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This study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability.

Selection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria.

The results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed.

Challenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency.

The novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.

The study aims to address a critical gap in existing healthcare payment schemes and care service pricing by recognizing the influential role of patients' decisions on self-management efforts. These decisions not only impact health outcomes but also shape the demand for care, subsequently influencing care costs. Despite the significance of this interplay, current payment schemes often overlook these dynamics. The research focuses on investigating the implications of a novel behavior-based payment scheme, designed to align incentives and establish a direct connection between patients' decisions and care costs. The primary objective is to comprehensively understand whether and how this innovative payment scheme structure influences key stakeholders, including patients, care providers, insurers and overall social welfare.

In this paper, we propose a game-theoretical model to incorporate the performance of self-management with the demand for healthcare service, compare the patient's effort decision for self-management and provider's price decision for healthcare service under a behavior-based scheme with that under two implemented widely payment schemes, that is, co-payment scheme and co-insurance scheme.

Our findings confirm that the behavior-based scheme incentives patient self-management more than current schemes while reducing their possibility of seeking healthcare service, which indirectly induces the provider to lower the price of the service. The stakeholders' utility under various payment schemes is sensitive to the cost of treatment and the perceived health utility of patients. Especially, patient health awareness is not always benefited provider profit, as it motivates patient self-management while diminishing the demand for care.

We provide a novel framework for characterizing behavior-based payment schemes. Our results confirm the need for modification of the current payment scheme to incentivize patient self-management.

COVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.

The eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.

The empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.

The present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.

The pharmaceutical industry is one of the most essential areas of health in any country. It is defined as a system of processes, operations and organizations involved in discovering, developing and producing drugs. The supply chain in the pharmaceutical field is one of the most important strategic issues in the pharmaceutical and health-care industries. The purpose of this study is to reduce the total cost of the supply chain network and reduce the amount of distribution scheduling.

In this study, the authors designed a drug supply chain network with uncertainty-related corruption. The optimal number and location of potential facilities, the optimal allocation of flow between facilities, the optimal routing of vehicles and the optimal amount of inventory in production and distribution center warehouses were determined to achieve these two objective functions.

In evaluating the small sample size problem, it was found that the comprehensive benchmarking method was more efficient than the other methods in obtaining the mean index of the first objective function. The utility function method has also proved its efficiency in obtaining the mean of the second objective function indices, the spacing index and the computational time. Because of the inefficiency of GAMS software in resolving size issues, the modified NSGA II and MOPSO algorithms with modified priority-based encryption have been used. First, using the Taguchi method, the initial parameters of the metaheuristic algorithms are adjusted, and then, 15 sample problems are designed in larger sizes. To avoid generating random data, five problems were equally designed, and the averages of objective functions and metrics of met heuristic algorithms (number of efficient solutions, maximum expansion index, spacing index and computational time) were analyzed as the basis of evaluation and comparison. Therefore, using all the indicators and results of the NSGA II algorithm is recommended.

In this research, a biobjective modeling approach is proposed to minimize the total costs of the supply chain network (construction costs, storage costs and product transportation costs between centers) and advertising costs and to minimize distribution and transportation scheduling across each level of the supply chain network.

Efforts to implement supplier selection and order allocation (SSOA) approaches in small and medium-sized enterprises (SMEs) are quite restricted due to the lack of affordable and simple-to-use strategies. Although there is a huge amount of literature on SSOA techniques, very few studies have attempted to address the issues faced by SMEs and develop strategies from their point of view. The purpose of this study is to provide an effective, practical, and time-tested integrated SSOA framework for evaluating the performance of suppliers and allocating orders to them that can improve the efficiency and competitiveness of SMEs.

This study was conducted in two stages. First, an integrated supplier selection approach was designed, which consists of the analytic hierarchy process and newly developed measurement alternatives and ranking using compromise solution to evaluate supplier performance and rank them. Second, the Wagner-Whitin algorithm is used to determine optimal order quantities and optimize inventory carrying and ordering costs. The joint impact of quantity discounts is also evaluated at the end.

Insights derived from the case study proved that the proposed approach is capable of assisting purchase managers in the SSOA decision-making process. In addition, this case study resulted in 10.89% total cost savings and fewer stock-out situations.

Criteria selected in this study are based on the advice of the managers in the selected manufacturing organizations. So the methods applied are limited to manufacturing SMEs. There were some aspects of the supplier selection process that this study could not explore. The development of an effective, reliable supplier selection procedure is a continuous process and it is indeed certainly possible that there are other aspects of supplier selection that are more crucial but are not considered in the proposed approach.

Purchase managers working in SMEs will be the primary beneficiaries of the developed approach. The suggested integrated approach can make a strategic difference in the working of SMEs.

A practical SSOA framework is developed for professionals working in SMEs. This approach will help SMEs to manage their operations effectively.

