Search results

The purpose of this paper is to propose a quantitative method for identifying multiple and hierarchical knowledge trajectories within a specific technological domain (TD).

The proposed method as a patent-based data-driven approach is basically based on patent classification systems and patent citation information. Specifically, the method first analyzes hierarchical structure under a specific TD based on patent co-classification and hierarchical relationships between patent classifications. Then, main paths for each sub-TD and overall-TD are generated by knowledge persistence-based main path approach. The all generated main paths at different level are integrated into the hierarchical main paths.

This paper conducted an empirical analysis by using Genome sequencing technology. The results show that the proposed method automatically identifies three sub-TDs, which are major functionalities in the TD, and generates the hierarchical main paths. The generated main paths show knowledge flows across different sub-TDs and the changing trends in dominant sub-TD over time.

To the best of the authors’ knowledge, the proposed method is the first attempt to automatically generate multiple hierarchical main paths using patent data. The generated main paths objectively show not only knowledge trajectories for each sub-TD but also interactive knowledge flows among sub-TDs. Therefore, the method is definitely helpful to reduce manual work for TD decomposition and useful to understand major trajectories for TD.

The idea is to develop a condition-based maintenance (CBM) model for continuously monitored degrading systems under imperfect maintenance. The system is subjected to random shocks, inspection, corrective maintenance (CM) and preventive maintenance (PM); respective thresholds are set to make decisions.

The generalized renewal process is considered for degradation modeling, and the maximum likelihood estimation method is used for parameter estimation. Optimum replacement time is derived by minimizing downtime per unit time (DT).

Optimum replacement time is derived by minimizing DT. The paper provides insight into the virtual age model’s applicability and efficacy in CBM policy-based decision-making.

This paper assesses the CBM policy for continuously degrading systems under random shocks and imperfect maintenance. An operating system faces five events, i.e. CM, PM, inspection, random shock and sudden failure, during its lifetime. The combination and timing of these mutually exclusive events decide the degradation path in addition to the system's natural degradation (wear-out) dynamics when it is not tampered with in any way. Sequential modeling of such events for a degradation dataset will provide maintenance estimation and prediction parameters.

In general, CBM models do not consider CM to be part of the maintenance policy. It is debatable whether to include CM as part of a CBM policy. In our case, we consider CM in CBM policy formulation, assuming: (1) fail-repair: the system is restored to its operating state after failure with repair; (2) salvage: disposal of non-repairable part or material from the system, i.e. CM actions are a result of minor component-level failures. System failure is referred to as a case involving rebuild/complete system replacement. We assume CM for minor failures and acknowledge that failure can be delayed using CBM but cannot be avoided entirely. Therefore, the consideration of CM in the CBM model will make the model more generic and comprehensive.

The paper seeks to ease the implementation of predictive maintenance policy in industry using the root cause analysis technique, and to compare the reliability and cost effectiveness of root cause based maintenance (RCBM) relative to conventional corrective maintenance (CM).

The system is modularized into its components and maintenance schedules are developed based on each component's individual degradation trends. The effectiveness of RCBM over CM is studied by analyzing system reliability patterns and total maintenance cost functions obtained through empirical cost models, accounting for yield and production loss, maintenance, replacement and catastrophic failure costs. Cost variations for various possible failure distribution parameter values (β, η) under the CM and RCBM policies are also obtained. The proposed methodology is tested in a real aircraft failures case study.

RCBM is generally more effective over CM in achieving timely maintenance at optimal cost (savings up to 65 percent) while keeping high system reliability, for a wide range of (β, η) values. However, CM could still be beneficial for a restricted range of large (β, η).

Industry should consider shifting from CM to adopt the proposed RCBM policy, which is proved to be more efficient in most cases. The implementation is not necessarily complex.

The effectiveness of RCBM over CM in terms of reliability and cost considerations is clearly illustrated. This paper justifies the need to shift from CM to RCBM, which brings us closer to a practical implementation of predictive maintenance. This work also serves as a simple and valuable guide to implementation for maintenance and operational managers in production industries.

