MIS 301 • McCombs School of Business • UT Austin

IT Management Study Guide

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MIS 301 • Key Concepts

Vocabulary
Reference

Precise, citation-backed definitions drawn from lecture slides and course readings. Use the filters and search to study by chapter.

Moore's Law Ch.10

An empirical observation, first published by Gordon Moore (co-founder of Intel) in 1965, that the number of transistors on an integrated circuit roughly doubles approximately every 18–24 months, yielding exponential improvements in computing performance per dollar. It is not a physical law but became a business plan and expected pace of innovation for the semiconductor industry.

📖 Moore, G.E. (1965). "Cramming more components onto integrated circuits." Electronics, 38(8). | Lecture Slides Ch.10, p.5.

Transistor Ch.10

A semiconductor device that acts as an electronic switch, representing binary data by toggling between two states: on (= 1) and off (= 0). Modern computer chips contain billions of transistors packed together to perform complex computations.

📖 Gallaugher, J. (2023). Information Systems: A Manager's Guide to Harnessing Technology, Ch.6. | Lecture Slides Ch.10, p.10.

Semiconductor Ch.10

A material (most commonly silicon) that conducts electricity under certain conditions and inhibits it under others. Semiconductors form the physical foundation of modern electronics and are used in CPUs, GPUs, memory chips, and virtually all electronic devices.

📖 Gallaugher (2023), Ch.6. | Lecture Slides Ch.10, p.10.

Bit (Binary Digit) Ch.10

The fundamental unit of digital information, taking a value of either 0 or 1. The term "bit" is a portmanteau of "binary digit." Computers represent all data—text, images, video, code—as sequences of bits. Data transfer speeds (internet, Wi-Fi) are measured in bits per second (bps, Mbps, Gbps). Note: lowercase "b" = bits; uppercase "B" = bytes.

📖 Shannon, C.E. (1948). "A mathematical theory of communication." Bell System Technical Journal. | Lecture Slides Ch.10, p.19.

Byte & Storage Units Ch.10

A byte = 8 bits ≈ one keyboard character. Storage is measured in Bytes (capital B): 1 KB ≈ 1,000 bytes (one typewritten page); 1 MB ≈ 1 million bytes (one MP3 ≈ 3 MB); 1 GB ≈ 1 billion bytes (one DVD ≈ 4.7 GB); 1 TB ≈ 1 trillion bytes (Library of Congress ≈ 20 TB); 1 PB ≈ 1 quadrillion bytes; 1 EB ≈ 1 sextillion bytes.

📖 Gallaugher (2023), Ch.6. | Lecture Slides Ch.10, p.12.

CPU (Central Processing Unit) Ch.10

The primary processor that executes instructions in a computer. Using the "data kitchen" analogy: the CPU is the chef that runs the computer by following software (its recipes). The CPU uses main memory (RAM) as its active workspace and stores completed work or future-needed data in long-term storage.

📖 Gallaugher (2023), Ch.6. | Lecture Slides Ch.10, p.9.

Main Memory (RAM) Ch.10

Volatile computer memory that serves as the CPU's active workspace, temporarily holding data and instructions currently being processed. In the "data kitchen" analogy, RAM is the counter space where the chef prepares ingredients. RAM is cleared when power is removed—unlike storage, which persists. Running too many applications simultaneously fills RAM and slows performance.

📖 Gallaugher (2023), Ch.6. | Lecture Slides Ch.10, p.9.

Price Elasticity of Demand (for Technology) Ch.10

An economic measure of how sensitively consumer demand responds to a change in price: Elasticity = % change in quantity demanded Ă· % change in price. Demand for computing technology is highly elastic—as prices fall, consumers find entirely new uses and demand far more. Example: storage prices fell from $399 for 5 GB (iPod, 2001) to free for 5 GB (iCloud, 2011), and total data storage consumed by consumers grew by orders of magnitude.

📖 Marshall, A. (1890). Principles of Economics (original elasticity concept). | Gallaugher (2023), Ch.6. | Lecture Slides Ch.10, p.6.

Multicore Processor Ch.10

A microprocessor containing two or more independent calculating cores on a single piece of silicon. A group of multicore chips typically outperforms a single fast chip on parallel workloads (tasks that can run simultaneously), while running cooler and drawing less power. Multicore processors can also run legacy software written for single-core chips by using only one core at a time.

📖 Gallaugher (2023), Ch.6. | Lecture Slides Ch.10, p.16.

GPU & ASICs Ch.10

A GPU (Graphics Processing Unit) is a chip originally designed for rendering computer graphics, requiring the simultaneous execution of thousands of smaller calculations (high parallelism). This massively parallel architecture turned out to be ideal for AI/ML training. The general category for purpose-built chips optimized for specific tasks is Application-Specific Integrated Circuits (ASICs). Nvidia's GPUs are the dominant ASIC for AI workloads.

📖 Gallaugher (2023), Ch.6. | Lecture Slides Ch.10, p.16.

