Using Big Data to Understand Chinese Users' Intentions to Tap Through Mobile Advertisements

This study examines influencing factors for users' intentions to tap through mobile advertisements. This chapter uses a data set with 115,899 records of ad tap-through from a mobile advertising company in China to fit a logit model to examine how the probability of advertisement tap-through is related to the identified factors. The results show that the influencing variables are application type, mobile operators, scrolling frequency, and the regional income level as they are positively correlated with the likelihood whether users would tap on certain types of advertising. Moreover, a Bayesian network model is used to estimate the conditional probability for a user to tap on an advertisement in an application after the user already taps on another advertisement in the same application. Based on the findings, strategies for mobile advertisers to engage in effective and targeted mobile advertising are proposed.

A Composite Risk Model for Optimizing Information System Security

This chapter describes a two-step decision-support risk model that focuses on investment in information technology security. In the first step, the risk level of each of the system's components is mapped with the goal of identifying the subsystems that pose the highest risk. In the second step, the model determines how much to invest in various technological tools and workplace culture programs to enhance information security. The mode is applied to an information system in an academic institution in Israel. This system comprises 10 subsystems and the three that are responsible to most of the risk are identified. These findings are then used to determine the parameters of the investment allocation problem and find the optimal investment plan. The results of the model's application indicate that monetary incentives and grade cheating are the greatest threats to the system's security. In addition, the results provide support to the claim that information security officials tend to overinvest in security technological tools and underinvest in improving security workplace culture.

Communication Matrices for Managing Dialogue Change to Teamwork Transformation

New technology platforms continue to be introduced inside organizations in this digital age. Technology and generational diversity will have sustained impact on how business organizations consider and adapt to meet a number of technology and people challenges. The case discusses why it is to promote and sustain crucial conversations dialogue among team members to both enhance and strengthen team and teamwork practices. In the case, the change management process was essential to being able to go deeper through matrixed crucial conversations to achieve a desired organization goal. This case study chapter offers insight and outcomes that were achieved by building a dialogic approach and model, so a vital unit of the organization could begin taking advantage of future technology enhancements.

Re-Defining Work-Life Boundaries

Technology has radically changed the way we live and work. This chapter explores the boundaries that knowledge workers employ to delineate work and personal time and the resulting outcomes. Based on scholarly research, the author proposes redefining the work-life boundary into three dimensions: flexibility, work boundary permeability, and home boundary permeability. While no longer able to control the time and place factors that once defined our work and personal time, employees can use behavioral and communicative tactics to maintain balance. These individual policy decisions and potential work-life balance tools are discussed. Organizations can support new boundary controls through education, support, and training. Finally, technology has resulted in cultural and societal changes, which may continue to be supported through national policy.

Grief and the Psychological Transition Process Among Information Technology Team Members

Information technology (IT) employees are responsible for planning, implementing, and supporting new solutions and services in support of enterprise shifts to accommodate new methods and modes of doing business and/or providing products, solutions, and services to internal users, external customers, and partners. But IT workers do not always have a say or input into the choices made by leadership and business managers as it applies to new technologies. This can lead to rework and failure upon initial deployment. In addition, change without inclusion of those responsible may have a negative impact on IT staff and the outcome of the overall change effort. In this chapter, the author explores the experience of personal transition and the impact of organizational change on those responsible, IT workers.

User-Created Online Learning Videos

Through digital technologies, learning has become more flexible in terms of location and mode of delivery, as well as open opportunities for inquiry, discourse, extension, and application towards the creation of new knowledge. A tendency towards a lack of uniformity in the quality of knowledge produced as well as deviation from the norms that guide content creation and sharing have been envisioned with new modes of knowledge creation and sharing in general. This chapter will synthesize research and strategies on designing high value, engaging online video content, with the aim of equipping creators within participatory, informal learning contexts with approaches for becoming more responsible and effective contributors to the digital ecosystem.

Big Data Adoption

This study inspects how big data is comprehended by IT experts and the difficulties that they have in respect to the reception of big data examination. The study also looks into the contributing factors of big data adoption within the manufacturing and services sectors in India. The data were analyzed using exploratory and confirmatory factor analyses, and relevant hypotheses were derived and tested by SEM analysis. The findings revealed that relative advantage, compatibility, complexity, organizational size, top management support, competitive pressure, vendor support, data management, and data privacy are the factors that are important for both industries. Through a comparison of the industries, statistically significant differences between the service and the manufacturing sectors were found; in other words, it has been noted that the relative importance of all factors for big data adoption differs between the industries, with the only exception being its complexity – it was found to be insignificant for the manufacturing sector.