This research analyzed why some university faculty resisted a new software program using a new model of motivation. The new model, called the motivation and acceptance model (MAM), was inspired by the technology acceptance model and the commitment and necessary effort model of motivation. This model was tested on faculty at a college in a large southeastern university who were resisting a new software program called LiveText. This research used regression analysis to determine the relationship between the variables of the MAM: perceived usefulness, perceived organizational support, perceived ease of use, and attitude toward LiveText. The research was conducted during the Spring 2007 semester. The data were analyzed with regression, independent-sample t-tests, and descriptive statistics using SPSS v15. This research demonstrates that the MAM accurately measured the relationship between professors' perceptions and their use of LiveText. The research also suggests that the perceived utility of LiveText and users' attitudes toward LiveText were statistically significant predictors of LiveText use and that perceived ease of use also predicted whether the professors found LiveText useful. Additional research should seek to develop a greater understanding of technology acceptance and employee resistance to innovations using larger sample sizes, a variety of environments and organizations, diverse populations, and different types of technologies and technology-implementation strategies.

The purpose of this research was to examine students' acceptance of the World Wide Web Course Tools (WebCT) online learning system. The Perceived Resources and Technology Acceptance Model (PRATAM) was created based on previous research to address the factors of perceived resources, perceived usefulness, perceived ease of use, attitude toward using, behavioral intention to use and actual system use. The aim for this research was to investigate the critical determinants and provide the causal relationships regarding students' acceptance behaviors when using WebCT. While institutions are expecting to adopt online learning to reach more students, there are still many challenges for institutions to retain students in their online courses. The literature review conducted in this research indicated that the Technology Acceptance Model (TAM) has successfully explained students' behaviors when they use educational information systems. In addition, the additional perceived resources variable in the PRATAM also showed a significant influence on the other belief and intention variables. The study analyzed a total of 115 students responses in two surveys administered during two WebCT based courses taught at a large southeastern public university. The beliefs, attitudes, intentions, and behavioral constructs of PRATAM showed significant goodness-of-fit indices and coefficient of determination after analyzing the data in both surveys. However, the results indicated several exceptions on PRATAM's constructs and causal relationships. First, the path coefficient between perceived resources to behavioral intention to use in both pre-test and post-test were insignificant. Second, the path coefficient between behavioral intention to use and actual system use in pre-test was insignificant. Third, the path coefficient between perceived resources and perceived usefulness in post-test were insignificant. In addition, the research also suggested an additional link between perceived ease of use and behavioral intention to use at the pre-test data. Overall, this research validated the influences of PRATAM's constructs factors to students' acceptance behaviors toward WebCT. The findings of this research could provide a guideline for future implementations of online learning systems in higher education.

Yes / Time, cost and risk are the main elements that effect the operating margin of the oil and gas companies due to Turnaround Maintenance (TAM). Turnaround Maintenance (TAM) is a methodology for the total shutdown of plant facilities during a pre-defined period to execute inspection actions, replacement and repairs according to Scope of Work (SoW). This paper presents a new methodology for improving TAM scheduling of oil and gas plants. The methodology includes four stages: removing Non-critical Equipment (NE) from reactive maintenance to proactive maintenance, risk-based inspection of Critical Static Equipment (CSE), risk-based failure of Critical Rotating Equipment (CRE), and application of failure distributions. The results from improving TAM scheduling is associated with decreasing duration and increasing interval between TAM leading to improved availability, reliability, operation and maintenance costs and safety risks. The paper presents findings from the TAM model application. The methodology is fairly generic in its approach and can also be adapted for implementation in other oil and gas industries that operate under similar harsh conditions.