Strategy.

MBA level. The case can be used primarily for the following courses: strategic management, competitive strategy. It can also be used for courses on: international business, international business environment, business marketing.

Intense competition and a turbulent economic environment posed problems for Infosys, a leading information technology (IT) company in India. Infosys lost market share and its second position in the IT industry to Cognizant. An adverse economic environment affected its clients' IT spending and introduced severe price-based competition in the market. Infosys' business model operated on charging price premium from clients, and the company never compromised on its margins. The company was forced to revaluate, as outsourcing, the main revenue earner for Infosys was experiencing commoditization, and other players were willing to compromise on margins. The Indian IT industry had moved up the value chain and competitors were offering consulting services, where there was huge scope for differentiation. Infosys did not have the requisite resources to compete in this domain. Decline in share prices, negative investor sentiments, downward revision of revenue guidance targets, loss of large clients, higher attrition rates, and visa problems in the US market (Infosys earned more than 60 percent revenues from this market) added worries for the company. In response to these challenges, Infosys initiated Strategy 3.0, wherein the company planned to move up the value chain and offer consulting services and other high-end solutions to clients. This was a shift from its predominantly outsourcing-based revenue model. The company acquired Lodestone to hasten implementation of Strategy 3.0. Initial analysis, however, suggested that Infosys was merely aping Cognizant's well-established strategy. Infosys also needed to tackle perceptual issues regarding its competencies.

The instructor can use this case to facilitate the understanding of: the impact of an intensely competitive environment on a company's strategy, how changes in the competitive landscape and business environment can erode sources of competitive advantage for an incumbent, the impact of a client's business environment on the vendor's business, the concept of value chain and analyze how companies in an industry move up in the value chain, the concept of business model, and how environmental changes can impact a hitherto robust business model of a company, evolution of business model over a period of time with changes in the business environment, the internal conflict between ideals and values versus revenues and market share for a company, key resources and capabilities that shape the differential advantage for an IT company, designing and implementing strategic solutions, the evolution of the Indian IT industry.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

India launched Smart City Mission in 2015 with an objective of development of 100 smart cities with a completion deadline in 2019 that was extended till June 2023. Smart City Mission is an important mission in the backdrop that urban population in India is projected to be 67.55 crore in 2035 from 48.30 crore in 2020. Further, by 2035, the percentage of population in India at mid-year residing in ‘urban area’ will be 43.2% as per the United Nations – Habitat's World Cities Report 2022 and it will be just next to China's urban population in 2035 that is projected at 1.05 billion. A recent World Bank report (2022) estimated that India will need to invest US (United States) $840 billion over the next 15 years, i.e. US $55 billion per annum – into urban infrastructure if it has to effectively meet the needs of its fast-growing urban population.

This chapter focuses on financing of sustainable smart cities in India. This chapter summarises financing options explored by the government in the beginning, challenges faced in financing of Smart City Mission in India over a period due to various developments such as pandemic, delay in execution of projects under the Smart City Mission, among others. Finally, suggestions have been given for making financing means effective and sustainable. These suggestions are based on the gaps between the ‘financing means thought of’ in the beginning and ‘financing means actually applied’ while executing Smart City Mission in India. Financing part is worth exploring in the background that India had the fiscal deficit at 3.9% of Gross Domestic Product (GDP) in 2015–2016 and most recently, the country had the fiscal deficit at 6.71% of GDP in FY22. And the country also dealt with the pandemic like other economies and provided COVID-19 vaccine free of cost to all citizens. Insights are useful for any other economy with a similar sustainable and smart city mission while facing resource constraints.

The learning outcomes of this study are as follows:

1. the benefits of differential pricing over uniform pricing;

2. the differences between second- and third-degree price discrimination;

3. the rationale for charging different prices for segments having different willingness to pay; and

4. how different prices for the same product can lead to perceptions of unfairness and how companies might manage such an issue.