Fab (Semiconductor Fabrication Plant) Ch.10

A manufacturing facility that produces semiconductor chips. Fabs cost $20+ billion to build, consume millions of gallons of ultra-purified water per day, and require massive power infrastructure—creating steep barriers to entry. Most of the world's advanced chip production is concentrated at Taiwan Semiconductor Manufacturing Company (TSMC), creating an oligopoly and geopolitical security risk. The U.S. CHIPS and Science Act (2022) provides $52B in subsidies and $24B in tax credits to incentivize domestic fab construction.

📖 Gallaugher (2023), Ch.6. | Lecture Slides Ch.10, p.13–14. | CHIPS and Science Act, Pub.L. 117-167 (2022).

Emulator & Compiler Ch.10

A compiler is a program that translates source code written by a developer into the machine-language instruction set that a specific processor understands. An emulator translates instructions at runtime so that software compiled for one processor can run on a different processor. Apple's Rosetta 2 emulator allowed software compiled for Intel chips to run on M-series (Apple Silicon) Macs, preserving backward compatibility during Apple's chip transition.

📖 Gallaugher (2023), Ch.6. | Lecture Slides Ch.10, p.15.

E-Waste (Electronic Waste) Ch.10

Discarded, often obsolete electronic devices and components. Global e-waste reached 62 million tons in 2022 and is projected to rise to 74 million tons by 2030—equivalent to discarding every commercial aircraft ever built in a single year. E-waste contains toxic substances (lead, cadmium, mercury) that contaminate soil and water if not processed properly. Informal recycling by burning is common in developing countries because it is often cheaper to ship e-waste abroad than to recycle it responsibly. The U.S. has no comprehensive federal e-waste recycling law.

📖 Global E-waste Monitor 2024. | Gallaugher (2023), Ch.6. | Lecture Slides Ch.10, p.27–29.

Quantum Computing Ch.10

An emerging computing paradigm that uses quantum bits (qubits) instead of classical binary bits. Unlike a bit (strictly 0 or 1), a qubit can represent 0, 1, or both simultaneously through quantum superposition, enabling entirely new categories of computation. Potential applications include predicting the weather months in advance, hyperdetailed human body simulations, and unbreakable cryptographic systems. Quantum computing is not yet commercially feasible.

📖 Preskill, J. (2018). "Quantum Computing in the NISQ Era and Beyond." Quantum, 2, 79. | Lecture Slides Ch.10, p.25.

Konana's Software Ecosystem Ch.10 & 15

A layered model of information technology infrastructure proposed by Prabhudev Konana (2007). From innermost to outermost: Hardware → Operating System → Database Management System → Middleware → Enterprise Applications → Consumer Applications. Because each layer depends on the layers beneath it, organizations face lock-in and switching costs when changing any layer, since changes cascade upward through the stack. Strategic IT decisions must account for this ecosystem entrenchment.

📖 Konana, P. (2007). Cited in Gallaugher (2023), Ch.6 & Ch.9. | Lecture Slides Ch.10 p.8; Ch.15 p.8.

Switching Costs & Lock-In Ch.10 & 15

The financial, operational, and psychological expenses incurred when changing from one technology product, vendor, or system to another. In Konana's ecosystem model, switching costs arise because each software layer depends on layers beneath it, making changes throughout the entire stack costly. In SaaS, switching costs create vendor lock-in: if a SaaS provider fails or raises prices, customers may face expensive, time-consuming migration to an alternative platform.

📖 Gallaugher (2023), Ch.6 & Ch.9. | Lecture Slides Ch.10 p.8; Ch.15 p.21.

Latency Ch.10 & 15

The delay between initiating a request and receiving a response. In cloud and SaaS computing, latency refers specifically to the time added by the round trip a data request must make between a user's device and a remote server over the internet. High latency can significantly degrade the performance of real-time applications. Online gaming requires low latency (under 50 ms); streaming 4K video tolerates higher latency because video buffers ahead.

📖 Gallaugher (2023), Ch.6 & Ch.9. | Lecture Slides Ch.10 p.18–20; Ch.15 p.21–25.

Bandwidth Ch.10 & 15

The maximum rate at which data can be transmitted over a network connection, measured in bits per second (Mbps or Gbps). Bandwidth is analogous to the number of lanes on a highway—more lanes (higher Mbps) means more data can flow simultaneously. It is complementary to, but distinct from, latency (the speed limit). Key gotcha: ISPs advertise bandwidth in Megabits (Mbps); file sizes are in Megabytes (MB). Divide Mbps by 8 to get MB/s download speed.

📖 Gallaugher (2023), Ch.6 & Ch.9. | Lecture Slides Ch.10 p.19–20.

SaaS (Software as a Service) Ch.15

A software delivery model in which applications are hosted on a vendor's remote servers and accessed by users through a web browser or thin client over the internet, rather than installed locally on each user's machine. In SaaS, the vendor manages all infrastructure layers (hardware, OS, database, middleware, and application). Examples: Google Sheets, Salesforce CRM, Office 365, Canvas (LMS). Business model: typically monthly subscription or usage-based pricing.

📖 Gallaugher (2023), Ch.9. | NIST Definition of Cloud Computing, SP 800-145 (2011). | Lecture Slides Ch.15, p.9, 13.

