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The study aims to analyze the effectiveness of digital rhetoric persuasion on GenZ purchase decision. Digital rhetoric (DR) is an art of persuasion used in social media communication to shape and influence the course of an individual. It has been used in social media advertisements (SMAs) to increase its perceived effectiveness. GenZ consumers are more vibrant than previous generations’ consumers because of high levels of literacy and capacity to adapt to new technology. Therefore, understanding the effects of rhetorical support decisions to act on and mold consumers’ reasoning and judgment is particularly significant in relation to GenZ purchasing decisions and the rhetorical persuasive methods. Concurrently, the moderating effect of generation cohort theory also needs to be examined.

The threshold model for consumers’ purchase decisions in the form of logistic regression has been applied to examine the impact of DR through SMAs on the purchase intention (PI) of GenZ consumers. Simultaneously, the moderating effect of generation cohort theory is being examined by comparative analysis of different generations’ PI moderation by DR effect.

The results of the current study reveal that DR via SMAs has a positive and significant influence on GenZ consumers’ PI, whereas other older generation consumers do not get similarly affected by the same.

In an emerging economy like India, where 30% of the population belongs to the GenZ category and the digital advertising industry is growing by double digits, the present study takes a novel approach to examine the impact of DR via SMAs on GenZ consumers’ PI. Concurrently, it also provides an understanding of the moderation effect of generation cohort theory on perceived effectiveness of DR.

Today the role of industry 4.0 plays a very important role in enhancing any supply chain network, as the industry 4.0 supply chain uses Big Data and advanced analytics to inform the complete visibility. Latest data are available to bring clarity and support real-time decision-making in the entire supply chain that’s why adopting optimization techniques such as lean manufacturing and lean supply chain concept for enhancing the supply chain network of the organizations is a good idea and would benefit them in increasing their cost efficiency and productivity. The purpose of this work is to develop a technique, which may be useful for future researchers and managers to identify and classification of the significant lean supply chain enablers.

In this paper, the authors considered hybrid analytical hierarchy process to find the ranking of the identified lean supply chain enablers by calculating their weightage. Interpretive structural modeling (ISM) is applied to develop the structural interrelationship among various lean supply chain management enablers. Considering the results obtained from ISM the Matrices d'Impacts Croises Multiplication Appliqué a un Classement (MICMAC) analysis is done to identify the driving and dependence power of Lean Supply Chain Management Enablers (LSCMEs).

Further, the best results applying these methodologies could be used to analyze their inter-relationships for successful Lean supply chain management implementation in an organization. The authors developed an integrated model after the identification of 20 key LSCMEs, which is very helpful to identify and classify the important enablers by ISM methodology and explore the direct and indirect effects of each enabler by MICMAC analysis on the LSCM implementation. This will help organizations optimize their supply chain by selective control of lean enablers.

For lean manufacturing practitioners, the result of the study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process, as well as in enhancing the supply chain.

This paper is the first research paper that considered firstly deep literature review of identified lean supply chain enablers and second developed structured modeling of various lean enablers of supply chain with the help of various methodologies.

Using the two waves of the India Time Use Survey, 1998–1999 and 2019, we document a 110-minute (30%) increase in average daily learning time. The largest offsetting decrease was in work time: 61 minutes. The composition of leisure changed, with television rising by 19 minutes, while talking fell by 10 minutes and games by 17 minutes. We then implement a Gelbach decomposition, showing that 68 minutes of the unconditional learning increase are predicted by demographic covariates. Of these predictors the most important are a child's state of residence and usual principal activity, which captures extensive-margin transitions into schooling.

The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.

This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.

For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.

The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.

This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.

The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.

The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.

Warehouse receipt-based financing (WRF), an innovative instrument with its structure embedded in the agricultural value chain can potentially address farmers' concerns about timely credit access and accessible remunerative markets. However, studies indicate farmers' exclusion from currently practiced WRF mechanisms across developing countries. Transaction cost and lack of assured remunerative markets post storage are the challenges thwarting farmers' participation. The study explores how these challenges can be addressed by analyzing a case study. The finding will help in coming up with a farmer-inclusive WRF mechanism.

The study uses a case study as an analysis tool. Primary data is gathered through farmers. Descriptive statistics and partial least squares (PLS) approach to structural equation modeling methodology has been adopted for empirical testing of the hypothesis of the study. The study uses SMART PLS 3.0 for analysis of data.

Single window offering of multiple value chain operations and technological intervention in physical handling substantially reduces transaction costs for farmers. Sustained farmers' participation in the case supports this finding. The presence of an assured market (PAM) is found to have a positive and significant relationship with WRF in the case of beneficiary farmers. The PAM is found to have a negative yet significant relationship with WRF in the case of nonbeneficiary farmers. Critical success factors of the entity KisanMitra stated in the case substantiates a farmer-inclusive WRF mechanism.

The study analyzes a case study of specific geography. However, similarities enlisted across developing countries in the introduction section provide a scope of generalization of findings across developing countries. The identified factors for a farmer-inclusive WRF mechanism will enable the governments, policymakers and development institutions to ascertain and align their WRF implementation measures to inculcate and upgrade these factors to the prospective WRF agents. Future studies can explore the replication of farmer-inclusive WRF mechanisms across other geographies. The studies also explores the role of technological interventions in further reducing the transaction cost and suitable policy modifications to encourage replication of the study in other geopgraphical context.

The study on WRF and the methodology adopted is first of its kind to identify factors for a farmer-inclusive WRF mechanism.

This case study would help students in understanding the dynamics of logistics and logistics vendor roles and contributions to overall business operations. The case study covers real-time information for applying the theoretical knowledge students gain related to the selection of logistics vendor. It would help students to understand and evaluate the dynamics of a new start-up related to cost, profits and dependency; understand and analyze the importance of third-party logistics (3PL) service providers in the supply chain; become aware of the key performance indicators (KPIs) important in the selection of logistics vendor; and develop and create measures for selecting logistics vendors on the basis of KPIs.

This case study was about an innovative start-up operating in the field of organic edible oils. The company catered to end consumers with its indigenous technology and processes. The innovative and healthy products were appreciated by the consumers, as was reflected in the surging demand figures. With the increasing popularity of organic products, the orders were surging. At the same time, issues such as damaged product delivery, increased cost per delivery of small packages and failure to deliver because of unserved pin codes by their logistics partners were being faced by the company. The case discusses the dilemma faced by the protagonist regarding the selection of the right 3PL partner. The case study is suitable for teaching courses in operations and logistics, supply chain management and entrepreneurship-related courses.

This case study is appropriate for postgraduate courses in entrepreneurship, operations management, logistics and supply chain management and general management.

Teaching notes are available for educators only.

CSS9: Operations and logistics.