Search results

This research explores the application of machine learning to optimize pricing strategies in the aftermarket sector, particularly focusing on parts with no assigned values and the detection of outliers. The study emphasizes the need to incorporate technical features to improve pricing accuracy and decision-making.

The methodology involves data collection from web scraping and backend sources, followed by data preprocessing, feature engineering and model selection to capture the technical attributes of parts. A Random Forest Regressor model is chosen and trained to predict prices, achieving a 76.14% accuracy rate.

The model demonstrates accurate price prediction for parts with no assigned values while remaining within an acceptable price range. Additionally, outliers representing extreme pricing scenarios are successfully identified and predicted within the acceptable range.

This research bridges the gap between industry practice and academic research by demonstrating the effectiveness of machine learning for aftermarket pricing optimization. It offers an approach to address the challenges of pricing parts without assigned values and identifying outliers, potentially leading to increased revenue, sharper pricing tactics and a competitive advantage for aftermarket companies.

Despite rapid growth in blockchains, there was limited discussion about non-technical and technical factors on blockchain governance in the extant literature. This study aims to contribute new knowledge to the literature on potential factors affecting the adoption, governance and scale-up of blockchain technologies in the health-care and energy sectors, presented in a holistic framework.

This study adopts the qualitative case study research methodology to research blockchain governance in practice. The authors contacted a national blockchain consortium to conduct their research on the governance issue of blockchain. Two leading case organizations, one from the health-care industry and another from the energy industry, were deliberately selected for their study for their active role and reputation in the consortium and practical experience in blockchain governance.

The developed framework helps identify potential research gaps or concerns on adopting a blockchain as well as assessing blockchain implementation and governance in other industries. Depending on the circumstances, some of the factors can be either drivers or obstacles to further blockchain development. The different forces may also be more or less evident over time as blockchains develop. The two real-world case studies contribute to the information technology governance literature on blockchain governance.

The results of this case studies will be beneficial for developing theories and empirical models to determine antecedents for achieving consensus and trust in blockchain and testing the relationship between these factors and blockchain governance at different levels. As a result, theories related to the governance of blockchain technologies could be further developed.

This case study is designed to enable students to analyze the competitive landscape of a business impacted by technological disruption; evaluate the viability of an organic growth strategy using stakeholder analysis; evaluate the revenue and cost structure of Apollo 24/7 and decide on the future investment strategy; and analyze funding strategies of traditional hospitals versus pure digital players.

To extend its reach, Apollo Hospitals Enterprise (Apollo Hospitals), a leading private sector brick-and-mortar hospital chain in India known for using state-of-the-art technology, launched a unified virtual mobile platform Apollo 24/7 in February 2020, 45 days into the COVID-19 pandemic. The management believed that the digital platform had a unique ecosystem that could not be replicated. The analysts were optimistic about the impact of the decision on the future performance of Apollo Hospitals, as it was expected to lead to higher penetration and increased revenue. They also anticipated the unlocking of value, as and when the venture capitalist (VC) would invest in Apollo Hospitals. However, with increasing operating expenses on account of burgeoning technological and marketing expenses, things did not seem to go going as planned. Three years later, in February 2022 after the Q3 of financial year 2023 results. Suneeta Reddy, the company’s managing director found herself pondering whether the digital platform could boost Apollo Hospitals’ profitability in addition to expanding its reach and increasing affordability when the company missed the analyst estimates. In India, which was then the second most populous country, “incremental access” and “affordability” were what mattered to the patients, However, for the investors and analysts, it was quarter-on-quarter performance. The change in the macroeconomic environment stalled the company’s plan of raising money from VCs.

Furthermore, the financing dilemma also plagued Reddy. She knew there was a difference between financing for conventional businesses that for digital businesses. She also had to take decide between short-term profitability with which investors were obsessed versus long-term sustainability, which involved taking care of stakeholders’ interests.

This case study is basically aimed at postgraduate courses and executive management courses.

Teaching notes are available for educators only.

CSS11: Strategy.

Upon completion of this case study analysis, the course audience is expected to achieve four learning outcomes. In particular, students should be able to conduct a comprehensive organizational diagnosis to uncover the peculiarities of managing a family business; analyze the specific challenges faced by family-owned enterprises in the context of emerging markets; evaluate the succession management practices in family organizations and design a profile of a successful successor; assess the effectiveness of managerial decision-making and provide recommendations for securing the sustainability of a family firm.

This case study unveils the tumultuous story of Vishwanath Shetty, an ambitious entrepreneur who transformed his small venture into a profitable family business with operations in Middle East, Asia and Africa. Since the early establishment of Qontrac International in 1989, he relied on the ownership and management participation of several members of his and his wife’s families. Over the years, Vishwanath was successful in pursuing a strategy of continuous growth and geographic diversification by taking advantage of the business opportunities in several regions and opening up branches in Oman, the United Arab Emirates (UAE), Ghana and India. Yet, almost three decades after its launch, the company was confronted with a number of family, growth and succession management challenges that endangered its survival in the long run. The Shetty family experienced a serious rift due to financial reasons, the performance of the two branches managed by siblings declined, and the old firm structure and management style did not fit well with the newly enlarged and geographically dispersed Qontrac International. To deal with these organizational issues, Vishwanath was faced with an additional dilemma of securing the support of a suitable intra-family candidate who could join the family business and become his successor. By describing the strategic events and family dynamics that shaped the evolution of Qontrac International over time, the case provides an opportunity to assess the effectiveness of managerial decision-making in the context of family firms and provide viable recommendations for ensuring firm survival and longevity.