IaaS (Infrastructure as a Service) Ch.15

A cloud computing model in which a third-party provider owns and manages physical hardware (servers, storage, networking infrastructure), while the customer manages the operating system, databases, middleware, and all applications running on that hardware. Example: Netflix and Walmart run their operations on Amazon Web Services (AWS) using IaaS—they manage their own software stack on Amazon's physical hardware.

📖 Gallaugher (2023), Ch.9. | NIST SP 800-145. | Lecture Slides Ch.15, p.9.

PaaS (Platform as a Service) Ch.15

A cloud computing model in which a provider manages all infrastructure layers (hardware, OS, database, middleware) and provides a development platform, while the customer develops and manages only their own applications. Example: Salesforce relies on PaaS to allow its enterprise customers to build custom applications connected to Salesforce data, without those customers managing the underlying infrastructure.

📖 Gallaugher (2023), Ch.9. | NIST SP 800-145. | Lecture Slides Ch.15, p.9.

Open Source Software Ch.15

Software whose source code is publicly available and may be freely used, examined, modified, and distributed by anyone with programming experience. Open source software allows community-driven development and improvement. Examples: Linux (operating system), Open Office (productivity suite), Android (mobile OS), MySQL (database). Contrast with closed source (proprietary) software, where the source code is not publicly disclosed.

📖 Open Source Initiative. The Open Source Definition. opensource.org. | Raymond, E.S. (1999). The Cathedral and the Bazaar. | Lecture Slides Ch.15, p.3.

Closed Source (Proprietary) Software Ch.15

Software whose source code is not publicly disclosed; only authorized employees of the owning company may view or modify how the program works. The software is distributed in compiled form only, protecting the owner's intellectual property. Examples: Microsoft Windows, macOS, Microsoft Excel, Adobe Photoshop. Opposite of open source software.

📖 Gallaugher (2023), Ch.8. | Lecture Slides Ch.15, p.3.

Open vs. Closed Standards Ch.15

Open standards are publicly available technical specifications that allow any company to create compatible or interoperable software without requiring permission from the original developer (e.g., Microsoft Windows—third parties can build Windows-compatible software freely). Closed standards are controlled by the owning firm, requiring its explicit permission before third parties may develop complementary applications (e.g., Apple's iOS App Store—developers must comply with Apple's approval process).

📖 Gallaugher (2023), Ch.8. | Lecture Slides Ch.15, p.4.

Total Cost of Ownership (TCO) Ch.15

A comprehensive accounting of all costs associated with acquiring and operating a technology system over its entire lifecycle. The purchase price represents only approximately 20% of TCO; the remaining ~80% consists of hidden costs including: requirements analysis and site prep, implementation and deployment, training, initial efficiency loss, ongoing operational support, maintenance and break-fix, and strategic development to keep up with a changing competitive landscape. SaaS typically has a lower and more predictable TCO than traditional on-premise software.

📖 Gallaugher (2023), Ch.9. | Gartner Research on IT TCO. | Lecture Slides Ch.15, p.17–18.

Freemium Pricing Ch.15

A pricing strategy in which a basic version of a product or service is offered at no cost to attract users, while advanced features, higher usage limits, or a premium version are available for a fee. Common among SaaS providers as a customer acquisition strategy. Example: Spotify (free with ads vs. paid subscription), Slack (free tier with message limits vs. paid plans), Dropbox (free storage vs. paid expansion).

📖 Anderson, C. (2009). Free: The Future of a Radical Price. | Gallaugher (2023), Ch.9. | Lecture Slides Ch.15, p.13.

Software Piracy Ch.15

The unauthorized copying, distribution, or use of proprietary software in violation of its license agreement. SaaS dramatically reduces piracy risk compared to traditional installed software because the application runs on the vendor's server and is never installed on the user's device—there is no executable file to copy or share illegally. Users must authenticate via the internet to access the service.

📖 Business Software Alliance (BSA). Global Software Survey (annual). | Gallaugher (2023), Ch.9. | Lecture Slides Ch.15, p.14.

Cloud Computing Ch.15

The delivery of computing services—servers, storage, databases, networking, software, analytics—over the internet ("the cloud"), enabling organizations to avoid building and maintaining their own data centers. As articulated in class: "Most companies shouldn't be in the business of running their own data centers any more than they should be in the business of generating their own electricity." Encompasses IaaS, PaaS, and SaaS models. Major providers: AWS, Microsoft Azure, Google Cloud.

📖 Gallaugher (2023), Ch.9. | NIST SP 800-145 (Mell & Grance, 2011). | Lecture Slides Ch.15, p.10.

Source Code Ch.8

The human-readable set of instructions written by a programmer in a specific programming language (e.g., Python, Java, C++) that defines how software behaves. Source code must be either compiled (translated entirely into machine code before execution) or interpreted (translated line-by-line at runtime by a separate program). Compiled languages (C, C++) run faster and are closer to hardware; interpreted languages (Python, PHP) are typically easier to develop in. Whether source code is publicly shared or kept secret is the defining difference between open source and closed source software.

📖 Gallaugher (2023), Ch.8. | Lecture Slides Ch.8 (OSS), p.9–10.

