Exploring technology acceptance for mobile services

Purpose: Mobile phone industries are no longer relying on voice services. It is predicted that data services are expected to be a remediation for the declining of average revenue per user (ARPU) in telecommunication market. The purpose of this paper is to find the factors that influence the acceptance/adoption of mobile services. Design/methodology/approach: This paper utilizes quantitative method. Questionnaire were distributed to collect the data. The questionnaires are designed specifically to address the factors related to mobile services. Later on, this paper uses Analytical Hierarchical Process (AHP) and Cluster analysis to identify the factors that influence the adoption of mobile services. Findings: The results show that ease of use and usefulness are top two factors that influence the adoption of mobile services. Research limitations/implications: The sample for the study is relatively small. For future research, the sample should be increased. The data collection should also look into different devices and operating system of mobile phones to find if there is any significant difference in adopting mobile services. Practical implications: This framework is expected to be able to help the mobile services provider to design better mobile services that can attract users to adopt them

Technology Forecasting for Wireless Communication

Wireless communications technologies have undergone rapid changes over the last 30 years from analog approaches to digital-based systems. These technologies have improved on many fronts including bandwidth, range, and power requirements. Development of new telecommunications technologies is critical. It requires many years of efforts. In order to be competitive, it is critical to establish a roadmap of future technologies. This paper presents a framework to characterize, assess and forecast the wireless communication technologies. A DEA-based methodology was used for predicting the state-of-the-art in future wireless communications technologies

Exploring Linkage of Quality Management to Innovation

In many business sectors today, focus on quality as a competitive tool is being replaced by a focus on innovation. Research exploring connections between quality management, innovation, and company performance suggests that quality is ‘necessary but insufficient’ in business today. In short, managers need to know how to adapt their quality management practices to achieve innovation performance in addition to quality performance. West coast U.S. forest products manufacturers were surveyed about their quality management practices and performance with respect to both quality and innovation. Results were analyzed to identify two categories of high-performing firms: those achieving primarily quality outcomes and those achieving both quality and innovation outcomes. Executives from firms in each category were interviewed to provide detail on management practices. Results suggest firms must first change how they view innovation; firms that were primarily focused on quality rather than innovation viewed innovation as an end rather than the means to some other business goal. The most significant finding is related to how firms interact with customers – firms focused on innovation proactively seek to identify and meet customers’ needs whereas quality-focused firms primarily emphasize reacting to customer complaints. Findings suggest several areas where managers can focus to improve innovation performance

Opening the Door to Breakthroughs that Address Strategic Organizational Needs: Applying Technology Roadmapping Tools and Techniques at an Electric Utility

Over the course of a decade, the Bonneville Power Administration\u27s Technology Innovation Office has developed a roadmapping approach that captures business challenges and opportunities critical to the agency, links these with barriers to success, and connects these with technical solutions and research questions. Senior leaders from across the agency establish strategic goals and objectives, and international technical subject matter experts are convened to articulate technology-associated paths to achieve these. Each roadmap captures insights from diverse experts in highly collaborative environments and focuses them on critically important topics. These roadmaps are an important element in the Bonneville Power Administration\u27s strategic approach to technology research and development

Monitoring and Anticipating the Convergence of Lighting Control Strategies

IPC co-classification in conjunction with cross impact analysis has been used to identify technology convergence and its strength over specific period of time. In this study, we tried to monitor the convergence of lighting control strategies which yields energy efficiency from 2009 to 2013. Also, we tried to anticipate the trend of impacts for near future. We take new approach to utilize detailed information including rate of change of co-classified patents, number of patents in each class, and convergence strength between two classes over time and build possible scenarios to anticipate the convergence trend. In our new approach, convergence strength rate of change is considered along side with two other parameters to understand the reason of rise or decline in convergence strength and possibility of its change in the future. With recent trends considering lighting as a system rather than group of components, our approach would help to anticipate the convergence trends of the controlling strategies including time control, presence control, illuminance control and so forth as a controlling system

Benchmarking of Technology Roadmapping Process in Energy Sector: A Literature Review

