(a) (i) What is e-business architecture? Which technological innovations have had a major impact on it? 5 marks (ii) Describe the…

(a)(i) E-business architecture

E-business architecture is the layered framework that connects a firm's web, mobile and partner channels with its internal systems. Typical layers: a presentation layer (website, app, portal), an application layer (catalogue, cart, payment, CRM, ERP, supply-chain logic), a data layer (databases, analytics) and a network and security layer (hosting, encryption, firewalls), linked by APIs and integration middleware. Major technological influences: the internet and broadband, smartphones, cloud computing, APIs and microservices, big-data analytics and AI, digital payments such as UPI, and, increasingly, IoT and blockchain.

(a)(ii) Pillars and interactions of e-governance

The four pillars are people (citizens, officials, leaders), process (re-engineered, simplified procedures), technology (networks, software, data) and resources (funds, skills, infrastructure). The interactions are G2C (services such as certificates and tax filing), G2B (licences, procurement, tax), G2G (inter-department data sharing and coordination) and G2E (government and its employees, such as payroll and HR).

(a)(iii) India's e-governance efforts

The National e-Governance Plan (2006) set up mission-mode projects, common service centres, state data centres and the State Wide Area Networks. Digital India (2015) added BharatNet connectivity, DigiLocker, UMANG, MyGov, e-Office and GeM. Aadhaar, the JAM trinity and Direct Benefit Transfer reduced leakage, UPI built digital payments, MCA21 and GST networks serve businesses, and e-Courts and e-Hospital serve citizens. Gaps remain in the digital divide, interoperability, language access and data protection.

(b)(i) Graphical solution of the linear programme

Minimise C = 5x₁ + 6x₂ subject to x₁ + x₂ = 1,000; x₁ ≤ 300; x₂ ≥ 150; x₁, x₂ ≥ 0.

Because of the equality, the feasible region is the part of the line x₁ + x₂ = 1,000 satisfying the other conditions. Then x₂ = 1,000 − x₁ ≥ 150 gives x₁ ≤ 850, which is slack beside x₁ ≤ 300. So the feasible segment runs from (0, 1,000) to (300, 700).

Corner values: at (0, 1,000), C = 6,000; at (300, 700), C = 5 × 300 + 6 × 700 = 5,700.

Since C = 6,000 − x₁ along the line, cost falls as x₁ rises. Optimum: x₁ = 300, x₂ = 700, minimum cost C = 5,700.

(b)(ii) Significance of drug versus sugar pills

H₀: the treatment and the reaction are independent (no difference in effect). H₁: they are associated.

Each treatment group has 250 patients, so the expected frequencies are, for both rows, 140 (helped), 35 (harmed) and 75 (no effect).

χ² = Σ(O − E)²/E = 2 × (10²/140 + 5²/35 + 5²/75) = 2 × (0.714 + 0.714 + 0.333) ≈ 3.52.

Degrees of freedom = (2 − 1)(3 − 1) = 2. The 5% critical value is 5.99. Since 3.52 < 5.99, H₀ is not rejected.

Conclusion: the data do not show a significant difference between the drug and sugar pills.

(c)(i) Decay curve of new product ideas for an e-bike

The decay curve shows that ideas fall away at each stage and only a few reach the market. For an e-bike, many ideas (battery swapping, subscriptions, folding frames, smart connected features) are generated; screening drops those off-strategy or infeasible; concept testing with target riders removes those with low appeal; business analysis checks cost, price, range and demand; prototype development and test-marketing remove still more; and only one or two reach commercial launch. Firms therefore start with many ideas and invest more only as uncertainty falls, so as to keep failure costs low.

(c)(ii)(A) Control limits

Average proportion defective p̄ = 150/1,000 = 0.15; sample size n = 10.

σₚ = √(p̄(1 − p̄)/n) = √(0.15 × 0.85/10) = 0.113.

95% limits (Z = 1.96): p̄ ± 1.96σₚ = 0.15 ± 0.221, so UCL = 0.371 and LCL = 0 (negative value taken as zero). In defective units per sample of 10: centre line 1.5, UCL ≈ 3.7, LCL = 0.

(c)(ii)(B) Is the process in control?

The sample defectives 3, 4, 2, 0, 7 are proportions 0.3, 0.4, 0.2, 0.0, 0.7. On the chart, the points 0.3, 0.2 and 0.0 lie within the limits; 0.4 (4 defectives) and 0.7 (7 defectives) lie above the UCL of 0.371 (3.7 defectives). The process is therefore out of control. With wider 3σ limits (UCL = 4.9 defectives), the sample of 7 would still signal.