MKT 6009 Marketing Internship (0 semester credit hours) Student gains experience and improves skills through appropriate developmental work assignments in a real business environment. Student must identify and submit specific business learning objectives at the beginning of the semester. The student must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, student prepares an oral or poster presentation, or a written paper reflecting on the work experience. Student performance is evaluated by the work supervisor. Pass/Fail only. Prerequisites: (MAS 6102 or MBA major) and department consent required. (0-0) S MKT 6244 Digital Marketing Strategy (2 semester credit hours) Executive Education Course. The course explores three distinct areas within marketing and sales namely, digital marketing, traditional sales prospecting, and executive sales organization and strategy. The continuing convergence of the digital marketing and sales funnels has created a strategic continuum from digital lead generation to digital sales. The course identifies the current composition of this digital continuum while providing opportunities to evaluate sales and marketing digital strategies. Prerequisites: MKT 6301 and instructor consent required. (2-0) Y MKT 6301 (SYSM 6318) Marketing Management (3 semester credit hours) Overview of marketing management methods, principles and concepts including product, pricing, promotion and distribution decisions as well as segmentation, targeting and positioning. (3-0) S MKT 6309 Marketing Data Analysis and Research (3 semester credit hours) Methods employed in market research and data analysis to understand consumer behavior, customer journeys, and markets so as to enable better decision-making. Topics include understanding different sources of data, survey design, experiments, and sampling plans. The course will cover the techniques used for market sizing estimation and forecasting. In addition, the course will cover the foundational concepts and techniques used in data visualization and \"story-telling\" for clients and management. Corequisites: MKT 6301 and OPRE 6301. (3-0) Y MKT 6310 Consumer Behavior (3 semester credit hours) An exposition of the theoretical perspectives of consumer behavior along with practical marketing implication. Study of psychological, sociological and behavioral findings and frameworks with reference to consumer decision-making. Topics will include the consumer decision-making model, individual determinants of consumer behavior and environmental influences on consumer behavior and their impact on marketing. Prerequisite: MKT 6301. (3-0) Y MKT 6321 Interactive and Digital Marketing (3 semester credit hours) Introduction to the theory and practice of interactive and digital marketing. Topics covered include: online-market research, consumer behavior, conversion metrics, and segmentation considerations; ecommerce, search and display advertising, audiences, search engine marketing, email, mobile, video, social networks, and the Internet of Things. (3-0) T MKT 6322 Internet Business Models (3 semester credit hours) Topics to be covered are: consumer behavior on the Internet, advertising on the Internet, competitive strategies, market research using the Internet, brand management, managing distribution and supply chains, pricing strategies, electronic payment systems, and developing virtual organizations. Further, students learn auction theory, web content design, and clickstream analysis. Prerequisite: MKT 6301. (3-0) Y MKT 6323 Database Marketing (3 semester credit hours) Techniques to analyze, interpret, and utilize marketing databases of customers to identify a firm's best customers, understanding their needs, and targeting communications and promotions to retain such customers. Topics

Marketing for management.

Статья посвящена вопросам влияния власти на поведение человека. Авторы рассматривают данные различных источников, в которых увеличение власти связывается с напористостью, а ее уменьшение - с подавленностью. Конкретно, власть ассоциируется с: а) позитивным аффектом; б) вниманием к вознаграждению и к свойствам других, удовлетворяющим личные цели; в) автоматической переработкой информации и резкими суждениями; г) расторможенным социальным поведением. Уменьшение власти, напротив, ассоциируется с: а) негативным аффектом; б) вниманием к угрозам и наказаниям, к интересам других и к тем характеристикам я, которые отвечают целям других; в) контролируемой переработкой информации и совещательным типом рассуждений; г) подавленным социальным поведением. Обсуждаются также последствия этих паттернов поведения, связанных с властью, и потенциальные модераторы.

Power, approach, and inhibition. Власть, напористость и подавленность

Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

On making causal claims : A review and recommendations

How does corporate social responsibility contribute to firm financial performance? The mediating role of competitive advantage, reputation, and customer satisfaction

Sustainable–Smart–Resilient–Low Carbon–Eco–Knowledge Cities; Making sense of a multitude of concepts promoting sustainable urbanization

Structural Equation Modeling or popularly known as SEM is one of the newest methods of multivariate data analysis developed specifically to overcome limitations experienced in the previous methodology namely the Ordinary Least Square Regression (OLS). This book introduces the application of the most popular SEM software namely Analysis of Moments Structures (AMOS). This book provides practical examples where SEM with AMOS Graphics is employed to model and solve the research problems in postgraduate studies.

