Senior Managers’ Information Behavior in Current Emerging Ubiquitous and Intelligent Computing Environment

Emerging ubiquitous and intelligent information systems, such as the Internet, social computing technologies and artificial intelligence (AI), have facilitated the increasing complexity and dynamism of operational and strategic information in a highly distributed environment. As a result, organizations have been busy seeking approaches and tools to support senior managers in coping with this challenge, from organizational learning to knowledge management, from competitive intelligence to business intelligence, and from management information systems to strategic (executive) information systems. Before embarking on formulating and developing these approaches and tools, senior managers’ informational roles and information behavior should be understood. This paper explores factors influencing and shaping existing senior managers’ information behavior in order to shed light on value-added approaches or technological solutions for supporting and improving informational roles of senior managers. The findings show that information behavior of senior managers is influenced and shaped by a number of factors, mainly the organizational actors and organizational situations, followed by their affective responses and the use of technological tools

DYNAMIC GENERATION OF AN ONTOLOGY-BASED AI SCHEMA FOR CHATBOTS

Strategic investments in Artificial Intelligence may enable companies to gain business advantages. There are challenges in using generic natural language processing (NLP) capabilities with complex products and with content that requires specialized domain-specific terminologies. Darwin Information Typing Architecture (DITA)-generated AI schema can leverage enterprise source code to train bots or any other conversational systems to improve the accuracy levels without any manual intervention. A well-defined AI schema is generated from the DITA source files that contain an ontology framework of Intents, Entities, Dialog nodes, along with child nodes, as a result. The schema can be depicted as a JSON file

Design Requirements for AI-based Services Enriching Legacy Information Systems in Enterprises: A Managerial Perspective

Information systems (IS) have been introduced in enterprises for decades to generate business value. Historically systems that are deeply integrated into business processes and not replaced remain vital assets, and thus become legacy IS (LISs). To secure the future success, enterprises invest in innovative technologies such as artificial intelligence-based services (AIBSs), enriching LISs and assisting employees in the execution of work-related tasks. This study develops design requirements from a managerial perspective by following a mixed-method approach. First, we conducted ten interviews to formulate requirements to design AIBSs. Second, we evaluated their business value using an online survey (N = 101). The results indicate that executives consider design requirements as relevant that create strategic advancements in the short term. With the help of our findings, researchers can better understand where further in-depth studies are needed to refine the requirements. Practitioners can learn how AIBSs generate business value when enriching LISs

AI and the Future of Business Strategists: A Review and Research Agenda

Artificial Intelligence has received increased attention from multiple research disciplines, including strategic management and information systems. Despite such heightened interest, there is a noticeable absence of a comprehensive framework to explain how business strategists work with AI to develop business strategies. This paper develops such a framework to illustrate the process of business strategists working with AI to develop business strategies. We also conducted a systematic literature review of AI in business strategy research and used the developed framework to structure the analysis. From the findings, we reveal which parts of the framework have been studied and which are still in need of further research. In doing so, this study makes important contributions by (1) proposing a comprehensive framework of strategy workers and AI delegation process, (2) identifying the currently reported contributions of AI and business strategy research, and (3) identifying promising venues and critical research questions for future research

Toward a Model Undergraduate Curriculum for the Emerging Business Intelligence and Analytics Discipline

Business intelligence (BI) combined with business analytics (BA) is an increasingly prominent strategic objective for many organizations. As a pedagogical subject, BI/BA is still in its infancy, and, in order for this to mature, we need to develop an undergraduate model BI/BA curriculum. BI/BA as an academic domain is emerging as a hybrid of disciplines, including information systems, statistics, management science, artificial intelligence, computer science, and business practice/theory. Based on IS 2010’s model curriculum constructs (Topi et al., 2010), we explore two curricular options: a BI/BA concentration in a typical IS major and a comprehensive, integrated BI/BA undergraduate major. In support, we present evidence of industry need for BI/BA, review the current state of BI/BA education, and compare anticipated requirements for BI/BA curricula with the IS 2010 model curriculum. For this initial phase of curricular design, we postulate a preliminary set of knowledge areas relevant for BI/BA pedagogy in a multi-disciplinary framework. Then we discuss avenues for integrating these knowledge areas to develop professionally prepared BI/BA specializations at the undergraduate level. We also examine implications for both AACSB and ABET accreditation and describe the next phase of applying the IS 2010 concept structure to BI/BA curriculum development

Competitive Intelligence Capabilities of Social Media Analytics for Value Creation