GNU GPL & Free Software Foundation (FSF) Ch.8

The Free Software Foundation (FSF), founded in 1983 by Richard Stallman, holds that restricting access to software source code is morally wrong. It created the GNU General Public License (GPL)—a "copyleft" software license designed to guarantee that software remains "free as in speech" (libre), meaning users have the right to run, study, modify, and redistribute it. The GPL requires that any derivative work built on GPL-licensed code must itself be released under the GPL. It is the legal foundation that allowed open source infrastructure to spread freely.

📖 Free Software Foundation. GNU General Public License v3. gnu.org/licenses/gpl-3.0. | Lecture Slides Ch.8 (OSS), p.15.

Linux & the Linux Kernel Ch.8

A free, open-source, Unix-like operating system kernel (the core of an OS that manages CPU, memory, and device I/O) created in 1991 by Linus Torvalds. Combined with the FSF's GNU tools, it formed a complete OS. Linux powers the majority of the global internet infrastructure, most cloud servers (including much of Microsoft Azure), and is the basis for Android (the world's most-used mobile OS). Its mascot is Tux the penguin. Versions exist for servers, desktops, and mobile devices.

📖 Torvalds, L. (1991). Original Linux kernel announcement. | Linux Foundation. Annual Linux Kernel Development Report. | Lecture Slides Ch.8 (OSS), p.14.

Linux Foundation & the "Bus Factor" Ch.8

By the early 2000s, the global internet ran on Linux maintained by a small group of unpaid volunteers—a dangerous "bus factor" (if key maintainers were hit by a bus, development would collapse). Rivals also feared fragmentation reminiscent of the "Unix Wars" (1980s–90s), when competing proprietary Unix variants created incompatibilities that harmed the entire industry. To address both risks, competing firms including IBM, HP, Intel, and Oracle formed the Linux Foundation (2007)—a neutral industry trade association that funds core maintainers (starting with hiring Linus Torvalds full-time in 2000 via the Open Source Development Labs), standardizes the code, and legally protects the Linux trademark.

📖 The Linux Foundation. About. linuxfoundation.org. | Gallaugher (2023), Ch.8. | Lecture Slides Ch.8 (OSS), p.16–17.

Technology Stack (Tech Stack) Ch.8

A coordinated set of layered software technologies that work together to make a website or application function. Each layer in the stack handles a different concern—the OS manages hardware resources, the database handles data persistence, the web server (middleware) routes requests, and the programming language/framework builds the user-facing application. Stacks are how Konana's ecosystem model maps to real-world products. Developers interact between layers via APIs (Application Programming Interfaces). Understanding a firm's tech stack allows managers to assess "data readiness" for AI deployment.

📖 Gallaugher (2023), Ch.8. | Lecture Slides Ch.8 (OSS), p.21.

LAMP Stack Ch.8

The original open-source web application stack, still powering 40%+ of websites. The acronym stands for: Linux (OS), Apache (web server / middleware), MySQL (relational database), PHP/Python/Perl (programming languages). All four components are free and open source. In Konana's ecosystem: Linux = OS layer; MySQL = DBMS layer; Apache = Middleware layer; PHP/Python = Application layer. Powers Facebook (originally), Wikipedia, YouTube (originally), and Slack.

📖 Gallaugher (2023), Ch.8. | Lecture Slides Ch.8 (OSS), p.23 & p.25.

MEAN Stack Ch.8

A modern open-source web stack using JavaScript across all layers: MongoDB (NoSQL/document database), Express.js (web server framework / middleware), Angular (front-end JavaScript framework), Node.js (server-side JavaScript runtime). Key advantages over LAMP: (1) all layers use a single language (JavaScript), making it easier to hire "full-stack" developers; (2) MongoDB's flexible document model is better for highly dynamic real-time applications (e.g., Netflix, Uber) where data structures change frequently. LAMP uses a relational (table-based) MySQL database; MEAN uses document-based MongoDB.

📖 Gallaugher (2023), Ch.8. | Lecture Slides Ch.8 (OSS), p.24 & p.26.

Web 1.0 vs. Web 2.0 (Static vs. Dynamic Sites) Ch.8

Web 1.0 refers to the early web (mid-1990s–early 2000s) characterized by static HTML pages that displayed fixed content—the same for every visitor, unchangeable without manually editing files. Web 2.0 refers to the era of dynamic web pages where content is generated on-the-fly by a server based on user identity, preferences, or real-time data, enabling interactive platforms where users create and share content (YouTube, Wikipedia, blogs, social media). Technology stacks (LAMP, MEAN) made dynamic sites possible at scale. The shift to dynamic sites also created new ad-supported business models (search ads, targeted ads).

📖 O'Reilly, T. (2005). "What is Web 2.0?" oreilly.com. | Gallaugher (2023), Ch.8. | Lecture Slides Ch.8 (OSS), p.20.

OSS Business Model Ch.8

Open source software vendors do not sell source code (it is free). Instead they monetize surrounding services. The four main revenue streams are: (1) Support & Stability—enterprises pay for guaranteed professional support (a "1-800 number at 2:00 AM"); (2) Security & Compliance—selling certified, hardened, heavily tested versions of free software for regulated industries; (3) Premium Tools for Scale ("The Anaconda Model")—selling enterprise-grade management, security, and deployment tools on top of the free software; (4) Hosted Managed Services—running the software for customers in the cloud so they avoid infrastructure headaches. Most OSS licenses prohibit selling customized derivative versions of the software itself.