Social media is a fast growing platform when it comes to marketing. It is, however, used to a greater extent by business-to-consumer (B2C) companies than by business-to-business (B2B) companies. Because of this there has been considerably less research done concerning marketing on social media for B2B, compared to B2C.  This study explores why social media marketing is less common in B2B than B2C. This was done by conducting eleven one-on-one interviews with people who work at different B2B companies, and who have influence over their respective company’s marketing process. To analyse and evaluate the findings of this study the Technology Acceptance Model (TAM) was used. By identifying the external variables that deter B2B companies from using social media the perceived usefulness and ease of use were analysed in order to explore what affects the difference in social media acceptance for marketing between B2B and B2C companies.  The findings of this study show that the perceived ease of use is the dominant factor that deters B2B companies from using social media for marketing purposes. This is however not the reason for the difference between B2B and B2C. The difference can instead be explained by a more negative perception of the usefulness of marketing on social media. This negative perception is a result of the characteristics of social media, the industry the company is operating within, as well as the company’s products and customers. / Sociala medier är en snabbt växande plattform när det kommer till marknadsföring. Det används dock i större utsträckning av business-to-consumer (B2C) företag än av business-to-business (B2B) företag. På grund av detta har det gjorts betydligt mindre forskning gällande marknadsföring på sociala medier för B2B än vad som har gjorts för B2C. Denna studie utforskar varför marknadsföring på sociala medier är mindre vanligt i B2B än B2C. Detta har gjorts genom att genomföra 11 intervjuer, öga-mot-öga, med personer som arbetar på olika B2B-företag och som har inflytande över sitt respektive företags marknadsföringsprocess. För att analysera och utvärdera resultaten från denna studie användes Technology Acceptance Model (TAM), en modell som mäter acceptansen av olika tekniska plattformar. Genom att identifiera de externa variabler som hindrar B2B-företag från att använda sociala medier analyserades användbarheten och användarvänligheten, som den uppfattas av dem intervjuade, för att undersöka vad som påverkar skillnaden i sociala media acceptans i marknadsföringssyfte mellan B2C- och B2B-företag.  Resultaten av denna studie visar att den upplevda användarvänligheten är den dominerande faktorn som hindrar B2B-företag från att använda sociala medier för marknadsföringssyften. Detta är dock inte orsaken till skillnaden mellan B2B och B2C. Skillnaden kan istället förklaras av en negativare uppfattning av användbarheten av marknadsföring på sociala medier. Denna negativa uppfattning är ett resultat av egenskaperna hos sociala medier, den bransch som företaget är verksam inom, samt företagets produkter och kunder.

This study aims to investigate college students' security awareness of using social media in sharing information. The two theories that have guided this study are the theory of planned behavior (TPB) and the technology acceptance model (TAM). Data was collected from both undergraduate and graduate students from the University of North Texas (UNT) in Denton. The total responses included 380 students from different majors with 291 valid responses for data analysis; The structural equation model (SEM) Lavaan package was used to find out the best fit of the model. A diagonally weighted least squares (DWLS) was used to model the variables as ordinal in this study's analysis as ordinal data made the model fit substantially. The study found that 6 factors: attitude (AB), subjective norm (SN), perceived behavior control (PBC), perceived usefulness (PU), perceived risks (PR), and security awareness (SA) influenced behavior intention (BI). Also, I found that AB was influenced by PR and SA, as well as SN influenced by SA. Self-efficacy (SE) influenced PBC. On the other hand, the study found that controllability (C) did not influence PBC; perhaps, an individual's skills do not interact with social media security settings. Perceived ease of use (PEOU) did not influence BI; perhaps this occurred because of an individual's inability to prevent his or her information from being disclosed in the future, even if they had taken the right precautions. This study contributed to literature on understanding the nature of information sharing among college students on social media. The results may help college security professionals to evaluate or revise the rules and policies regarding cybersecurity and privacy.

tRNA genes are highly transcribed and perform one of the most fundamental cellular functions. Although a universal pattern observed across all three domains of life is that highly transcribed genes tend to evolve slowly, tRNA genes have been shown previously to evolve rapidly. This rapid sequence evolution could result from relaxed selection, increased mutation rate, or a combination of both. Here, we use mutation-accumulation line sequencing data to show that tRNA genes accumulate more mutations than other gene types. Our results indicate that this elevated mutation rate is a consequence of both elevated transcription-associated mutagenesis and a lack of transcription-coupled repair in tRNA genes. We also identify the gene MSH2 as being involved in transcription-coupled repair.

