Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results

Knowledge Requirements, Gaps and Learning Responses in Smart Grid Adoption: An Exploratory Study in U.S. Electric Utility Industry

The U.S. electric utility industry is facing a number of challenges today, including aging infrastructure, growing customer demand, CO2 emissions, and increased vulnerability to overloads and outages. Utilities are under greater regulatory, societal and consumer pressure to provide a more reliable and efficient power supply and reduce its carbon footprint. In response, utilities are investing in smart grid technologies. Despite various definitions of smart grid, it is characterized by employing a set of sophisticated sensing, processing and communicating digital technologies to enable a more observable, controllable, and automated power supply. Yet, the adoption of smart grid technologies presents significant knowledge challenges to electric utilities. This study aims to advance the understanding of IT knowledge challenges in smart grid adoption by focusing on three research questions: 1) What knowledge requirements are critical for smart grid adoption? 2) What knowledge gaps are utilities facing with smart grid adoption? How do utilities vary in the level of knowledge gaps? 3) How do utilities overcome knowledge gaps through learning? How do utilities vary in the learning choices? This study adopts a qualitative approach using data from 20 utility interviews and secondary information to address the above questions. The analysis indicates four broad areas of knowledge requirements, which are smart grid technology and vendor selection, smart grid deployment and integration, big data, and customer management. The data also reveals several knowledge gaps faced by utilities in these four areas, and confirms that utilities vary in the level of knowledge gaps, which depends on a mix of factors including prior experience, IT sophistication, service territory characteristics, size, ownership form, regulatory support and support from external organizations. The data further indicates several learning practices that are commonly adopted by utilities to overcome the knowledge gaps in smart grid adoption. It is also determined that utilities vary in the configuration of these practices, and the scale and format of many practices. The variance in learning responses is jointly determined by level of knowledge gaps, knowledge relatedness, size, risk-averse culture and top management support. This study has both research and practical implications. Theoretically, it enriches IT adoption, broader IS research and organizational learning literature in several ways. From the practical perspective, it also has valuable implications for utilities, regulators and other regulated industries and economies

Using big data for customer centric marketing

This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe

Big Data in the Oil and Gas Industry: A Promising Courtship

The energy industry remains one of the highest money-producing and investment industries in the world. The United States’ own economic stability depends greatly on the stability of oil and gas prices. Various factors affect the amount of money that will continue to be invested in producing oil. A main disadvantage to the oil and gas industry is its lack of technological adaptation. This weakens the industry because the surest measures are not currently being taken to produce oil in optimally efficient, safe, and cost-effective ways. Big data has gained global recognition as an opportunity to gather large volumes of information in real-time and translate data sets into actionable insights. In a low commodity price environment, saving time, reducing costs, and improving safety are crucial outcomes that can be realized using machine learning in oil and gas operations. Big data provides the opportunity to use unsupervised learning. For example, with this approach, engineers can predict oil wells’ optimal barrels of production given the completion data in a specific area. However, a caveat to utilizing big data in the oil and gas industry is that there simply is neither enough physical data nor data velocity in the industry to be properly referred to as “big data.” Big data, as it develops, will nonetheless significantly change the energy business in the future, as it already has in various other industries.Petroleum and Geosystems Engineerin

Business intelligence in the electrical power industry

Nowadays, the electrical power industry has gained tremendous interest from both entrepreneurs and researchers due to its essential roles in everyday life. However, the current sources for generating electricity are astonishing decreasing, which leads to more challenges for the power industry. Based on the viewpoint of sustainable development, the solution should maintain three layers of economically, ecologically, and society; simultaneously, support business decision-making, increases organizational productivity and operational energy efficiency. In the smart and innovative technology context, business intelligence solution is considered as a potential option in the data-rich environment, which is still witnessed disjointed theoretical progress. Therefore, this study aimed to conduct a systematic literature review and build a body of knowledge related to business intelligence in the electrical power sector. The author also built an integrative framework displaying linkages between antecedents and outcomes of business intelligence in the electrical power industry. Finally, the paper depicted the underexplored areas of the literature and shed light on the research objectives in terms of theoretical and practical implications

The relationship between the use of artificial intelligence and the customer experience in the power industry

The literature on customer experience and artificial intelligence is extensive, but there is a gap in the power industry regarding these issues. This thesis aims to assess if there is a connection between artificial intelligence usage and the customer experience in the industry. An index that indicates several technologies 'existence or absence in each of the forty companies examined was developed. There we reno significant effects of AIan dCX on the power industry, but the null hypothesis is not accepted since it was not proved that there is no relationship. It might exist, but the current study missed it

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective