The fourth industrial revolution is upon us, and entire industries are seeking to reap the benefits resulting from the use of the technologies that the revolution brings. Waste management is one of those industries. The rapid growth of the human population causes more consumer goods to be produced every day. This underlines the importance of effective and efficient waste management. The Internet of Things (IoT) has proven to be a useful tool to increase the efficiency of waste collection and while many waste management organizations are beginning to adopt these solutions, many are struggling to fully adopt and implement the technology. It is still unclear, due to lack of research, what factors are hindering the adoption and implementation of IoT technology in these organizations and how the process can be improved. The research scope of this study was three-sided: Industry specific, organizational, and technological. The focus was set on the waste management industry, and the intra-organizational barriers that hindered the adoption and implementation process of IoT-powered fullness sensors. The core problems at hand were identifying what the intra-organizational IoT adoption barriers are, what are the most influential barriers, what mitigation strategies can be employed to mitigate these barriers and how it can all be illustrated within an IoT adoption and implementation process framework. The main research question formulated to answer these problems is: How can the adoption and implementation process of IoT-powered fullness sensors in waste management be improved? Four sub-research questions were formulated that held partial information which were needed to answer the main question. The overarching structure of this research project follows the Design Science Research Methodology (DSRM). DSRM provides a commonly accepted approach which involves a rigorous six step activity process for creating and evaluating an IT artifact intended to solve organizational problems. This methodology was altered to fit this particular study, and thus followed five of the six steps. An analysis of literature was performed to identify the general intra-organizational innovation adoption barriers. An exploratory case study was conducted within a large waste management company in Iceland which recently decided to install IoT-powered container fullness sensors to increase the efficiency of their processes. The case study revealed, through interviews, which of the identified barriers had the most significant effect on their adoption and implementation process. Expert interviews and desk research were used to formulate strategies that organizations could employ to mitigate the most prominent barriers identified. After all the interviews had been transcribed, coded, and categorized, data triangulation was used where data from multiple different interviewees was compared and analyzed. From the results of these research efforts, a framework explaining the IoT adoption and implementation process for waste management was then designed and developed. Expert interviews were again conducted to evaluate the framework and confirm the framework’s theoretical validity, application and expected performance in terms of its set goals and objectives. The results of this research are the identified general intra-organizational innovation adoption factors, the most influential factors affecting IoT adoption and implementation within waste management along with their proposed mitigation strategies as well as a designed IoT adoption and implementation process framework in which these strategies are incorporated. The general intra-organizational innovation adoption factors identified are: Leaders’ attitude towards change, Centralization, Complexity, Formalization, Interconnectedness, Organizational slack, Size, Culture, Degree of risk-taking, End user behavior, Strategic objectives and Uncertainty of business benefits. The following are the three most influential barriers to IoT adoption and implementation within waste management and their proposed mitigation strategies: The first barrier is Uncertainty of business benefits and its proposed mitigation strategies are: Gaining a Proof of Value (PoV) and Incremental scale-up. The second barrier is Strategic objectives and its proposed mitigation strategies is: Using information to gain a competitive advantage. The proposed mitigation strategy for the third barrier, Degree of risk-taking, is Renting with an option to buy. The applicability of the designed framework in a real-life setting is yet to be tested. Future research could involve using the framework and applying it in an actual implementation of IoT fullness sensors in a waste management organization.