📖 Gallaugher (2023), Ch.8. | Lecture Slides Ch.8 (OSS), p.12 & p.30.

OSS Strategic Value: "Keeping the Lights On" Ch.8

Although open source is a $60B industry, it has disproportionate impact on the $1.4 trillion IT software market. By making reliable, secure infrastructure free, OSS frees budget that firms would otherwise spend on fixed IT costs ("keeping the lights on"). These freed funds can then be redirected to innovation and competitive initiatives that actually differentiate a firm. Running commodity infrastructure does not create competitive advantage—but what you build on top of it can. OSS also levels the playing field for small firms by lowering infrastructure barriers.

📖 Gallaugher (2023), Ch.8. | Lecture Slides Ch.8 (OSS), p.18 & p.29.

Chapter 8 • MIS 301 Quiz

Open Source
Software (OSS)

5 scenario-based questions targeting application and analysis. Select the best answer to check your understanding.

📖 Topics covered: Open vs. closed source · OSS business model (how vendors earn revenue) · Linux & the Linux Foundation · LAMP vs. MEAN stacks · OSS strategic value for firms
Q1 OSS Business Model — How Vendors Make Money
A hospital's IT director is considering switching from a $120,000/year proprietary database to MySQL, an open source database that can be downloaded free of charge. A vendor rep from MySQL's parent company (Oracle) follows up and says: "We'd love to help you make that switch. You get the database for free, but we'd like to talk about our enterprise support contract." The director is confused—if the software is free, why would the hospital pay Oracle anything?
Which statement BEST explains how open source software vendors like Oracle (MySQL) generate revenue when the software itself is free?
✔ Correct: C — Explanation The OSS business model does not sell source code—it sells the support, expertise, and stability that enterprises need around the free software. As the slides state, enterprise users "aren't buying the code. They are buying insurance, scale, and peace of mind." Revenue streams include: guaranteed professional support contracts (the 1-800 number at 2 AM), security-certified/hardened software editions for regulated industries, premium enterprise management tools (the Anaconda model), and hosted managed services. A is wrong—access to OSS source code is free and unrestricted by definition. B is wrong—advertising is a consumer SaaS model, not an infrastructure OSS model. D is wrong—per-user royalties are a closed-source licensing model (like Microsoft), not OSS.
Q2 Linux Foundation — Why Rivals Cooperated
By 2003, the global internet ran almost entirely on Linux servers. Billions of dollars of corporate infrastructure depended on software maintained by a small group of volunteer programmers—including Linus Torvalds himself—who had no formal employment obligation to continue. Technology executives at IBM, HP, Intel, and Oracle started losing sleep over this. They were also haunted by the memory of the 1980s–90s "Unix Wars," when competing proprietary versions of Unix fragmented the market and left everyone worse off.
Which TWO risks MOST directly drove competing companies to set aside their rivalry and collectively fund the Linux Foundation?
✔ Correct: B — Explanation Two specific risks drove the formation of the Linux Foundation. First, the "bus factor": a multi-billion dollar digital economy depended on a tiny group of unpaid volunteer maintainers—if they stopped, critical global infrastructure would break. Second, fragmentation risk: companies feared a repeat of the Unix Wars, where competing proprietary forks of Unix created incompatible systems that harmed everyone. The solution was a neutral, jointly funded trade association (the Linux Foundation, 2007) that paid core maintainers, standardized the codebase, and legally protected the Linux trademark. A is wrong—the GPL does not expire, and the DoJ action is unrelated. C is wrong—the GPL explicitly permits commercial use; Google's Chrome OS is also Linux-based. D is wrong—Linux has never required purchase; Torvalds does not sell licenses.
Q3 LAMP Stack — Components & Konana's Ecosystem