In recent times, social media has been a major tool where traces of clients’ engagements of a business product or services are kept. The petabytes of daily social media data are utilized to make informed decisions grounded in context. Businesses are tapping into this chunk of data to make intelligent decisions. The use of these Artificial Intelligence (AI) technologies is shown to drive business value. Existing reviews of Social Media Analytics (SMA) use and other digital innovations lack the theorisation of the value created from the use of SMA as a digital transformation of businesses(Matarazzo et al. 2021). Davenport and Ronanki (2018), purports that there are three main business needs that make use AI technology. These are, process automation, cognitive insights and cognitive engagement. Gaining competitive intelligence from social media data has become a market requirement among businesses. Competitive intelligence is “a process that includes collection, analysis, interpretation and dissemination providing strategic information that can be used in a decision making process” (Acharya et al. 2018). Fan and Gordon (2014) indicated that social media analytics produces intelligence that contributes to creating competitive advantages and business value. On this premises, it is interesting to study the competitive intelligence capabilities of SMA and value creation. This study sought to theorise the ways to create business value from the use of SMA. The study seeks to answer the research question: What value is derived from the competitive intelligence capabilities of Social Media analytics. This research will adopt both conceptual analysis and empirical quantitative design to achieve its objectives. A cross-sectional survey research design will be adopted in this study. Managers of the banking and telecommunication companies in Ghana, specifically in Greater Accra region, will be chosen to respond to the survey questionnaire that will be administered. The key informants will be managers of these companies because their experiences, and professional knowledge about SMA use will provide reliable information to this study. The study will use partial least square-based structural equation modelling to evaluate the measurement items. This study is one of the few types of research to investigate the causal relationships between the SMA, competitive intelligence, and value creation. This study contributes to information systems literature by conceptualising the competitive intelligence capabilities of SMA to understand the value that is derived from its use

Design and Implementation of Enterprise Financial Decision Support System Based on Business Intelligence

Purpose: Business intelligence and decision support systems are now recognized as critical enterprise infrastructure. Data is increasingly being used by business enterprises to react to key operational and strategic operations of their customers, markets, and stakeholders.   Theoretical framework: Business intelligence has advanced as the volume of data generated by smart technologies and the Internet has increased exponentially.   Design/Methodology/Approach: When using AI in the business world, privacy concerns arise when sensitive data is transmitted to a third-party vendor who has no connection to the company. Financial decision support data is managed by these AI service providers. Even while it may seem like a drawback, artificial intelligence technology actually has several advantages.   Findings: The results of this research show that a network can improve the financial management of a corporation. Using many applications of AI technology helps bring down the overall cost of operations. When it comes to financial services, both the number of IT workers needed and the number of necessary pieces of hardware (servers) can be reduced, resulting in a marginal drop in capital expenditures.   Research, Practical & Social implications: When it comes to financial services, both the number of IT workers needed and the number of necessary pieces of hardware (servers) can be reduced, resulting in a marginal drop in capital expenditures. It is now simpler and quicker to get your hands on relevant financial information, which should lead to greater efficiency.   Originality/value: With our proposed approach, the accuracy of financial decision support in company increases to 99.84% from 88.94%, while the implementation time and cost are reduced

UNMC AI Task Force Report

In July 2023, University of Nebraska Medical Center and Nebraska Medicine leadership charged a task force with investigating facets of artificial intelligence (AI) in an academic health center setting. What must we know, do and plan for regarding generative artificial intelligence in the domains of enhancing education, research, clinical care, business functions and in combating misinformation/disinformation? Task force members were allocated into five subcommittees to investigate key points to inform strategic planning—Enhance Learning, Enhance Research, Enhance Clinical Care, Enhance Business Function and Combat Dis-/Mis-Information and Bias. This work was aligned with the UNMC Strategic Planning process as a “big rock” for 2023. The task force chairs conducted a landscape analysis of AI at UNMC’s nine peer institutions. The work of this task force paralleled that of other universities this fall: four of the nine peer institutions had charged AI task forces or committees with investigating similar issues. While the task force chairs conducted this analysis, the five subcommittees began exploring the ideal scenarios, potential risks, needed policies, additional areas of exploration and resultant goals for each of their given subject areas. Many themes were consistent across all five of the subcommittees. Each group noted the need for clear policies and protocols for AI usage, communication around UNMC’s goals and efforts relating to AI, education for all university stakeholders who may engage with AI systems and funding to ensure that AI tool adoption is carried out smoothly. In acknowledgment of the robust work that is already being conducted at the various colleges and centers within UNMC, any future AI programs and initiatives should attempt to align with and build upon the current efforts.https://digitalcommons.unmc.edu/unmc_reports/1000/thumbnail.jp

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes