This research develops a combined qualitative and quantitative activity based analysis methodologies framework for integrated fixed activity chaining schedules (IFACS). What is central to my dissertation is the extent to which the type, sequence, duration, and timing of fixed activities influence trip chaining. The goal is to improve the practice of travel behavior forecasting by modeling the activity patterns and chaining behavior of individuals with restricted schedules. Cluster analysis methods are applied to narrow down the endless possibilities of individual trip decision-making into a manageable group. Then space-time prism concepts are applied to locate unique activity travel patterns. Qualitative examples show that aggregate and individual trip-chaining behaviors are not easy to model. Informed by the qualitative analysis results, the framework incorporates both discrete choice and time-to-event models as quantitative analysis tools. The quantitative models cover two levels of chaining: trip link and whole journey. Logit models are used to analyze the choice to make a chained link right after the completion of an activity, and the decision to conduct a chained journey. Poisson models are used to examine the decision to make a number of chained links on a journey. Hazard-based duration models are used to analyze time of chaining events by studying the duration of a chained journey, then the duration of time elapsed till a chained link is made right after a fixed activity with the goal of defining a time threshold to the occurrence of trip chaining. Integrating results from qualitative and quantitative methods leads to a better understanding of how people make their trip-chaining and travel behavior decisions. Data from the 1998 Mobidrive six-week travel diary survey is used in the analyses. This framework focuses on the effects of five types of fixed activities on trip-chaining behavior: work, work-related, school, further education, and club/group meetings. Understanding factors influencing a person's decision to participate in a trip chain allows motivations behind trip-chaining behavior to become more tangible. One of the highlights of the analysis results of the modeling framework is discovering that the chaining behavior of individuals is influenced in different ways among different population subgroups.

The purpose of this study is to continue development of a benchmarking model to help companies assess their technical maturity when it comes to adopting Continuous Integration, Continuous Delivery and DevOps in their organization. The goal of the research is to assess how to improve the quality of qualitative models. Which conclusions can be drawn from comparing companies using benchmark and to assess which actions are the most effective to take to reach higher Continuous Integration, Continuous Delivery and DevOps maturity. The benchmark consisted of a questioner of two hundred statements that were answered for level of agreement with a current situation analysis and an ought-to-be analysis to be able to draw conclusions from the possible discrepancy between these categories. The questioner was answered during an interview study with chosen clients. Conclusions drawn from this study were that a lot can be done to improve the quality of qualitative models for examining Continuous Integration, Continuous Delivery and DevOps maturity. Different actions are necessary but the most important seems to be to ask open ended questions as well ass questions about different aspects of the same problem to promote discussion. It was also showed to be important to peer review the questions in the interview material beforehand to increase quality. The study also showed that it is possible to see trends in Continuous Integration, Continuous Delivery and DevOps maturity when comparing qualitative results for research subjects. The study showed that the most effective method for increasing Continuous Integration, Continuous Delivery and DevOps maturity is to use extensive automated testing suites that covers all testing disciplines. / Syftet med studien är att vidareutveckla ett benchmarkingverktyg för att hjälpa företag att bedöma sin tekniska mognad när det gäller att anta Continuous Integration, Continuous Delivery och DevOps i sin organisation. Målet med forskningen är att bedöma hur man kan förbättra kvaliteten på kvalitativa modeller för att mäta detta, samt vilka slutsatser som kan dras av att jämföra företags resultat som nyttjat studien. Samt att undersöka vilka åtgärder som är effektivast att ta för att nå en högre mognadsgrad inom Continuous Integration, Continuous Delivery och DevOps. Benchmarken bestod av ett frågebatteri av tvåhundra påståenden som besvarades av kunden i hur mycket de instämde till ett påstående. Resultatet samanställdes till en aktuell nulägesanalys och en börlägesanalys, med målet att dra slutsatser i vilka skillnaden som fanns mellan dessa två kategorier. Kunden besvarade frågebatteriet under en intervjustudie med utvalda anställda. Slutsatser som härrör från denna studie var att mycket kan göras för att förbättra kvaliteten på kvalitativa modeller för att undersöka Continuous Integration, Continuous Delivery och DevOps mognadsgrad. Olika åtgärder är möjliga, men det viktigaste förefaller vara att fråga öppna frågor för att främja diskussion samt att ställa frågor om olika aspekter av samma problem. Samt att opponera frågorna internt i intervjuundersökningen innan det utförs hos en kund, för att öka kvaliteten. Studien visade också att det även är möjligt att se trender i Continuous Integration, Continuous Delivery och DevOps mognad hos deltagarna när man jämför de kvalitativa resultaten. Studien visade att de mest effektiva metoderna för att öka Continuous Integration, Continuous Delivery och DevOps mognadsgrad är att använda omfattande automatiserade testsviter för samtliga testmetoder.

The purpose of this research is to investigate the effect of DevOps adoption on organizational agility. The first chapter establishes through literature review and a pilot study, a theoretical definition of DevOps and identifies through survey data, the key DevOps adoption capabilities. The definition and key capabilities identified during the pilot study establishes the foundation through which a subsequent study is performed. The second chapter details an examination of the effects of DevOps adoption capability on organizational agility. Through capability theory, we propose that DevOps adoption capability positively affects organizational agility. We also propose that the capabilities of collaboration/communication, continuous monitoring, measurement and automation positively affect DevOps adoption capability and likewise; the capabilities of responsiveness, competency, flexibility, and quickness affect organizational agility capability. We test our model with survey data collected from 333 respondents and find that our hypotheses are supported and that DevOps adoption capability has a positive effect on organizational agility capability. / Business Administration/Management Information Systems