Technology roadmapping (TRM) has been recognized as an effective and flexible technology planning tool to assist company, industry, or nations to layout their strategic technology needs and align their vision and strategic objectives. Technology roadmap have been developed into various types and formats, by means of generic or customized processes, to suit specific organizational requirement in different industry settings such as energy sector. However, evaluating the effectiveness of roadmapping process remains a critical issue for roadmap updating and improvement. This paper proposes a benchmark approach based on a literature review to help develop a comparative model with required checklists

Technology Readiness Levels Improving R&D Management: A Grounded Theory Analysis

The strategic management of an organization is well understood and there are several different frameworks and methodologies available to develop and implement strategies that will put the organization on the right track and make it more competitive. Nevertheless, the ability to manage technology and to assess and choose the right technologies to be used and to be invested in is not that well understood and developed. The technology readiness is important to be measured and managed, so that organizations have the chance to make better decisions on which technologies to rely on and when. Technology Readiness Levels (TRL) is a method developed by NASA nearly 40 years ago and it has been proven to help organizations to manage their technologies. The aim of this paper is to understand, in more details, how TRL can help organizations and what are the potential implications of using it to the betterment of R&D management. Interviews were conducted with technology managers and researchers. Grounded theory was used to analyze the data, and the results indicate that TRLs can improve R&D management. Some hypotheses were formulated and should be confirmed or refuted by future research

Forecasting OLED TV Technology Using Bibliometrics and Fisher-Pry Diffusion Model

The market of flat panel displays is experiencing rapid growth with the advancement of digital technologies in broadcasting service. The next challenge of LCD is OLED in TV market. The study attempts to investigate the trends in advanced or emerging technologies by determining their technology diffusion rates due to the lack of experiential data. With the development of information and communication technology, one of the recent methods to assist in technology forecasting is data mining in bibliometric or textual data from various sources such as patents, journals, and research awards. The information extracted from diverse sources can be employed in technology diffusion models such as Fisher-Pry where emerging technologies substitute older ones. The study uses web of science and compendex for bibliometric analysis to forecast the growth of next-generation OLED technologies based on the analogous growths of LCD technologies

A Hierarchical Decision Model (HDM) for Exploring the Adoption of Electronic Health Records

Information systems have been making a noticeable entrance in the healthcare, although their adoption has been slow. This paper examines the factors influencing the electronic healthcare records (EHR) adoption by modeling behavioral intention of physicians towards EHR adoption. Three main criteria: perceived usefulness, perceived ease of use and external factors along with the subcriteria, are studied by the authors. Analytical hierarchical process (AHP) model is tested through the expert judgment quantification of physicians in Portland metro area. The results showed high importance of the Perceived Ease of Use criteria on the behavioral intensions of physicians towards EHR adoption. Search ability and user interface - subcriteria of Perceived Ease of Use had some of the highest values. Another important subcriterion in the analysis under External Factors Criteria was Cost. None of the criteria evaluated could be considered unimportant, i.e. having really low values after the analysis. The results indicate that EHR should be mandatory in terms of reducing of time spent and errors, improving the outcomes and productivity and in terms of optimum patient treatment. © 2016 Portland International Conference on Management of Engineering and Technology, Inc

The Use of Readiness Assessment for Big Data Projects

Big data projects, including smart-city-related big data projects, are facing an alarmingly high percentage of failure. The reasons behind this phenomenon and the lessons learned from it are well researched. However, big data projects are still failing, as there is a lack of effective models to leverage the lessons learned from previous projects to evaluate an organization’s readiness for a big data project. In this paper, the authors introduce a novel readiness assessment model. This model leverages the lessons learned from previous projects and the experience of experts to be better prepared for an upcoming smart-city-related big data project. Cities can use the model to evaluate their readiness for this type of project in a structured and comprehensive way that will allow for higher chances of conducting a successful big data project. To develop the model, hierarchical decision modeling (HDM) and expert judgment quantification were used to provide the categorization and relative ranking of factors that influence smart-city-related big data projects. HDM is an effective way to understand the relationship between multiple factors and allows for expert panels to prioritize those factors. Moreover, desirability functions were used to extend the understanding of the factors’ dynamics and what needs to be done to better prepare for the challenges associated with each factor. Finally, the model was tested by applying it to several smart-city-related big data projects to show its value. This research highlights the importance of readiness assessment for conducting big data projects and provides a readiness assessment model that cities can use to prepare for an upcoming big data project