Management Information System

Why enterprise applications are needed

Large organizations have many functions (sales, finance, HR, production, logistics) that traditionally ran on separate, incompatible systems. This causes duplicated data, inconsistent information, slow decisions and poor coordination. Enterprise applications integrate these processes on a common platform and database. They give a single, enterprise-wide view of data, improve coordination between departments and with suppliers and customers, reduce costs and errors, speed up decisions, and support competitive strategy. The main types are enterprise systems (ERP), supply chain management, customer relationship management (CRM) and knowledge management (KM) systems.

Customer Relationship Management (CRM) systems

CRM systems capture and integrate all customer data from every contact point (web, phone, email, store, social media) into a central repository, and analyze it to manage the whole customer relationship.

• Sales force automation: lead and opportunity tracking, contact and quotation management, forecasting.
• Customer service: call-center support, ticketing, self-service web portals, knowledge bases and chat.
• Marketing: campaign management, customer segmentation, direct marketing and cross-selling.
• Analytical CRM: data warehousing and analysis to compute customer lifetime value (CLTV), churn and profitability.
• Partner relationship management (PRM) and employee relationship management (ERM) extend the same ideas to partners and staff.

Benefits include higher customer satisfaction and retention, better targeting, increased sales, lower marketing cost and identification of the most profitable customers.

Knowledge Management (KM) systems

KM systems collect, store, share and apply an organization's knowledge, both explicit (documents, procedures) and tacit (employees' experience), so that the firm can learn and decide better.

• Enterprise-wide KM systems: general-purpose tools for structured knowledge (document repositories, content management systems), semi-structured knowledge (email, video, graphics, intranets) and a "knowledge network" or expertise location system that connects people.
• Knowledge work systems (KWS): specialised workstations for engineers, scientists and designers, such as CAD, virtual reality and investment workstations.
• Intelligent techniques: expert systems, case-based reasoning, fuzzy logic, neural networks, genetic algorithms and intelligent agents that capture and use tacit knowledge.

KM systems aid knowledge acquisition, storage, dissemination and application, reduce rework, and increase innovation and productivity.

Michael Porter's competitive forces model

The five forces that shape competition in an industry are:

1. Traditional competitors: rivalry among existing firms.
2. New market entrants: threat from new firms entering the industry.
3. Substitute products and services: alternatives that customers can switch to.
4. Customers: their bargaining power and ability to demand lower prices.
5. Suppliers: their bargaining power over input prices and terms.

Information system strategies for dealing with the forces

Porter identified four generic strategies, each supported by information systems.

• Low-cost leadership: use systems to reduce operating costs and prices below competitors by improving efficiency, and to cut customers' costs of switching to the firm. Example: Walmart's retail link continuous replenishment system links suppliers to sales data, lowering inventory cost.
• Product differentiation: use systems to offer unique products or services, or to enable mass customization. Example: Dell's build-to-order, Apple's iPhone/App ecosystem, Google's search.
• Focus on market niche: use data analysis to target a narrow segment very closely and serve it better than broad competitors. Example: Hilton's customer data used for tailored offers.
• Strengthening customer and supplier intimacy: use systems to tie the firm tightly with customers and suppliers, raising switching costs. Example: Amazon's personalised recommendations, supplier extranets.

Other responses include building switching costs, supply chain efficiency (linking to suppliers for just-in-time delivery), and the value chain/value web approach, where firms use IT to benchmark and partner to add value. These strategies reduce the power of customers and suppliers, raise entry barriers, and make substitutes less attractive.

Business intelligence (BI)

Business intelligence refers to the infrastructure and tools for collecting, storing, integrating, analyzing and presenting data so that managers can make better decisions. It includes data warehouses and data marts, analytics software (OLAP, data mining), reporting and dashboards, and delivers information on performance such as sales, costs and customer behaviour. Typical outputs are production reports, parameterized reports, scorecards, ad hoc queries and forecasts.

Predictive analytics

Predictive analytics uses statistical techniques, data mining, historical data and assumptions to predict future outcomes and trends. It builds models (regression, classification, decision trees, neural networks) from past data to estimate the probability of future events.

• Examples: predicting customer response to a campaign, likelihood of loan default, customer churn, credit scoring, demand forecasting, and insurance claims fraud.
• Procedure: define the objective, prepare data, build the model, validate it, and deploy it to score new data.
• Benefit: proactive decisions, better targeting, and risk reduction.

Big data analytics

Big data refers to datasets so large and varied that conventional database systems cannot handle them, characterized by volume, velocity and variety (and veracity, value). Sources include social media, sensors (IoT), web logs, mobile devices and transactions.