SEM Made Simple: A Gentle Approach to Learning Structural Equation Modeling

Lately, there was some attention for the Variance Based SEM (VB-SEM) against that of Covariance Based SEM (CB-SEM) from social science researches regarding the fitness indexes, sample size requirement, and normality assumption Not many of them aware that VB-SEM is developed based on the non-parametric approach compared to the parametric approach of CB-SEM In fact the fitness of a model should not be taken lightly since it reflects the behavior of data in relation to the proposed model for the study Furthermore, the adequacy of sample size and the normality of data are among the main assumptions of parametric test itself This study intended to clarify the ambiguities among the social science community by employing the data-set which do not meet the fitness requirements and normality assumptions to execute both CB-SEM and VB-SEM The findings reveal that the result of CB-SEM with bootstrapping is almost similar to that of VB-SEM (bootstrapping as usual) Therefore, the failure to meet the fitness and normality requirements should not be the reason for employing Non-Parametric SEM

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Parametric and Non Parametric Approach in Structural Equation Modeling (SEM): The Application of Bootstrapping

Article history: Received: September 5, 2018 Received in revised format: October 8, 2018 Accepted: November 7, 2018 Available online: November 7, 2018 A constant concern of academic institutions has been to improve the customer satisfaction in university libraries. The service quality within academic library context is pivotal for satisfying customers by meeting the customers’ needs to create loyalty amongst customers. This research uses LibQual to analyze the gap between customer’s perception and expectation, concerned with the services at the University Sultan Zainal Abidin (UniSZA) Library. There are six dimensions in service quality; namely general services, search for materials, library collection, staff, environment and environment, considered for this empirical research. The purpose of this paper is to 1) know service quality dimensions that satisfy the customers and 2) to observe the impact of service quality on customer satisfaction. The research methodology is carried out using a questionnaire survey distributed among 170 samples through simple random sampling. The data obtained was analyzed by using covariance-based structural equation modeling and importance-performance analysis. The results suggest that quality of service had a significant impact on customer satisfaction. Among the service quality dimensions, library environment and general service were viewed as high importance and strong performance index. Implication for research and practice resulting from these findings were also discussed. © 2019 by the authors; licensee Growing Science, Canada

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Assessing the effects of service quality on customer satisfaction

Universities worldwide are aiming to have committed lecturers in their system. Many studies have shown that committed workers are more likely to become high performing workers who benefit their organization in terms of productivity and profit (Narimawati, 2007; Tella et al. 2007). This study intended to ascertain the impact of job satisfaction of university lecturers on their commitment towards academic activities. Besides that, it also investigated specifically factors contributing to job satisfaction, namely promotional opportunities, remuneration, working environment, workload, relationship with colleagues, and management style. The study used self administered questionnaires which were distributed to 320 lecturers from UiTM Kelantan. Data was analysed using SPSS 16 for descriptive analysis while AMOS 17 was used for Structural Equation Modelling (SEM). The study found that promotional opportunities, workload and relationship with colleagues significantly affect job satisfaction of lecturers. This is consistent with the findings of Chimanikar et al. (2007), and Santhapparaj and Alam (2005). Most importantly, this study also confirmed that there is a significant relationship between job satisfaction and work commitment. Overall, the findings of this study indicate that satisfied lecturers will be committed to their job, and promotional opportunities, workload and relationship with colleagues should be given due consideration as they significantly affect job satisfaction level among lecturers.

Modelling Job Satisfaction And Work Commitment Among Lecturers: A Case Of UiTM Kelantan

The Likert scale is commonly used in survey research using primary and secondary data to measure the respondent attitude by asking insofar to which they agree or disagree with a particular questions. In generals, Likert scale would be preferred in the questionnaire development stage to ascertain the researchers conducting their research needed. However, the researchers nowadays are abuse to understand the nature of measurement scale in data analysis and thus causes the finding obtained are meaningless. This article is aimed to compare the performance of two categories of measurement scales which are 5 point and 10 points of Likert scales using the same sample size and research subject that would pave the way to understand the real different between both of these ranges using Structural Equation Modeling (SEM). Moreover, this study also interested to clarify briefly between two types of measurement scale namely ordinal and interval data. The findings reveal that 10 points of Likert scale is more efficient than 5 points of Likert scale in operating of measurement model.

Zainudin Awang

Papers

SEM Made Simple: A Gentle Approach to Learning Structural Equation Modeling

Parametric and Non Parametric Approach in Structural Equation Modeling (SEM): The Application of Bootstrapping

Assessing the effects of service quality on customer satisfaction

Modelling Job Satisfaction And Work Commitment Among Lecturers: A Case Of UiTM Kelantan

The Likert scale analysis using parametric based Structural Equation Modeling (SEM)