Chapter 7 Part 3

Vocabulary flashcards covering the uses of Big Data across organizational hierarchy, Artificial Intelligence functions, AI benefits and drawbacks, and the four general decision-making styles.

Big Data Industry Applications

Key areas where industries utilize big data, specifically for meeting customer needs, improving human resource management practices, and enhancing production efficiency.

Lower Managerial Level Big Data Use

The responsibilities of lower-level managers regarding big data, which include analyzing data, project management, safeguarding data, and presenting data to middle management.

Middle Managerial Level Big Data Use

The responsibilities of middle-level managers regarding big data, which include deciding what data is necessary, project management, and presenting data to executives.

Top Managerial Level Big Data Use

The responsibilities of top-level managers regarding big data, which include making data-driven decisions and strategizing, project management, and influencing others to support data-driven decisions.

Artificial Intelligence's Four Functions

The four main functional categories of AI: Automate (Robotic AI), Analyze (Biometric AI), Advise (Conversational AI), and Anticipate (Algorithmic AI).

Automate (Robotic AI)

An AI function that enables robots to scan their environments, gather new information, respond to the information, and learn.

Analyze (Biometric AI)

An AI function designed to analyze data for identification and information security.

Advise (Conversational AI)

An AI function that reflects computers' ability to facilitate conversations with humans by understanding their questions and providing intelligent responses.

Anticipate (Algorithmic AI)

An AI function that gathers new information and makes predictions about the future.

Benefits of AI

Organizational advantages derived from implementing AI, including lower costs, discovering valuable insights, making organizational processes more efficient, customizing/improving existing products/services, predicting demand, creating new products/services, improving decision-making, increasing revenue, and anticipating customer needs.

AI's Drawbacks

The unique set of challenges and concerns associated with AI, including AI implementation, data issues, cost, weaponizing AI, and job displacement concerns ('Will AI Replace Us?').

Value Orientation

A dimension of decision-making style that indicates whether a manager focuses on task and technical concerns versus people and social concerns.

Tolerance for Ambiguity

A dimension of decision-making style that indicates the extent to which a person has a high or low need for structure and control when facing uncertain situations.

Decision-Making Styles Matrix

A framework mapping four general decision-making styles—Analytical, Conceptual, Directive, and Behavioral—across the axes of tolerance for ambiguity (High to Low) and value orientation (Task & technical concerns to People & social concerns).

Directive Style

A decision-making style characterized by a low tolerance for ambiguity and an orientation toward task and technical concerns.

Analytical Style

A decision-making style characterized by a high tolerance for ambiguity and an orientation toward task and technical concerns.

Conceptual Style

A decision-making style characterized by a high tolerance for ambiguity and an orientation toward people and social concerns.

Behavioral Style

A decision-making style characterized by a low tolerance for ambiguity and an orientation toward people and social concerns.