• Technologies: Hadoop (HDFS and MapReduce), Spark, NoSQL databases, in-memory computing, analytic platforms and cloud storage.
• Analytics: finds patterns in structured and unstructured data, including text, sentiment, click-stream and location analysis.
• Uses: personalised recommendations, real-time fraud detection, sentiment monitoring, supply chain and maintenance optimization, healthcare analysis.

Predictive models are often trained on big data, and together they improve the quality and speed of decisions.

Technical approach

The technical approach emphasizes mathematically based models and the physical technology of systems and their capabilities. It draws on:

• Computer science: theories of computability, methods of computation and data storage/access.
• Management science: models for decision making and management practice, optimisation.
• Operations research: mathematical techniques for optimising parameters like transportation, inventory control and costs.

It focuses on how to design efficient, correct and powerful systems.

Behavioral approach

The behavioral approach studies the problems of long-term maintenance and use of systems that arise from human attitudes and behaviour, which technical models cannot capture. It draws on:

• Sociology: how groups and organisations shape and are shaped by systems.
• Psychology: how individuals perceive and use information, decision-making.
• Economics: how systems affect control, costs and structure of the firm and markets.

It examines issues such as user acceptance, resistance to change, organisational politics, and changes in jobs and power.

Sociotechnical view

Information systems are best understood by combining both. A system works well only when technology is fitted to the organisation and people through joint design, optimising both technical and social performance.

Tools and technologies for collaboration and social business

Collaboration is working with others to achieve shared, explicit goals. Social business uses internal and external social networking to deepen engagement with employees, customers and suppliers. The main tools are:

• Email and instant messaging: basic asynchronous and real-time messaging (Outlook, Gmail, Slack, Teams).
• Wikis: web pages that many users can edit to collect and share knowledge (Wikipedia, Confluence).
• Virtual worlds: 3D online environments where avatars meet and work.
• Collaboration and social business platforms: Microsoft Teams, Google Workspace, Lotus Notes, SharePoint, Jive, Yammer; with file sharing, project workspaces and profiles.
• Cloud-based document tools: Google Docs, Office 365, Dropbox for real-time co-editing and storage.
• Videoconferencing and telepresence: Zoom, Google Meet, Teams for remote meetings.
• Social networking: Facebook, LinkedIn, Twitter and internal enterprise social networks for connecting, feedback and marketing.
• Blogs and microblogs: informal publishing and updates.
• Social bookmarking, tagging and RSS: sharing and organising content.
• Project management and workflow tools: Trello, Asana, Jira to track tasks.

Benefits are faster communication, better teamwork across locations, knowledge sharing, productivity, and closer customer engagement.

Challenges posed by strategic information systems

Strategic information systems change the goals, operations, products or relationships of a firm to give a competitive advantage. They face these challenges:

• Sustaining competitive advantage: advantages are often temporary because competitors copy the system quickly (for example ATMs and online banking), so the firm must keep innovating.
• Aligning IT with business strategy: a system must fit the firm's business model, culture and structure; misalignment wastes investment.
• Organisational change: strategic systems usually require changes in processes, structure, jobs and culture, which meet resistance and risk failure.
• High cost and risk: large investment, uncertain returns, and project failure.
• Dependence on technology: disruption or failure has major business impact, creating security and reliability concerns.
• Easily copied or commoditised technology: technology alone rarely gives a lasting edge; it needs complementary assets such as skills and brand.
• Need for skilled staff and management commitment.
• Ethical, legal and social issues: privacy, and the effect on competitors and employees.

Ethics

Ethics refers to the principles of right and wrong that individuals, acting as free moral agents, use to make choices that guide their behaviour. In information systems, ethical issues arise around information rights, property rights, accountability and liability, system quality, and quality of life.

Ethical analysis process

When faced with an ethical dilemma, a five-step analysis is followed:

1. Identify and describe clearly the facts: find out who did what to whom, and where, when and how; get the facts from independent sources.
2. Define the conflict or dilemma and identify the higher-order values involved: such as privacy, property, freedom, security or efficiency.
3. Identify the stakeholders: every individual or group who has an interest in the outcome and may be affected.
4. Identify the options that you can reasonably take: some options may satisfy one stakeholder but not another; there may be no perfect solution.
5. Identify the potential consequences of your options: consider long-term and short-term outcomes, and the probability of each, including legal consequences.

The options are then judged by ethical principles: the Golden Rule, Kant's categorical imperative, Descartes' rule of change (if it cannot be done repeatedly, it should not be done at all), Utilitarian principle, Risk Aversion principle, and the "no free lunch" rule.