Upper-level undergraduate audience Graduate audience (in Master of Global Entrepreneurial Management program).

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

Strategy.

Need of circular economy (ce) practices for net-zero and sustainability in construction sector is well known, and thus the need for information flow between current and potential users about materials and processes. Material passports (MPs) are the tool for this information flow. This study aims to examine the research trend about digitalisation and MPs in construction, explore the application of digital technologies (DTs) for information management required for MPs and provide further research directions.

Systematic search and review of literature was conducted adopting both qualitative and quantitative approach for analysis. Firstly, quantitative bibliometric analysis of 201 papers was conducted to get the context from ongoing research around the area and qualitative content and thematic analysis of selected 14 papers were then done to further explore the literature.

Bibliometric analysis suggested building information modelling (BIM) as the most widely studied topic for digitalisation and MPs, which has been studied together with other DTs, whereas blockchain is niched within supply chain and waste management. Qualitative review observed BIM as the most prevalent technology, providing platform for information generation and management for MPs, and most other DTs are applicable mostly for information generation. Artificial intelligence (AI) is useful for information generation, but more suiting for information analysis. Blockchain, on the other hand, is for decentralised and reliable information management.

This study has tried to explore the digitalisation for circularity in construction with focus on information management for MPs. As the ce in construction boils down to information flow and MPs, this study provides the idea about possible applications of DTs for MPs and suggests further research directions for development and use of MPs for ce in construction.

The study has been aimed to search an appropriate process environment for simultaneous optimization of quality‐productivity favorably. Various surface roughness parameters (of the machined product) have been considered as product quality characteristics whereas material removal rate (MRR) has been treated as productivity measure for the said machining process.

In this study, three controllable process parameters, cutting speed, feed, and depth of cut, have been considered for optimizing material removal rate (MRR) of the process and multiple surface roughness features for the machined product, based on L₉ orthogonal array experimental design. To avoid assumptions, limitation, uncertainty and imprecision in application of existing multi‐response optimization techniques documented in literature, a fuzzy inference system (FIS) has been proposed to convert such a multi‐objective optimization problem into an equivalent single objective optimization situation by adapting FIS. A multi‐performance characteristic index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method.

The study demonstrates application feasibility of the proposed approach with satisfactory result of confirmatory test. The proposed procedure is simple, and effective in developing a robust, versatile and flexible mass production process.

In the proposed model it is not required to assign individual response weights; no need to check for response correlation. FIS can efficiently take care of these aspects into its internal hierarchy thereby overcoming various limitations/assumptions of existing optimization approaches.

This study aims to investigate the mediating roles of servant leadership and employee vitality in the relationship between psychological ownership and employee creativity among healthcare workers in Ghana.

A sample of 736 public and private healthcare respondents was selected using a convenience sampling technique. Data collected using a self-reported questionnaire was analyzed via partial least square structural equation modeling.

The findings reveal that psychological ownership directly improves employee creativity, while servant leadership and employee vitality mediate the relationship between psychological ownership and employee creativity separately and complementarily.

The research used self-reported data, increasing the potential for common method variance. However, sufficient care was taken to minimize these limitations.

This research makes valuable contributions to the field of healthcare practice literature. The findings suggest that management of health care entities should focus on creating a workplace culture that cultivates psychological ownership among employees and policies that enhance employee vitality and promote servant behavior to foster employee creativity.

This study represents one of the earliest attempts to examine a theoretical framework that connects servant leadership, employee vitality, employee creativity and psychological ownership within the context of the health service industry.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

According to the Indian Sign Language Research and Training Centre (ISLRTC), India has approximately 300 certified human interpreters to help people with hearing loss. This paper aims to address the issue of Indian Sign Language (ISL) sentence recognition and translation into semantically equivalent English text in a signer-independent mode.

This study presents an approach that translates ISL sentences into English text using the MobileNetV2 model and Neural Machine Translation (NMT). The authors have created an ISL corpus from the Brown corpus using ISL grammar rules to perform machine translation. The authors’ approach converts ISL videos of the newly created dataset into ISL gloss sequences using the MobileNetV2 model and the recognized ISL gloss sequence is then fed to a machine translation module that generates an English sentence for each ISL sentence.

As per the experimental results, pretrained MobileNetV2 model was proven the best-suited model for the recognition of ISL sentences and NMT provided better results than Statistical Machine Translation (SMT) to convert ISL text into English text. The automatic and human evaluation of the proposed approach yielded accuracies of 83.3 and 86.1%, respectively.

It can be seen that the neural machine translation systems produced translations with repetitions of other translated words, strange translations when the total number of words per sentence is increased and one or more unexpected terms that had no relation to the source text on occasion. The most common type of error is the mistranslation of places, numbers and dates. Although this has little effect on the overall structure of the translated sentence, it indicates that the embedding learned for these few words could be improved.

Sign language recognition and translation is a crucial step toward improving communication between the deaf and the rest of society. Because of the shortage of human interpreters, an alternative approach is desired to help people achieve smooth communication with the Deaf. To motivate research in this field, the authors generated an ISL corpus of 13,720 sentences and a video dataset of 47,880 ISL videos. As there is no public dataset available for ISl videos incorporating signs released by ISLRTC, the authors created a new video dataset and ISL corpus.