A startup is building a social media app where users can create profiles, post text updates, and follow each other. The founding engineer announces: "We'll use the classic open-source web stack." She writes four technologies on the whiteboard: Linux, Apache, MySQL, and PHP. Her co-founder, fresh from MIS 301, immediately maps each one to a layer of Konana's software ecosystem.
Which mapping of LAMP components to Konana's ecosystem layers is CORRECT?
✔ Correct: D — Explanation In Konana's ecosystem, from innermost to outermost: Hardware → OS → DBMS → Middleware → Enterprise/Consumer Apps. The LAMP stack maps as: Linux is the OS (controls hardware resources); MySQL is the Database Management System (data persistence and I/O); Apache is the Middleware web server (routes HTTP requests between layers, transports data between apps); PHP/Python/Perl is used to build the Consumer or Enterprise Application layer (user-facing functionality). A, B, C all swap these roles incorrectly—the most common error is confusing Apache (a web server = middleware) with the OS, or MySQL (a database) with middleware.
Q4 LAMP vs. MEAN — Choosing the Right Stack
Two student startup teams are choosing their technology stacks. Team Alpha is building a content-heavy blog platform. Their engineers know several programming languages (PHP, Python, Ruby), and their data has a consistent, predictable structure. Team Beta is building a real-time ride-sharing app where data (driver locations, surge pricing, trip status) changes constantly, and they want to hire developers who only need to know one language from front-end to back-end.
Based on the characteristics of LAMP and MEAN stacks, which stack recommendation for each team is MOST appropriate?
✔ Correct: B — Explanation LAMP is the classic stack with a relational MySQL database (ideal for structured, consistent data like blog posts) and supports multiple programming languages—a good fit for Team Alpha. MEAN was specifically built for highly dynamic real-time applications (like Netflix and Uber, analogous to Team Beta's ride-sharing app) where data changes constantly. MEAN's key hiring advantage: all layers use JavaScript, making it easier to find "full-stack" developers. A is wrong—MongoDB's unstructured document model is actually less ideal for the predictable data of a blog; PHP is not inherently faster than JavaScript for real-time use cases. C is wrong—Angular does not provide "blogging templates," and Node.js actually handles high concurrency extremely well. D is wrong—market share (40%+) reflects historical prevalence, not universal superiority; the right stack depends on use case requirements.
Q5 OSS Strategic Value — Competitive Advantage & Fixed Costs
Two competing e-commerce companies are making IT infrastructure decisions. Company A decides to build and maintain its own proprietary operating system, web server, and database management system from scratch—spending $8M per year keeping these systems running. Company B adopts the open-source LAMP stack, paying only $400,000 per year in support contracts, and redirects the $7.6M in savings into AI-powered product recommendations and a personalized loyalty program that no competitor has yet. Three years later, Company B has significantly higher customer retention and revenue growth.
Which insight from the OSS slides BEST explains why Company B's approach created a competitive advantage while Company A's did not?
✔ Correct: C — Explanation The key insight from class: "Keeping the lights on does not create competitive advantage." Running commodity infrastructure (OS, web server, database) is a necessity, not a differentiator—both companies must do it. OSS frees funds that firms would otherwise spend on these fixed infrastructure costs. Company B redirected $7.6M from a non-differentiating fixed cost (custom OS/infrastructure) to genuinely differentiating innovations (AI recommendations, loyalty program). This is precisely why OSS has a $60B market size but a disproportionate impact on the $1.4T IT market. A is wrong—while network effects contribute to OSS reliability (more developers = more bug fixes), this is not the strategic argument about competitive advantage. B is wrong—OSS is not categorically more secure in "all cases"; the argument is about budget allocation, not universal technical superiority. D is wrong—first-mover advantages and switching costs are a separate concept; the scenario does not involve switching costs between the two companies.

Ch.8 Complete! 🐧

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Chapter 10 • MIS 301 Quiz

Moore's Law
& Hardware

5 scenario-based questions targeting application and analysis. Select the best answer to check your understanding.

📖 Topics covered: Moore's Law · Price elasticity of technology demand · Bits vs. bytes · GPU/ASICs for AI · E-waste challenges
Q1 Moore's Law — Nature & Origin
A business professor tells her class: "What we call 'Moore's Law' sounds like it was discovered by physicists in a laboratory, but it actually started as an observation published in a 1965 industry paper. The semiconductor industry later adopted this observation as a planning target—treating it as the expected pace of innovation they needed to hit every cycle." A student asks whether this means chip performance is guaranteed to keep doubling indefinitely.
Which statement BEST characterizes Moore's Law and why chip performance cannot be guaranteed to keep improving at this historical rate?
✔ Correct: B — Explanation Gordon Moore, co-founder of Intel, published his observation in 1965 that transistor counts on chips roughly doubled at regular intervals. This was an empirical finding—not a physical law—that the semiconductor industry voluntarily adopted as its innovation roadmap. Today, transistor miniaturization is approaching the limits of physics (individual transistors are just a few atoms wide), which is why the doubling cadence has slowed. A is wrong—no government mandates this doubling rate. C is wrong—quantum mechanics is actually what is stopping further shrinkage, not what drives it. D is wrong—Moore's observation was a technical finding about industry trends, not a marketing campaign.
Q2 Price Elasticity of Demand for Technology
In 2001, Apple released the first iPod with 5 GB of storage for $399. By 2011, Apple offered 5 GB of iCloud storage free of charge. During that decade, consumers went from storing a few hundred songs digitally to expecting entire photo libraries, 4K videos, and automatic device backups to live permanently in the cloud. A MIS professor attributes this dramatic behavioral shift to a core economics principle about how consumers respond to price changes.
Which concept BEST explains why the dramatic drop in storage costs led to a dramatic increase in storage demand?
✔ Correct: D — Explanation Price elasticity of demand measures how sensitively quantity demanded responds to price changes. Demand for computing technology is highly elastic: as storage prices plummeted from $399 for 5 GB to free, consumers didn't just buy the same amount of storage more cheaply—they invented new behaviors (photo storage, video backups, cloud sync) that consumed far more. This exemplifies a key insight from class: managers are often blindsided by how dramatically cheap tech reshapes consumer behavior. A is wrong—network effects explain value from more users, not price-driven demand growth. B is wrong—switching costs explain inertia with existing vendors, not the expansion of demand. C is wrong—vertical integration is a supply chain strategy unrelated to consumer demand elasticity.
Q3 Bits vs. Bytes — ISP Speed Gotcha
A student signs up for a campus internet plan advertised as "500 Mbps." Excited to download a large design file for class, she watches the download bar and sees the file arriving at roughly 62 MB/s. She tells her roommate: "My connection says 500, but it's only downloading at 62—something must be broken." Her roommate, who just finished the MIS 301 hardware module, laughs and explains the math.
Which explanation BEST clarifies why a 500 Mbps connection produces approximately 62 MB/s of download speed?
✔ Correct: C — Explanation The golden rule: 8 bits = 1 byte. ISPs advertise bandwidth in Megabits per second (Mbps) while file sizes are displayed in Megabytes (MB). To convert: 500 Mbps Ă· 8 = 62.5 MB/s—exactly what the student observes. This discrepancy often makes consumers feel cheated, but it reflects a consistent naming convention: lowercase "b" = bits (network speed), uppercase "B" = Bytes (storage). A is wrong—latency affects response delay, not maximum throughput; the download speed is as expected. B is wrong—RAM affects active processing, not network download throughput. D is wrong—ISPs typically advertise download speed prominently; this explanation is fabricated.
Q4 GPU / ASICs for AI — Purpose-Built Chips
An AI research lab is purchasing hardware to train large language models. The lab director says: "We should buy Nvidia chips rather than standard Intel CPUs. Nvidia's chips were originally designed to render video game graphics—a task that requires processing thousands of smaller calculations simultaneously rather than a few large sequential ones. That same massively parallel architecture turned out to be exactly what AI model training needs." The lab orders a rack of these chips.
What type of chip is the director recommending, and what is the general category term for purpose-built chips like it?
✔ Correct: B — Explanation GPUs (Graphics Processing Units) were first designed by Nvidia for rendering graphics, which requires executing thousands of small matrix calculations simultaneously. This massively parallel architecture proved ideal for AI/ML training, which also demands high parallelism. The broader term for chips designed for specific tasks rather than general-purpose computation is Application-Specific Integrated Circuits (ASICs). A is wrong—multicore CPUs are general-purpose chips; "PEUs" is not a real classification. C is wrong—quantum chips use qubits and are a fundamentally different paradigm, not yet commercially viable for AI training. D is wrong—neuromorphic chips mimic brain architecture but are a distinct emerging technology category, not what Nvidia produces.
Q5 E-Waste — Challenges & Root Causes
A student writing a sustainability report discovers that global e-waste reached 62 million tons in 2022 and is projected to hit 74 million tons by 2030. She also finds that despite growing awareness, informal e-waste processing—including burning electronics in open air, which exposes workers (often children) to lead, cadmium, and mercury—continues in many developing countries. She also notes that the United States has no comprehensive federal law requiring proper e-waste recycling.
Which factor MOST directly explains why proper e-waste recycling remains uncommon globally despite the known environmental and health harms?
✔ Correct: D — Explanation Responsible e-waste recycling is expensive and technically difficult given how small and varied electronic components are. Shipping electronics abroad where environmental regulations are weaker and informal burning is tolerated is often the cheaper path. Compounding this, the U.S. lacks a comprehensive federal e-waste recycling law, making enforcement inconsistent even where state laws exist. A is wrong—electronics contain valuable recoverable materials (gold, copper, rare earth metals); informal burning destroys value and releases toxins. B is wrong—while robotics (like Apple's Daisy robot) are promising, they have not fully solved the problem at a global scale. C is wrong—the toxins (lead, mercury, cadmium) exist in the materials themselves and are released by improper processing methods like burning, not by recycling per se.

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Chapter 15 • MIS 301 Quiz

Software as a
Service (SaaS)

5 scenario-based questions targeting application and analysis. Select the best answer to check your understanding.