Intellectual property rights

Intellectual property is intangible property created by individuals or corporations, protected by trade secret, copyright and patent laws. Information technology has made protection difficult because digital content can be copied, shared and changed easily at almost no cost.

Challenges

• Ease of copying and distribution: software, music, films and books can be duplicated perfectly and transmitted instantly across the internet (peer-to-peer sharing, torrents), so piracy is widespread.
• Difficulty of enforcement: identifying infringers across jurisdictions is hard; laws differ between countries and the internet is global.
• Software is hard to classify: programs are protected by copyright (the code), patents (the idea/algorithm) and trade secrets, but the boundaries are unclear, and patents on algorithms are controversial.
• Digital Millennium Copyright Act (DMCA) and fair use: balancing creators' rights against users' fair use, and the liability of internet service providers for user content.
• Mashups and user-generated content: reusing and remixing material blurs ownership; the web makes it hard to trace original sources.
• Open source and licensing complexity.
• Weak legal protection in some countries, including Nepal, and limited awareness.
• Lack of clear rules for AI-generated content and for linking or framing other sites' content.

These challenges threaten the incentive to create and require technical protection (DRM), legal measures and public awareness.

Blockchain technology

A blockchain is a distributed, shared, tamper-resistant ledger that records transactions across many computers (nodes) in a peer-to-peer network, with no central authority such as a bank. Transactions are grouped into blocks; each block holds a list of transactions, a timestamp, and the cryptographic hash of the previous block, which links them in a chain. Altering any earlier block changes its hash and breaks all later links, so tampering is easily detected.

How it works

1. A user requests a transaction (for example, transfer of cryptocurrency or a contract record).
2. The transaction is broadcast to the network of nodes.
3. Nodes validate it using a consensus mechanism such as proof of work or proof of stake.
4. Validated transactions are combined into a new block (by miners/validators).
5. The block is added to the end of the chain, with its hash linking to the previous block.
6. Every node updates its copy of the ledger; the transaction is complete and permanent.

Features and uses

Decentralisation, transparency, immutability, security through cryptography, and traceability. Uses include cryptocurrencies (Bitcoin), smart contracts, supply chain tracking, land and medical records, voting and cross-border payments.

Role of knowledge management systems (KMS) in business

Knowledge management systems help an organisation capture, store, distribute and apply knowledge, which is a strategic asset and a source of competitive advantage. Their roles are:

• Knowledge acquisition and storage: collect explicit documents and tacit expertise into repositories, so knowledge is not lost when employees leave.
• Sharing and distribution: make information available to the right people at the right time through intranets, search and expertise directories.
• Better decision-making: give managers and staff timely, relevant knowledge based on best practices.
• Improved productivity and efficiency: reduce duplicated work and speed up problem solving and training.
• Innovation: combine knowledge from different areas to develop new products and services.
• Better customer service: staff can quickly find answers, improving response and satisfaction.
• Organisational learning: the firm learns from experience and adapts to the environment faster.
• Competitive advantage and lower costs: a unique, hard-to-copy knowledge base improves responsiveness and reduces errors.

KMS include enterprise-wide systems, knowledge work systems and intelligent techniques, together with collaboration tools.

Use of business intelligence by senior management

Senior managers face unstructured, long-term strategic decisions, and rely on BI tools to monitor performance and the external environment.

• Executive dashboards and digital cockpits: the main interface, showing key performance indicators (KPIs) such as revenue, profit, market share and customer satisfaction in real time with graphics.
• Balanced scorecard and performance management: measuring performance against strategic goals across financial, customer, internal process and learning perspectives.
• Drill-down and OLAP analysis: moving from summary figures to detailed data to find the cause of a problem and compare by product, region and time.
• Exception reporting and alerts: highlighting deviations from targets so management can act promptly.
• Forecasting and "what-if" analysis: predictive analytics and simulations to evaluate options such as new markets, pricing and investment.
• External data: monitoring competitors, markets and economic trends alongside internal data.
• Decision support and group decision support systems: helping senior teams reach strategic decisions.

These tools give top management a timely, unified view of the organisation, improving the speed, quality and accountability of strategic decisions.

(a) Information systems department

The information systems (IS) department is the formal organizational unit responsible for the firm's information technology services. It is led by a Chief Information Officer (CIO) and staffed by:

• Programmers – write the software instructions for the computer.
• Systems analysts – translate business problems into information requirements and system designs; they link the IS department and the end users.
• Information systems managers – lead teams of programmers and analysts, project managers, facility managers, and operations staff.
• Database and network administrators – maintain the data and communication infrastructure.

Its functions include maintaining hardware, software, data storage and networks, developing new systems, providing technical support and training, and advising management on IT strategy. End users, by contrast, are the departments for whom the applications are built.

(b) Knowledge work system (KWS)

A knowledge work system supports knowledge workers such as scientists, engineers, architects and designers in creating new knowledge and integrating it into the organization. These systems need powerful graphics, analytical tools, communications and document-management capabilities, and access to external knowledge bases.

Examples are computer-aided design (CAD) systems, 3D virtual reality simulation systems, investment workstations for financial analysts, and medical imaging workstations. Together with office systems, they form the knowledge-level systems of the organization.

Organizations are divided into strategic, management, operational and knowledge levels, and each level needs a different kind of information system.

1. Transaction Processing Systems (TPS) – operational level

• Record the daily routine transactions of the business, such as sales orders, payroll, cash deposits and reservations.
• Serve operational managers and staff who track the basic activities of the firm.
• Inputs are transactions; outputs are detailed reports and lists, and they feed the other systems.

2. Management Information Systems (MIS) – middle management

• Summarize and report on the basic operations of the firm using data from the TPS.
• Produce routine, scheduled, exception and summary reports (e.g. monthly sales or inventory reports).
• They are mostly structured, with little analytical capability.

3. Decision Support Systems (DSS) – middle management

• Support non-routine, semi-structured decisions using analytical models and data analysis tools (e.g. what-if analysis, sensitivity analysis).
• Combine internal data from TPS/MIS with external data; interactive and user-friendly.

4. Executive Support Systems (ESS) – senior management

• Address non-routine, unstructured strategic decisions that need judgement and insight.
• Provide aggregated, summarized data from internal and external sources through dashboards and drill-down facilities.

5. Knowledge-level systems

• Knowledge management systems and office systems help knowledge and data workers create and share knowledge and process documents and schedules.

Cross-level systems: Enterprise systems (ERP), supply chain management, customer relationship management and knowledge management systems integrate functions across the organization.

Features of organizations managers need to know

1. Routines and business processes – well-defined rules and procedures that employees follow, giving efficiency but also resistance to change.
2. Organizational politics – different groups hold different viewpoints, interests and resources, so system changes can cause conflict.
3. Organizational culture – shared assumptions about goals and products; systems must fit the culture or face resistance.
4. Organizational environments – organizations absorb resources from, and respond to, their surroundings; information systems help them scan the environment.
5. Organizational structure – entrepreneurial, machine bureaucracy, divisionalized bureaucracy, professional bureaucracy, adhocracy; each uses systems differently.
6. Goals, agency of decisions, and type of leadership and tasks, which influence the kind of system needed.
7. Unique history and values of the organization.

Economic impacts of information systems

• Information technology as a factor of production: IT can substitute for capital and labour. As the cost of IT falls relative to labour, firms replace people with technology.
• Transaction cost theory: firms exist because markets involve costs of searching, contracting and coordinating. IT lowers these costs, so firms can contract with outside suppliers cheaply and may shrink or outsource.
• Agency theory: the firm is a nexus of contracts between self-interested individuals (principals and agents). IT reduces the cost of monitoring agents, so firms can grow without adding proportional managers and supervisors, flattening the hierarchy.
• Effect on size and structure: lower coordination and information costs let firms operate with fewer layers of management and more decentralized decision making.

Types of decisions

1. Unstructured decisions – novel, non-routine and important, where no agreed procedure exists (e.g. entering a new market). Typical of senior management.
2. Structured decisions – repetitive and routine with a definite procedure (e.g. reordering stock at a minimum level). Typical of operational management.
3. Semi-structured decisions – only part of the problem has a clear answer from an accepted procedure (e.g. budget planning, setting a production schedule). Typical of middle management.

By level, decisions are strategic, management (tactical) and operational.

Stages of the decision-making process (Simon)

1. Intelligence – discovering, identifying and understanding the problems occurring in the organization: why, where and with what effect.
2. Design – identifying and exploring various solutions to the problem.
3. Choice – choosing among the solution alternatives.
4. Implementation – making the chosen alternative work and monitoring how well the solution is working.

If the solution fails, the process returns to an earlier stage; hence decision making is iterative. Information systems support each stage, for example MIS reports in intelligence and DSS models in design and choice.

A business process is a logically related set of activities and tasks that defines how specific business work is performed, to produce a product or service. It is also the unique way an organization coordinates work, information and knowledge. Examples: fulfilling an order, hiring an employee, creating a marketing plan, paying a supplier.

Relation to information systems

• Information systems automate parts of business processes. For example, an order-fulfilment process (check credit, approve order, ship, invoice) is carried out through order-entry and accounting systems.
• IT can make processes more efficient by automating manual steps, executing steps in parallel instead of sequentially, and eliminating delays.
• Information systems can change processes by supporting new ways of working, such as business process redesign and reengineering.
• Each functional area (sales, finance, manufacturing, HR) has its own processes, and enterprise systems integrate them across departments.
• Well-designed systems improve the speed, accuracy and cost of processes, giving operational excellence and better customer service.

Thus processes determine what a system must do, and systems determine how well the processes perform.

Information systems raise new ethical, social and political issues because they concentrate power and create new opportunities for both benefit and harm.

Social issues

• Information rights and privacy – collection and use of personal data by firms and governments (spam, tracking, profiling).
• Property rights – protecting intellectual property such as software, music and text against copying; difficulty of enforcing copyright and patents in digital form.
• Accountability and control – who is liable when systems cause harm, for example through faulty software or data loss?
• System quality – what level of accuracy and reliability is acceptable, and who is responsible for data errors?
• Quality of life – risks of dependence on computers, loss of jobs, technostress, the digital divide, boundary erosion between work and home, and cybercrime.

Political issues

• Passing laws on privacy, free speech, and intellectual property (e.g. data protection laws, electronic transaction laws).
• Government surveillance versus individual rights; censorship of the Internet.
• Unequal access to information, giving some groups power over others.

Society must respond with clear laws, professional codes of conduct, and ethical policies to balance the benefits of technology with individual rights.

In a traditional file environment, each functional area (sales, accounting, HR) builds and maintains its own files and application programs. This causes:

1. Data redundancy – the same data (e.g. customer address) is stored in many files, wasting storage space.
2. Data inconsistency – the same attribute has different values in different files because updates are not made everywhere.
3. Program-data dependence – programs are tightly coupled to the file formats; changing a data item requires changing every program that uses it.
4. Lack of flexibility – ad hoc reports and new queries are hard to produce; each needs a programmer to write a new program.
5. Poor security – there is little control over who can access or change data.
6. Lack of data sharing and availability – data are isolated in separate departmental files, so information cannot be combined across the organization.
7. Poor data integrity and high maintenance cost – lack of central control.

A database management system (DBMS) solves these problems by centralizing data and providing a single logical view, controlled redundancy, and shared access.

Types of decisions

• Unstructured – novel and non-routine with no agreed procedure (strategic level, e.g. launching a new product line).
• Semi-structured – part of the problem can be solved with a procedure but judgement is also needed (middle management, e.g. budgeting).
• Structured – repetitive and routine with a definite procedure (operational level, e.g. inventory reorder).

Decision-making process (Simon's model)

1. Intelligence – identify and understand the problem and its causes.
2. Design – develop and explore possible solution alternatives.
3. Choice – select the best alternative.
4. Implementation – put the chosen solution into practice and monitor how well it works.

The process is iterative: if monitoring shows the solution fails, the decision maker returns to an earlier stage. Information systems such as MIS, DSS and ESS provide the information and models that support these stages.

Firms invest in information systems to achieve six strategic business objectives:

1. Operational excellence – improve efficiency to get higher profits, e.g. supply chain and ERP systems that reduce costs and speed up operations.
2. New products, services and business models – IT enables new offerings, e.g. online music and e-banking.
3. Customer and supplier intimacy – serve customers well and keep them returning, and work closely with suppliers for lower costs, e.g. CRM systems.
4. Improved decision making – real-time data and analytics help managers decide better and faster, avoiding decisions based on guesses.
5. Competitive advantage – doing things better, faster or cheaper than rivals through any of the above objectives.
6. Survival – meeting industry standards, regulatory requirements and competitors' moves (e.g. ATMs for banks, government reporting systems).

Information systems are thus essential tools for achieving these goals.

The Internet has changed competitive advantage mainly by reducing entry barriers and sharpening rivalry, which in many industries lowers profits.

Effects on Porter's competitive forces

• Substitute products – new Internet-based substitutes appear (e.g. online streaming replaces DVDs).
• Customers' bargaining power – customers can compare prices easily, so their power rises.
• Suppliers' bargaining power – procurement through the Internet lets buyers find suppliers, which can reduce supplier power; but suppliers can also reach more customers directly.
• Threat of new entrants – low cost of starting an online business reduces entry barriers.
• Rivalry – competition expands geographically, and price becomes the main basis for competition, reducing profit margins.

Benefits for firms

• Wider market reach, lower transaction cost, easier customer relationship building and new business models.
• Because Internet technology is available to all, it is hard to hold an advantage from it alone; firms must combine it with a distinctive strategy and unique assets.

The Internet makes it easy to collect, store, link and distribute personal information, creating several privacy challenges:

1. Tracking via cookies and web beacons – websites record visits, searches and purchases to profile users, often without clear consent.
2. Web bugs and spyware – hidden code and malicious software monitor user behaviour and send data to third parties.
3. Search engine and social media profiling – queries and posts are stored, analysed and sold for targeted advertising.
4. Behavioural targeting and data brokers – data from many sources are combined into detailed profiles.
5. Email and chat monitoring, spam and phishing.
6. Data breaches and hacking exposing personal data.
7. Mobile and location tracking by apps and devices.
8. Weak or inconsistent laws across countries and long privacy policies that users do not read, so consent is not truly informed.

Protection measures include opt-in consent, privacy policies, data protection laws, encryption, and tools such as anonymous browsers and cookie controls.

An enterprise system (ERP) integrates the key business processes of a firm into a single software system with a common central database. Its business value includes:

1. Operational efficiency – common data and standardized processes remove duplicate work and speed up order fulfilment and production, lowering costs.
2. Better decision making – managers get firm-wide, accurate, real-time information on sales, inventory and costs.
3. Reduced inventory and costs – better coordination of demand, production and supply reduces stock and procurement costs.
4. Improved customer service – quicker response to orders and delivery.
5. Increased flexibility and coordination across functions, units and locations.
6. Standardization and improved data quality – one version of data across the firm.
7. Support for regulatory compliance and reporting.

Costs and risks include high implementation expense, organizational change and difficulty of customization.

(a) Complementary assets

Complementary assets are additional assets required to derive value from a primary investment. Investing in information technology alone does not give returns; firms must also invest in organizational and managerial assets, such as:

• Organizational assets – supportive culture, flexible organization, efficient business processes, decentralized authority and good incentive systems.
• Managerial assets – strong senior management support, training programs, and management that fosters teamwork and learning.
• Social assets – the Internet and telecommunications infrastructure, standards, laws, and a skilled workforce.

Firms that invest in these complementary assets get greater returns from technology than those that do not.

(b) Professional code of conduct

A code of conduct is a set of promises or rules adopted by a professional association to guide the ethical behaviour of its members. Examples are the ACM and IEEE codes. Typical principles: contribute to society and avoid harm, be honest, respect privacy and confidentiality, respect intellectual property, maintain professional competence, and obey the law. Such codes help members make ethical decisions where law is silent and protect the public and the profession.

Porter's competitive forces model

Michael Porter's model says a firm's profitability and strategy are shaped by five forces in its industry:

1. Traditional competitors (rivalry among existing firms) – all firms in the industry compete on price, quality and service.
2. New market entrants – new firms can enter if barriers are low, taking market share.
3. Substitute products and services – alternatives that customers may switch to.
4. Customers – their bargaining power depends on how easily they can switch and compare prices.
5. Suppliers – their power depends on how few they are and how important their inputs are.

Using information systems against these forces (generic strategies)

• Low-cost leadership – use systems to reduce costs below rivals. Example: Walmart's retail link and supply chain system lets suppliers see sales and replenish stock automatically; in Nepal, Daraz's logistics and inventory systems keep delivery costs low.
• Product differentiation – use systems to offer unique products or services. Example: Apple's iTunes/App Store ecosystem; Nepali banks' mobile banking and eSewa/Khalti wallets differentiate their services.
• Focus on market niche – analyse customer data to serve a narrow segment very well, e.g. Hilton's customer data warehouse targeting frequent guests, or an insurer using data analytics for specific groups.
• Strengthen customer and supplier intimacy – increase customer switching costs through CRM and loyalty programmes, e.g. Amazon's one-click ordering and recommendations; link suppliers through shared supply chain systems, e.g. Toyota.

Effect on each force

• Entrants are discouraged by high-cost IT systems and network effects.
• Substitutes are fended off by continually improving service.
• Customer power is reduced by switching costs.
• Supplier power is reduced by multiple sourcing through online procurement.

Other strategies include the value chain model and business ecosystems.

These three are among the five moral dimensions of the information age (with accountability and quality of life).

1. System quality Concerns what level of data and system accuracy and reliability is acceptable. Sources of poor quality are software bugs and errors, hardware failures, and poor input data quality.

• Businesses: poor quality causes losses, lawsuits, wrong decisions and reputational damage (e.g. incorrect billing).
• Users: they get wrong information, lost time and sometimes harm (medical or banking errors).
• Society: increased dependence on critical systems (air traffic, power, banking) means failures can cause large-scale harm. The ethical question is when software can be released and who is liable.

2. Information rights Concern what information individuals and organizations have a right to keep private (privacy) and what they are entitled to access. Privacy is protected by laws and principles such as fair information practices (notice, choice, access, security, enforcement).

• Businesses: must collect and use data lawfully; breaches bring penalties and loss of customer trust. Profiling provides advantage but invites regulation.
• Users: risk of misuse of personal data, tracking, identity theft and spam.
• Society: balance between security/surveillance and freedom; need for data protection laws.

3. Property rights Concern protecting intellectual property (trade secrets, copyright, patents) when digital content is easy to copy and distribute.

• Businesses: software and content firms protect their investment through copyright and licensing; piracy reduces revenue and incentive to innovate.
• Users: must obey licences; legal access to content may be limited; open-source and fair-use rules give alternatives.
• Society: too little protection discourages innovation, too much restricts the sharing of knowledge; laws must balance both.

Together these dimensions require ethical policies, laws, professional codes and careful system design.

Importance of data quality for business intelligence (BI)

BI tools such as dashboards, reports and analytics are only as good as the data behind them. Poor data lead to "garbage in, garbage out":

• Wrong decisions and lost revenue, e.g. incorrect customer or inventory counts.
• Duplicate or inconsistent records distort totals and customer views.
• Loss of trust in reports, and regulatory or compliance problems.
• Higher costs for correction and rework.

Good quality data are accurate, complete, consistent, timely, valid and unique, and so allow reliable forecasts, segmentation and performance measurement.

Techniques to ensure accuracy, consistency and reliability

1. Data quality audit – structured survey of accuracy and completeness by sampling files.
2. Data cleansing (scrubbing) – detect and correct incorrect, incomplete, improperly formatted or redundant data; remove duplicates.
3. Integrity constraints – primary key (entity integrity), foreign key (referential integrity), domain/check constraints, NOT NULL and unique constraints.
4. Input validation – type, range and format checks at data entry; drop-down lists.
5. Normalization – removes redundancy and update anomalies, giving consistency.
6. Transactions with ACID properties – ensure reliable updates and recovery.
7. Master data management – a single authoritative source for core entities.
8. ETL and data governance – standard definitions, data stewards, policies and metadata in the data warehouse.
9. Backup, recovery, access control and logging – protect reliability and security.
10. Regular monitoring and profiling of data against rules.

Strategic business objectives of information systems

1. Operational excellence – improve efficiency, productivity and profit.
2. New products, services and business models.
3. Customer and supplier intimacy.
4. Improved decision making.
5. Competitive advantage.
6. Survival in the business environment.

How they improve efficiency Systems such as ERP, SCM and TPS automate and integrate processes, reduce errors, delay and cost, optimize inventory and speed up customer service. Reaching operational excellence lets a firm produce more with fewer resources.

How they improve decision making MIS, DSS, ESS and business intelligence give managers accurate, timely and relevant information through reports, dashboards and models. Better information reduces reliance on guesswork, which helps managers respond faster to market change and make better strategic choices.

Collaboration is working with others to achieve shared and explicit goals. Social business is the use of social networking platforms (internal and external) to engage employees, customers and suppliers.

Benefits

• Faster communication and decision making through shared tools (email, wikis, shared documents, video conferencing, Teams/Slack).
• Better use of knowledge, as employees find experts and share best practice.
• Reduced travel and communication costs; support for remote and global teams.
• Greater customer engagement and quicker feedback.
• More responsive, flexible organization with a flatter hierarchy and better employee satisfaction.

Productivity: less time searching for information, fewer meetings and less duplicated work, because teams work in parallel on shared documents.

Innovation: diverse people exchange ideas, crowdsourcing brings customer and employee ideas, and groups can solve problems faster and more creatively, leading to better products and processes.

Supply chain management (SCM) system

A supply chain is the network of organizations and processes that gets raw materials from suppliers, converts them into products, and distributes them to customers. A supply chain management system automates and coordinates the flow of materials, information and payments among suppliers, the firm, distributors and customers. It consists of supply chain planning systems (demand forecasting, production and distribution planning) and supply chain execution systems (managing warehouses, transport and delivery).