This case outlines the decisions that Adar Poonawalla, the CEO of Serum Institute of India (Serum), had to make in late April 2021 concerning its pricing for the COVID-19 (Covid) vaccine. Serum was the world’s largest manufacturer of vaccines, and its Covishield vaccine had received regulatory approval, but faced an unusual challenge and opportunity. In most countries, governments had procured Covid vaccines from manufacturers and then delivered the vaccines to consumers free of cost. But in India, there was a three-tier pricing system. While the Government of India had committed to free vaccines in government-run public hospitals, it also allowed vaccine makers to directly sell vaccines to state governments, as well as private hospitals, who were at liberty to charge consumers for the vaccines. This created an interesting pricing dilemma for Serum: as different customers had different willingness to pay, should Serum use differential pricing? Would such a tiered pricing system be considered fair? How many different price points should Serum maintain? By exploring these and related decisions that Poonawalla had to make, the case is intended to teach price discrimination.

The case is intended for graduate-level courses in marketing, pricing and economics. This case illustrates the principles of differential pricing/price discrimination. More specifically, it highlights pricing strategies motivated by second- and third-degree price discrimination in an emerging market’s health-care context. From the information in the case, the student can learn to apply the concepts of second- and third-degree price discrimination in marketing. After working through the case and assignment questions, instructors will be able to help students understand the following concepts:

Teaching objective 1: the benefits of differential pricing over uniform pricing.

Teaching objective 2: the differences between second- and third-degree price discrimination.

Teaching objective 3: the rationale for charging different prices for segments having different willingness to pay.

Teaching objective 4: how different prices for the same product can lead to perceptions of unfairness and how companies might manage such an issue.

Teaching notes are available for educators only.

CSS 8: Marketing

The paper aims to explain how agency theory can be used to inform our understanding of the dynamics surrounding supply chain behaviours and relationships.

A structured review of the literature using a three‐stage refinement process is used. The articles were sourced through online databases and keyword classifications, such as “agency theory”, “principal‐agent relationships” and “supply chain management”. The search initially identified over 86 articles. After further screening these were reduced to 19 for final assessment and comparison.

Despite agency theory's prevailing descriptive and predictive qualities there is scarcity in its application to the SCM discipline. The authors posit that agency theory provides valuable insights for relationship engineering within supply chains where social, political, legal and behavioural dynamics dominate.

It is a critical task for managers to understand and mitigate abnormal behaviours across the supply chain. Agency theory serves this need by providing them with a useful tool to respond to transaction cost dilemmas through contractual and non‐contractual remedies.

This is one of the first studies that examines the current state of agency theory application in the SCM literature and suggests potential avenues for future research.

This paper aims to discuss and test the claim that utilization of the personalization techniques can be valuable to improve the efficiency of collaborative information retrieval (CIR) systems.

A new personalized CIR system, called PERCIRS, is presented based on the user profile similarity calculation (UPSC) formulas. To this aim, the paper proposes several UPSC formulas as well as two techniques to evaluate them. As the proposed CIR system is personalized, it could not be evaluated by Cranfield, like evaluation techniques (e.g. TREC). Hence, this paper proposes a new user‐centric mechanism, which enables PERCIRS to be evaluated. This mechanism is generic and can be used to evaluate any other personalized IR system.

The results show that among the proposed UPSC formulas in this paper, the (query‐document)‐graph based formula is the most effective. After integrating this formula into PERCIRS and comparing it with nine other IR systems, it is concluded that the results of the system are better than the other IR systems. In addition, the paper shows that the complexity of the system is less that the complexity of the other CIR systems.

This system asks the users to explicitly rank the returned documents, while explicit ranking is still not widespread enough. However it believes that the users should actively participate in the IR process in order to aptly satisfy their needs to information.

The value of this paper lies in combining collaborative and personalized IR, as well as introducing a mechanism which enables the personalized IR system to be evaluated. The proposed evaluation mechanism is very valuable for developers of personalized IR systems. The paper also introduces some significant user profile similarity calculation formulas, and two techniques to evaluate them. These formulas can also be used to find the user's community in the social networks.