights into the challenges encountered by fashion comp... more This chapter uncovers the most important insights into the challenges encountered by fashion companies that want to realize eco-innovation to market a justified, sustainable brand. This chapter examines the opportunities and risks and highlights the major changes that companies should make within the fashion industry to become more sustainable. To support the main objective, an interview questionnaire was designed to compare sustainability experts’ perceptions with current fashion brand attitudes towards social responsibility. The results indicate that while the current fashion industry is taking steps to become more sustainable, this is a slow process. This delay is mainly due to a repetitive pattern: preserving the fashion industry is a vicious cycle where stakeholders do not cooperate to change. Among many others, the first challenge is for governments, businesses, and consumers to become more aware of the importance of sustainability to break the current pattern in which “fast fashion” dominates “slow fashion” and to speed up developments. Based on the interview results, recommendations are made for the fashion industry for its eco-innovation in processes and products.
M-Commerce is steadily on the rise – mainly driven by increasing smartphone usage. This so-called... more M-Commerce is steadily on the rise – mainly driven by increasing smartphone usage. This so-called “Mobile Revolution” is even considered to be of similar impact as the “Internet Revolution” in the 1990s. This study aims to analyse the level of consumers’ intention to use M-Commerce – and the factors which influence their intentions. Since the level of M-Commerce varies country by country, a comparison of two countries, a developed market (Germany) and a developing country (Peru), has been conducted. The following influencing factors have been analysed: (1) sense of comfort (including perceived ease of use, social influence, convenience, appreciation of consultative services and cash preferences), (2) involvement into E-Business (including intention to purchase online, trust in online shops and intention to use social commerce) and (3) perception of safety (attitude towards data protection, attitudes towards transaction security and safety precautions).The study could identify the mo...
Angewandte Induktive Statistik und Statistische Testverfahren, 2018
Die klassische Messfehlertheorie geht beim Messen von der Stabilitat eines zu messenden Merkmals ... more Die klassische Messfehlertheorie geht beim Messen von der Stabilitat eines zu messenden Merkmals (Reliabilitat einer Messung) und von der Grundannahme aus, dass die Ergebnisse der Messungen mit den wahren Werten korrespondieren (Validitat einer Messung). Dabei bestreitet die klassische Messfehlertheorie nicht die Existenz von Fehlern. Vielmehr geht sie von der durchaus realitatsnahen Annahme aus, dass Messungen selbst bei groster Sorgfalt niemals perfekt sein konnen und somit zwangslaufig Messfehler auftreten.
Applied Statistics and Multivariate Data Analysis for Business and Economics, 2019
The term descriptive statistics refers to all techniques used to obtain information based on the ... more The term descriptive statistics refers to all techniques used to obtain information based on the description of data from a population. The calculation of figures and parameters and the generation of graphics and tables are just some of the methods and techniques used in descriptive statistics. Inferential statistics, sometimes referred to as inductive statistics, did not develop until much later. It uses samples to make conclusions, or inferences, about a population. Many of the methods of inferential statistics go back to discoveries made by such thinkers as Jacob Bernoulli (1654–1705), Abraham de Moivre (1667–1754), Thomas Bayes (1702–1761), Pierre-Simon Laplace (1749–1827), Carl Friedrich Gaus (1777–1855), Pafnuty Lvovich Chebyshev (1821–1894), Francis Galton (1822–1911), Ronald A. Fisher (1890–1962), and William Sealy Gosset (1876–1937). Thanks to their work, we no longer have to count and measure each individual within a population, but can instead conduct a smaller, more manageable survey. This comes in handy when a full survey would be too expensive or take too long, or when collecting the data damages the elements under investigation (as with various kinds of material testing, such as wine tasting). But inferential statistics has a price: because the data are collected from a sample, not from a total population, our conclusions about the data carry a certain degree of uncertainty. But inferential statistics can also define the “price” of this uncertainty using margins of error. Classical measurement theory gives us the tools to calculate statistical error.
In the preceding chapter we used a variety of independent variables to predict dress sales. All t... more In the preceding chapter we used a variety of independent variables to predict dress sales. All the trait values for sales (dependent variable) and for catalogue image size (independent variable) were recorded over the same period of time. Studies like these are called cross-sectional analyses. When the data is measured at successive time intervals, it is called a time series analysis or a longitudinal study. This type of study requires a time series in which data for independent and dependent variables are observed for specific points of time (t = 1,…, n). In its simplest version, time is the only independent variable and is plotted on the x-axis. This kind of time series does nothing more than link variable data over different periods. Figure 6.1 shows an example with a graph of diesel fuel prices by year.
