Energy management and carbon accounting schemes are increasingly being adopted as a corporate response to climate change. These schemes often demand the setting of ambitious targets for the reduction of corporate greenhouse gas emissions. There is however only limited empirical insight in the companies’ target setting process and the auditing practice of certifying agencies that evaluate ambition levels of greenhouse gas reduction targets. We studied the target setting process of firms participating in the CO2 Performance Ladder. The CO2 Performance Ladder is a new certifiable scheme for energy management and carbon accounting that is used as a tool for green public procurement in the Netherlands. This study aimed at answering the question ‘to what extent does the current target setting process in the CO2 Performance Ladder lead to ambitious CO2 emission reduction goals?’. The research methods were interviews with relevant stakeholders (auditors, companies and consultants), document reviews of the certification scheme, and an analysis of corporate target levels for the reduction of CO2 emissions. The research findings showed that several certification requirements for target setting for the reduction of CO2 emissions were interpreted differently by the various actors and that the conformity checks by the auditors did not include a full assessment of all certification requirements. The research results also indicated that corporate CO2 emission reduction targets were not very ambitious. The analysis of the target setting process revealed that there was a semi-structured bottom-up auditing practice for evaluating the corporate CO2 emission reduction targets, but the final assessment whether target levels were sufficiently ambitious were rather loose. The main conclusion is that the current target setting process in the CO2 Performance Ladder did not necessarily lead to establishing the most ambitious goals for CO2 emission reduction. This process and the tools to assess the ambition level of the CO2 emission reduction targets need further improvement in order to maintain the CO2 Performance Ladder as a valid tool for green public procurement.
During the past two decades the implementation and adoption of information technology has rapidly increased. As a consequence the way businesses operate has changed dramatically. For example, the amount of data has grown exponentially. Companies are looking for ways to use this data to add value to their business. This has implications for the manner in which (financial) governance needs to be organized. The main purpose of this study is to obtain insight in the changing role of controllers in order to add value to the business by means of data analytics. To answer the research question a literature study was performed to establish a theoretical foundation concerning data analytics and its potential use. Second, nineteen interviews were conducted with controllers, data scientists and academics in the financial domain. Thirdly, a focus group with experts was organized in which additional data were gathered. Based on the literature study and the participants responses it is clear that the challenge of the data explosion consist of converting data into information, knowledge and meaningful insights to support decision-making processes. Performing data analyses enables the controller to support rational decision making to complement the intuitive decision making by (senior) management. In this way, the controller has the opportunity to be in the lead of the information provision within an organization. However, controllers need to have more advanced data science and statistic competences to be able to provide management with effective analysis. Specifically, we found that an important skill regarding statistics is the visualization and communication of statistical analysis. This is needed for controllers in order to grow in their role as business partner..
Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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