MAIO

Yes

- Raw materials
- Land
- Labor
- Capital

Yes

Yes

The management of a firm makes a policy choice to remain or become a low-cost producer. This limits the scope of variation products.

The firm will choose relatively simple, standardized products that sell for relatively low prices compared to firms that follow other policies.

Firms that seek to remain or become low-cost producer make cost reduction the focus of management and employees

Product Features
Timing
Product Mix
Links with Other Firms
Location and Presence
Sources of Product Differentiation
Reputation

- Quality
- Design
- Color
- Style
- Trademarks
- Patented feature

These characteristics match the preferences of certain customers

AI

Like Microsoft, have a lot of differents products

A first mover introduces certain product or service for the first time.

 It invests heavily on R&D and advertising

 A second mover responds to the first mover’s competitive actions, typically through imitation of product features

A late mover responds after considerable time has elapsed after the first mover’s action and the second mover’s response.

- Suppliers
- Distributors and retailers
- Customers

The physical location can be a source of product differentiation for a firm

- Product development with sales
- Product development with financing
- Sales with distribution
- Sales with financing

One of the most influential sources of product differentiation is a firm‘s reputation

▪ Reputation with customers
▪ Reputation with suppliers
▪ Brand name
▪ Perceptions of product quality, durability, and reliability

Yes

“Optimal reputation cheating” describes a level of reputation where the firm can reduce product quality without damaging that level of reputatio

Economists have predicted that under some circumstances, performing at a lower level than what customers expect, can maximize the firm's economic performance

In practice, optimal reputation cheating can have disastrous consequences for the firm

See picture

AI algorithms may have the potential for dramatically expanding the scale, scope, and speed of strategic analysis through its capacity for interacting with the environment and generating data

External Analysis

Industry Analysis

Internal Analysis

POLITICAL

ECONOMIC

SOCIETAL

TECHNOLOGICAL

LEAGAL

ENVIRONMENTAL

Infer ”political climate” across regions
through AI-based text analysis tools

Identify patterns in news outlets, legislative
debates and online political discourse

Predict outcomes of elections (e.g., Coletto et al., 2015),
policy changes (e.g., Chan & Zhong, 2018),
and political bias and conflicts (Biessmann et al., 2016)

Measure economic trends, such as economic growth, the onset of economic
recessions (e.g., Wu et al., 2020), increasing poverty (Kshirsagar et al., 2017),
and bankruptcies (Cielen, Peeters, & Vanhoof, 2004)

Predict stock returns and thereby make better investment
decisions (Avramov, Cheng, & Metzker, 2019)

Estimate systemic financial risks (e.g., Kou et al., 2019)

“map the contours of cultural fields, classify cultural elements
and trace the evolution of culture over time” (Bail, 2014)

Enable the systematic measurement of culture and modeling of its
evolution within organizations and social groups (Doyle et al., 2017)

ssist companies in monitoring technological developments
and anticipate any relevant technological changes

Identify patterns of technology development by sifting through massive
amounts of patent or publication data (e.g., Lee et al., 2018)

Construct “knowledge profiles” of their industry and major
competitors (Suominen, Toivanen, Seppänen, 2017).

Support firm internal collection and processing of legal data,
potentially reducing overall legal expenditures
(e.g., Yousfi-Monod, Farzindar, and Lapalme 2010)

Automate financial compliance monitoring and
regulation (e.g., Treleaven & Batrinca, 2017)

Research into whether hydrogen electrical vehicles may become the
dominant means of transportation for consumers (Ranaei et al., 2016