Technology for Strategic Positioning in Business

Technology for Strategic Positioning

• Performing different activities than rivals, or the same activities in different ways
• Using technology to create novel business approaches that are difficult for competitors to copy
• FreshDirect uses technology to offer a wider selection of fresh goods at lower prices compared to local supermarkets
• FreshDirect operates an efficient warehouse for same-day and next-day deliveries
• FreshDirect buys directly from suppliers, eliminating middlemen and reducing prices
• Consumers are drawn to FreshDirect because of its unique business model and benefits

Example - 2: Automotive Industry

• The introduction of electric cars has impacted the automotive market with a shift towards intensified competition
• Electric vehicle (EV) production requires significant research and development (R&D) investment
• Existing automakers have an advantage due to their established resources and economies of scale
• Hybrid and alternative fuel technologies are becoming increasingly viable substitutes for EVs
• Consumers have more choices in the EV market, which increases their bargaining power
• Suppliers of battery technology have gained significant leverage in the EV market
• Automakers are investing in battery technology and exploring alternative resources to combat supplier bargaining power

Analyzing the Competition Structure

• The boundaries of firms have become more fluid, making it challenging to identify actual competitors
• Consumers use social media to compare firms that serve similar needs or have similar features
• Consumers engage in online word-of-mouth discussions regarding competitor firms
• StockTwits is a microblogging website where users share and discuss stocks
• Users on StockTwits frequently discuss S&P 500 companies
• Users on StockTwits are often impacted by new entrants, government policies, and technological innovations

Data Extraction and Analysis

• Extract data from StockTwits on S&P 500 companies
• Generate a network data of stocks based on their mentions in tweets
• Identify closely connected communities of stocks within the network
• Each community represents a group of competitors
• Unsupervised machine learning techniques (Graph or Network Analysis) are beneficial for community detection

Networks: Examples

• Social networks such as friendship circles
• Networks in medicine, for example infectious disease transmission
• Technological networks like computer networks and power grids
• Business networks including strategic alliances

Network Analysis

• Processing data structured as graphs
• Studying properties of the network
• Identifying central nodes, highly connected components, and overall network shape

Graph

• A graph represents a network with nodes connected by edges
• Nodes can represent people, companies, or other entities
• Edges represent relationships, such as friendships or comparisons

Types of Graphs

• Directed graph: edges have a direction and asymmetry
• Undirected graph: edges have no direction and symmetry
• Edges can be weighted or unweighted
• The weight of an edge represents the strength of the relationship

Graph Representation

• Graphs can be represented as adjacency matrices or lists of pairs of nodes and their attributes

• An adjacency matrix (A) displays the edge weight between two nodes (i, j)

• Example:

                     0   1   1   0   0                Node1   Node2   Weight
                     1   0   0   1   1                 A       B        1
                     1   0   0   0   0
                                                       A       C        1
                     0   1   0   0   1
                                                       …
                     0   1   0   1   0
  

Degree

• Represents the number of edges connected to a node
• In-degree refers to the number of incoming edges in a directed graph
• Out-degree refers to the number of outgoing edges in a directed graph
• Edge weights can be associated with each edge in the graph

Path

• A path in a graph is a sequence of nodes connected by edges
• Shortest path: The path between two nodes with the minimum number of edges (for unweighted graphs) or the minimum sum of edge weights (for weighted graphs)

Connected Components

• A connected graph means there is a path between every pair of nodes
• A fully connected graph means each pair of nodes is connected by an edge
• Fully connected graphs are also called complete graphs

Subgraphs and Communities

• A subgraph is a subset of nodes and edges from a graph
• Subgraphs must include all endpoints of the edge subset
• Communities are subgraphs where nodes within a community are densely connected. They have few connections to the rest of the network

Communities

• Community structure is a prominent feature of networks
• Nodes inside a community share a high degree of connectivity
• Example: On Facebook, you might have different communities: school, college, hometown communities

Modularity

• Modularity is a measure of how well a network can be partitioned into groups compared to a random network
• Community Detection: Identifying sets of nodes with strong internal connections and few external connections. This can be accomplished using algorithms such as the Louvain Algorithm

Louvain Algorithm

• Iteratively optimizes modularity to detect communities
• Begins by considering each node as its own community
• Each iteration involves two phases:
  • Phase 1: Optimizing modularity through local changes in node-community membership
  • Phase 2: Aggregating identified communities into super-nodes and constructing a new network

Application: Identifying Competition

• Using a network of companies to identify competitors
• Nodes represent companies
• Edges are weighed and undirected, representing the probability of two companies being mentioned together in a tweet--calculated based on the co-occurrence of stocks
• Louvain Community Detection can be used in conjunction with Python's networkx library to identify communities
• Comparing the sectors within communities to those provided in SEC filings can provide insights into how companies are perceived by the market, versus their self-reported sectors

Final Result

• Findings from the analysis of S&P 500 companies on StockTwits reveal discrepancies between how firms categorize their industries and how consumers perceive them
• Firms with strong community connections (Tesla, PayPal, Netflix, and Amazon) are often associated with different sectors than their self-reported classifications
• Communities are dynamic and constantly evolving, reflecting changing market trends and consumer perceptions

This quiz explores how businesses leverage technology to differentiate themselves from competitors and enhance their strategic positioning. It discusses practical examples, such as FreshDirect's innovative approach to online grocery delivery and the impact of electric vehicles in the automotive industry. Test your understanding of these concepts and their implications for competition and business models.