How it optimizes operations

• Shares demand and inventory data with partners, which reduces the bullwhip effect (distortions of demand as it moves upstream).
• Cuts inventory and holding costs through just-in-time delivery.
• Better forecasting and production scheduling reduce stockouts and waste.
• Faster order fulfilment and delivery, improving customer service.
• Cost reduction through efficient sourcing and transport routing.
• Improved coordination and visibility across partners, helping manage disruptions.

Examples: Walmart's retail link, Dell's build-to-order model.

Artificial intelligence (AI) builds systems that mimic human behaviour such as learning, reasoning and perception. Machine learning (ML) is a subset in which software learns patterns from data without being explicitly programmed.

Contributions to modern business operations

• Customer service – chatbots and virtual assistants answer queries 24/7.
• Marketing and sales – recommendation engines, customer segmentation and personalized offers (Amazon, Netflix).
• Finance and risk – fraud detection, credit scoring, algorithmic trading.
• Operations and supply chain – demand forecasting, route optimization and predictive maintenance reduce cost and downtime.
• Human resources – resume screening and workforce analytics.
• Decision support – analysing large data sets for patterns and predictions.
• Automation – robotic process automation of repetitive tasks, computer vision in quality control.

AI brings efficiency, accuracy and new products, but also raises issues of bias, privacy, job displacement and transparency.

Business value of improved decision making

Decisions are made at the strategic, management and operational levels. Better decisions have direct financial impact. Examples: more accurate forecasts of demand, better pricing, better supplier selection, faster responses to customers. The value of the information systems that support decision making can be seen in:

• Higher revenue and lower cost – optimal stock levels, targeted marketing, reduced waste.
• Faster response – real-time dashboards allow quick reaction to market changes.
• Reduced risk and error – decisions based on data rather than intuition.
• Better resource allocation and improved productivity.
• Customer satisfaction and retention through informed service decisions.
• Sum of many small operational decisions – thousands of routine daily decisions together have large financial effect.

Impact on organizational success: firms with better decisions achieve higher profitability, competitive advantage and growth, and are more adaptable. Poor decisions (inaccurate, late or incomplete information, or bias) lead to losses and failure.

Middle managers make semi-structured, tactical decisions such as budgeting, scheduling, staffing and monitoring performance against plan. Business intelligence (BI) helps them in these ways:

• Consolidated data – BI draws on data warehouses and data marts to give one version of the truth.
• Reporting and dashboards – production reports, parameterized reports, scorecards and exception reports show KPIs for their units.
• Analysis tools – OLAP, drill-down and slicing allow multidimensional analysis (e.g. sales by region, product and period).
• Ad hoc queries and data mining reveal trends and relationships without depending on IT staff.
• Predictive analytics and what-if analysis support forecasting and planning.
• Monitoring – alerts show deviations from targets so managers can take corrective action.

This gives middle managers timely, accurate information for better planning and control.

E-government models include G2C (citizen services), G2B (business), G2G (between agencies) and G2E (employees).

Relational vs nonrelational databases

Cloud databases are provided as a service over the Internet (e.g. Amazon RDS, Google Cloud SQL). They offer pay-as-you-go cost, scalability, availability, automatic backup and reduced administration, though security and control are concerns.

Blockchain is a distributed, tamper-resistant ledger where records are grouped in cryptographically linked blocks verified by network consensus. It gives trusted, transparent and auditable data without a central authority, useful for financial transactions, supply chain tracking and contracts.

(a) Impacts of information systems on organizations

• Economic impact: IT substitutes for labour and capital; it reduces transaction and agency costs, so firms can shrink in size or grow without proportionate management.
• Organizational and behavioural impact: flatter structures with fewer management levels, more decentralized decision making, empowered employees, and faster information flow. Resistance to change may arise from politics, culture and routines.
• Operational impact: automated and redesigned processes improve efficiency, quality and speed.
• Strategic impact: new products, business models and competitive advantage.
• Workforce impact: changes in skills needed, jobs lost or created, and work from remote locations.

(b) Real-world ethical dilemmas in MIS

Ethical dilemmas arise when interests conflict and no option is clearly right. Examples:

• A company monitors employees' email and Internet use for productivity; this conflicts with their privacy.
• Selling or sharing customer data with advertisers versus protecting privacy (e.g. Facebook-Cambridge Analytica).
• Whether to release software with known minor bugs to meet a deadline.
• Using AI for hiring or credit scoring that may be biased.
• Whistleblowing on unethical practices at the risk of one's job.
• Copying software or content for use without a licence.
• Whether to automate jobs when it displaces workers.

Such dilemmas are resolved with ethical principles (Golden Rule, utilitarian, risk aversion), laws and professional codes of conduct.