Gas plants consist of several pieces of both critical static and rotating equipment, which operate continuously under severe operating conditions. These pieces of equipment are permanently subjected to be inspected and maintained during total shutdown of plant facilities to execute Turnaround Maintenance (TAM) event. The TAM is the largest maintenance activities used in most oil and gas companies in terms of both cost and time. Oil and gas companies have suffered losses in the production and enormity in the TAM cost due to duration and interval of TAM which have randomly estimated without taking the size and age of plants into account. Sirte Oil Company (SOC) was a good example and used as a reference point for other gas plants to achieve the aim of this thesis associated with optimising TAM scheduling for gas plants (decreasing duration and increasing interval of TAM) by implementing the TAM model. The contribution of this research is in developing the TAM model, consisting of four stages, which is broken down into four main stages: First stage; removing Non-critical pieces of Equipment (NEs) from the Scope of Work (SoW) of TAM to proactive maintenance strategies. Second stage; selecting Critical Static pieces of Equipment (CSEs) that constitute the highest risk based on Risk-Based Inspection (RBI). Third stage; selecting Critical Rotating pieces of Equipment (CREs) that constitute the highest risk based on Risk-Based Failure (RBF). Fourth stage; defining the optimum duration and interval of TAM based on Failure Distributions (FDs). Consequently, the TAM model developed in this study provides a novelty in the TAM event and decision making process. This is basically about optimisation of TAM scheduling in the medium and long-term, characterized by decreasing duration and increasing interval of TAM based on both CSEs and CREs to achieve the TAM model results. The result is the reduction in TAM cost and production losses, and the improvement in reliability and availability requirements of gas plants according to the residual life of critical equipment and operating conditions. To ensure reliability and consistency of the TAM model, it was validated with three Libya-plants SOC and data from three published case studies. The results from the validation of the TAM model are consistent with the real duration and interval of TAM in most plants SOC. The research concludes that the developed TAM model is a reliable and applicable tool to assist decision-makers in the estimation of TAM scheduling for any a processing plant.

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Service reliability is one of the main factors influencing public transport level of service and, thus, passengers’ satisfaction. Public transport services are subject to various sources of uncertainty related to traffic conditions, public transport operations and passenger demand. Passengers are able to form their perception of trip attributes and service reliability through accumulating experiences of repetitive travel choices. Perceived service reliability can be improved either by increasing the ground-truth service reliability (e.g. introduce exclusive bus lanes, control strategies etc.) or by providing real time information (RTI) to passengers. However, RTI prediction schemes might not be perfectly accurate and thus, passengers might be able to account for the reliability of the provided information as well. The learning mechanism of individuals becomes, as a result, an important component in Dynamic Transit Assignment Models (DTAM) which enables accounting for how perceived reliability of service and the provided information evolves, through iterative network loading. This thesis provided the modeling framework for passengers’ perception of reliability and its effects on decision making with respect to path choice. Within-day effect is represented through the incorporation of scheduling constraints, while passengers’ learning mechanism accounts for updates in their expectations and the perceived level of information credibility in the day-to-day context. The proposed model was applied to Stockholm’s rapid transit network which was simulated in BusMezzo, an agent-based public transport assignment model. The application used the real-world timetables, vehicle schedules and RTI prediction scheme. Passengers’ learning function was analysed under various specifications which corresponded to different levels of adaptation. The results highlight the importance of capturing service uncertainty and the credibility associated with alternative information sources, while they stress the need for empirical estimation and validation of the proposed model. This study also provides the framework for future evaluation of measures which aim to improve service reliability.