This chapter uncovers the most important insights into the challenges encountered by fashion comp... more This chapter uncovers the most important insights into the challenges encountered by fashion companies that want to realize eco-innovation to market a justified, sustainable brand. This chapter examines the opportunities and risks and highlights the major changes that companies should make within the fashion industry to become more sustainable. To support the main objective, an interview questionnaire was designed to compare sustainability experts’ perceptions with current fashion brand attitudes towards social responsibility. The results indicate that while the current fashion industry is taking steps to become more sustainable, this is a slow process. This delay is mainly due to a repetitive pattern: preserving the fashion industry is a vicious cycle where stakeholders do not cooperate to change. Among many others, the first challenge is for governments, businesses, and consumers to become more aware of the importance of sustainability to break the current pattern in which “fast fashion” dominates “slow fashion” and to speed up developments. Based on the interview results, recommendations are made for the fashion industry for its eco-innovation in processes and products.
This chapter uncovers the most important insights into the challenges encountered by fashion comp... more This chapter uncovers the most important insights into the challenges encountered by fashion companies that want to realize eco-innovation to market a justified, sustainable brand. This chapter examines the opportunities and risks and highlights the major changes that companies should make within the fashion industry to become more sustainable. To support the main objective, an interview questionnaire was designed to compare sustainability experts’ perceptions with current fashion brand attitudes towards social responsibility. The results indicate that while the current fashion industry is taking steps to become more sustainable, this is a slow process. This delay is mainly due to a repetitive pattern: preserving the fashion industry is a vicious cycle where stakeholders do not cooperate to change. Among many others, the first challenge is for governments, businesses, and consumers to become more aware of the importance of sustainability to break the current pattern in which “fast fashion” dominates “slow fashion” and to speed up developments. Based on the interview results, recommendations are made for the fashion industry for its eco-innovation in processes and products.
M-Commerce is steadily on the rise – mainly driven by increasing smartphone usage. This so-called... more M-Commerce is steadily on the rise – mainly driven by increasing smartphone usage. This so-called “Mobile Revolution” is even considered to be of similar impact as the “Internet Revolution” in the 1990s. This study aims to analyse the level of consumers’ intention to use M-Commerce – and the factors which influence their intentions. Since the level of M-Commerce varies country by country, a comparison of two countries, a developed market (Germany) and a developing country (Peru), has been conducted. The following influencing factors have been analysed: (1) sense of comfort (including perceived ease of use, social influence, convenience, appreciation of consultative services and cash preferences), (2) involvement into E-Business (including intention to purchase online, trust in online shops and intention to use social commerce) and (3) perception of safety (attitude towards data protection, attitudes towards transaction security and safety precautions).The study could identify the mo...
Angewandte Induktive Statistik und Statistische Testverfahren, 2018
Die klassische Messfehlertheorie geht beim Messen von der Stabilitat eines zu messenden Merkmals ... more Die klassische Messfehlertheorie geht beim Messen von der Stabilitat eines zu messenden Merkmals (Reliabilitat einer Messung) und von der Grundannahme aus, dass die Ergebnisse der Messungen mit den wahren Werten korrespondieren (Validitat einer Messung). Dabei bestreitet die klassische Messfehlertheorie nicht die Existenz von Fehlern. Vielmehr geht sie von der durchaus realitatsnahen Annahme aus, dass Messungen selbst bei groster Sorgfalt niemals perfekt sein konnen und somit zwangslaufig Messfehler auftreten.
Applied Statistics and Multivariate Data Analysis for Business and Economics, 2019
The term descriptive statistics refers to all techniques used to obtain information based on the ... more The term descriptive statistics refers to all techniques used to obtain information based on the description of data from a population. The calculation of figures and parameters and the generation of graphics and tables are just some of the methods and techniques used in descriptive statistics. Inferential statistics, sometimes referred to as inductive statistics, did not develop until much later. It uses samples to make conclusions, or inferences, about a population. Many of the methods of inferential statistics go back to discoveries made by such thinkers as Jacob Bernoulli (1654–1705), Abraham de Moivre (1667–1754), Thomas Bayes (1702–1761), Pierre-Simon Laplace (1749–1827), Carl Friedrich Gaus (1777–1855), Pafnuty Lvovich Chebyshev (1821–1894), Francis Galton (1822–1911), Ronald A. Fisher (1890–1962), and William Sealy Gosset (1876–1937). Thanks to their work, we no longer have to count and measure each individual within a population, but can instead conduct a smaller, more manageable survey. This comes in handy when a full survey would be too expensive or take too long, or when collecting the data damages the elements under investigation (as with various kinds of material testing, such as wine tasting). But inferential statistics has a price: because the data are collected from a sample, not from a total population, our conclusions about the data carry a certain degree of uncertainty. But inferential statistics can also define the “price” of this uncertainty using margins of error. Classical measurement theory gives us the tools to calculate statistical error.
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