📖 Topics covered: Open vs. closed source & standards · SaaS / IaaS / PaaS · Total Cost of Ownership (TCO) · SaaS piracy resistance · SaaS risks (vendor dependence, latency)
Q1 Open Source & Closed Standards
A student club treasurer uses LibreOffice Calc for the club's budget. A club member who is a programmer downloads LibreOffice's publicly available source code and customizes it to integrate directly with the club's payment processor. Meanwhile, the treasurer's friend uses Apple Numbers and notes that Apple does not publish Numbers' source code. He also observes that third-party developers must get Apple's explicit permission to build apps that integrate with Apple's software ecosystem.
Which row CORRECTLY classifies BOTH tools across the source code and standards dimensions from class?
✔ Correct: B — Explanation LibreOffice is open source (anyone can download and modify the code) and uses open standards (other developers can create compatible files or integrations without permission—e.g., .ods format is an open standard). Apple Numbers is closed source (Apple does not publish its code) and uses closed standards (developers need Apple's approval to build integrating apps through the App Store / Apple ecosystem). A is wrong—LibreOffice uses open, not closed, standards. C is wrong—LibreOffice's source code is publicly available, making it open source, not closed. D is wrong—Apple Numbers is closed source; the scenario explicitly states Apple does not publish the source code.
Q2 SaaS vs. IaaS vs. PaaS
A technology journalist profiles three cloud strategies: Company A (UT Austin) pays Microsoft a monthly subscription for Office 365—Microsoft manages everything: physical servers, the operating system, the database, and the Office applications themselves. Company B (Netflix) rents physical servers from Amazon Web Services but installs and manages its own operating system, database, and streaming software on those servers. Company C's enterprise customers (Salesforce clients) rent all infrastructure layers from a cloud provider but build and manage only their own custom CRM applications on top of Salesforce's platform.
Which assignment of cloud service models is CORRECT for all three companies?
✔ Correct: C — Explanation SaaS: the vendor manages everything including the application (UT/Office 365—Microsoft handles all layers). IaaS: the vendor provides physical hardware only; the customer manages OS, database, and apps (Netflix on AWS—Netflix runs its own software on Amazon's servers). PaaS: the vendor manages all infrastructure layers; the customer manages only their own application (Salesforce's enterprise clients build custom apps on Salesforce's managed platform). A, B, D all mix up the key distinguishing factor—how many layers the vendor manages vs. the customer manages.
Q3 Total Cost of Ownership (TCO)
A regional hospital's CFO reviews a $400,000 quote for a new on-premise patient records system. She says: "That's 40% of our annual IT budget—we can't afford it." The hospital's CIO pushes back: "The $400,000 is only a fraction of what this system will truly cost us. You haven't accounted for the consultant fees to deploy it, six months of reduced staff productivity during the learning curve, the annual technical support contract, hardware refresh every four years, or the cost of adapting the system as healthcare regulations change." The CFO asks what concept captures all of these costs together.
What concept does the CIO's argument illustrate, and approximately what share of total system cost does the sticker price (purchase price) typically represent?
✔ Correct: B — Explanation Total Cost of Ownership (TCO) is the comprehensive accounting of all costs over a system's lifecycle. Research and class slides show that the initial purchase price represents roughly 20% of TCO, while the remaining ~80% consists of hidden costs: requirements analysis, implementation, training, initial productivity loss, ongoing operational support, maintenance, and strategic adaptation costs. The iceberg analogy applies—most of the cost is beneath the surface. A is wrong—the 60/40 split reverses the real proportion; switching costs are a different concept (the cost of changing vendors). C is wrong—economies of scale describe cost per unit falling as volume rises, not the hidden lifecycle costs of a technology investment. D is wrong—freemium is a pricing strategy (free base + paid premium), unrelated to the hospital's on-premise purchase scenario.
Q4 Why SaaS Reduces Software Piracy
A software developer sold her video-editing program as a downloadable desktop application for $199 per license. She constantly fought piracy: users would buy one license and share the installer file with dozens of friends. Frustrated, she redesigns the product entirely as a SaaS application. Now users pay $15/month, log in through a web browser, and all processing happens on her servers—nothing is installed on the user's computer. The piracy problem essentially disappears overnight.
Why does the SaaS delivery model drastically reduce software piracy compared to the traditional downloaded-installer model?
✔ Correct: C — Explanation Traditional software piracy involves copying an installer or executable file and sharing it without authorization. In a SaaS model, no software is installed on the user's device—the application runs entirely on the vendor's servers, and users access it through a browser session. There is nothing to copy or share. Users must authenticate over the internet with valid credentials to use the service, making unauthorized distribution fundamentally impossible in the same way. A is wrong—SaaS software is typically closed source (Google Sheets, Salesforce, etc.); this is not what prevents piracy. B is wrong—standards (open or closed) govern interoperability, not piracy prevention; closed standards do not inherently stop piracy. D is wrong—EULAs exist for both SaaS and desktop software and are not the structural reason piracy drops; enforcement of license agreements has always been difficult for desktop software.
Q5 SaaS Risks — Vendor Dependence & Latency
A finance manager is evaluating whether to move her 200-person firm from locally installed Excel to online Excel (Office 365 SaaS). Her IT colleague raises several concerns: the team will be dependent on Microsoft's uptime; if Microsoft discontinues the service or raises prices significantly, migrating thousands of files and retraining staff will be time-consuming and costly; the online version lacks the Scenario Manager tool the finance team uses constantly; and every time someone opens or saves a file, that action must travel from the user's laptop to Microsoft's data center and back before completing.
Which SaaS risk is the IT colleague describing when he highlights the delay caused by file operations traveling to Microsoft's remote data center?
✔ Correct: D — Explanation Latency is specifically the delay caused by a request traveling from a user's device to a remote server and back. In SaaS, every file operation involves this internet round trip. The class example with online Excel and UT Box illustrated exactly this: computation happens on Microsoft's server, but the file travels through multiple network hops, and "with this much network travel, latency can become a significant issue." A is wrong—switching costs refer to the expense of changing between vendors or systems, not the per-operation delay of using the current system. B is wrong—piracy refers to unauthorized copying; Microsoft's authentication systems address this; file open/save delays are unrelated. C is wrong—TCO is a financial accounting framework for total lifecycle costs; latency is a performance characteristic, not a cost accounting concept.

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