Pay-what-you-want (PWYW) is a pricing strategy implemented in a variety of settings like supermarkets and museums, in which consumers determine the price they are willing to pay for a product or service based on their perceived utility. The authors propose an analytical model to investigate the impact of PWYW delivery pricing on the online food delivery (OFD) platforms.

Using a game-theoretic model, the authors characterize the equilibrium as a function of the platform's average delivery cost and the consumer's social preferences parameters like fairness and reciprocity. The authors derive the parametric conditions under which PWYW generates higher profits for the platform compared to the traditional pay-as-asked delivery pricing.

For the PWYW strategy to be profitable, the average delivery cost to the platform should be low. Therefore, OFD platform managers should focus on reducing delivery costs. The authors also identify the feasible region in which the platform managers need to maintain the consumer's social preferences.

Under PWYW, the authors recommend that the platform managers impose a minimum delivery fee which consumers can use as a benchmark to minimize zero delivery fee payments and consumers' free-riding tendencies simultaneously. This allows OFD platforms to extract online orders from highly price-conscious consumers.

This is one of the first studies to explore the innovative application of PWYW to a particular segment of delivery pricing in OFD platforms. The authors establish that the overall consumer surplus and social welfare are higher under the PWYW strategy, forming a solid ground for its implementation in OFD platforms.

The purpose of this study is to investigate the pricing strategies for knowledge payment products, taking into account the quality level of pirated knowledge products, in the context of platform copyright supervision.

This study abstracts the knowledge payment transaction process and aims to maximize producer's revenue by constructing a pricing model for knowledge payment products. It discusses pricing strategies for knowledge payment products under two scenarios: traditional supervision and blockchain supervision. The analysis explores the impact of pirated knowledge products quality level and blockchain technology on pricing strategies and consumer surplus, while providing threshold conditions for effective strategies.

Deploying blockchain technology in platform operations can significantly reduce costs and increase efficiency. In both scenarios, knowledge producer needs to balance factors such as the quality of pirated knowledge products, the supervision level of platform, and consumer surplus to dynamically adjust pricing strategies in order to maximize his own revenue.

This study enriches the literature on the pricing models of knowledge payment products and has practical significance in guiding knowledge producer to develop effective pricing strategies under copyright supervision.

This exploratory study examines and comprehends the relative importance of mobile app attributes from a consumer perspective. Both quantitative and qualitative analysis approaches explore users' behavior and attitudes toward the priorities of mobile app attributes and preferences, identifying correlations between attributes and aggregating individual attributes into groups.

Online convenience sampling and snowball sampling resulted in 417 valid responses. The numerical data are analyzed using the relative to an identified distribution (RIDIT) scoring system and gray relational analysis (GRA), and qualitative responses are investigated using text-mining techniques.

This study finds enhanced nuances of user preferences and provides data-driven insights that might help app developers and marketers create a distinct app that will add value to consumers. The latent semantic analysis indicates relationship structure among the attributes, and text-based cluster analysis determines the subsets of attributes that represent the unique functions of the mobile app.

This study reveals the essential components of mobile apps, paying particular attention to the consumer value component, which boosts user approval and encourages prolonged use. Overall, the results demonstrate that developers must concentrate on its functional, technical and esthetic features to make an app more exciting and practical for potential users.

Most scholarly research on apps has focused on their technological merits, aesthetics and usability from the user's perspective. A post-adoption multi-attribute app analysis using both structured and unstructured data is conducted in this study.

Contract Manufacturers (CM, factory) can cultivate factory brand products by imitating Original Equipment Manufacturers' (OEM, brand owner) National Brand products, and compete with OEM through the online retailer, that is, factory encroachment. In practice, few consumers can identify the quality of those two products in the online market. Implementing blockchain technology (BTI) can help all consumers identify product quality but may change the operation decisions and incur implementation costs. This study aims to explore how will the BTI strategies affect participants' operation performance under the factory encroachment and delve into the decisions regarding NB product quality and CM encroachment.

This study constructs a three-level outsourcing supply chain comprising one contract manufacturer (CM, factory), one original equipment manufacturer (OEM) and one online retailer. By utilizing the Stackelberg game, the authors first compared the results between two strategic decisions of BTI and no-BTI by online retailers under the factory encroachment scenario. Then, the NB product quality decision and the CM's encroachment decision are also investigated.

BTI strategy can benefit all participants (triple win), which both occurs in exogenous and endogenous quality cases, and the triple win area will expand (shrink) as the BTI cost decreases (increases). In addition, the OEM will improve product quality to confront competition from the CM, and the OEM may not always benefit from the BTI, it depends on the maturity of the market. Interestingly, BTI could improve the consumer surplus when the proportion of novice consumers is low. Finally, this study also investigates the extended case that CM always encroaches into the market whether the online retailer choose BTI or not, which hurts OEM's profit and decreases the product quality.

This study sheds light on the strategic decisions of online retailers' BTI regarding supply chain members' profits, consumer surplus and social welfare under factory encroachment. It also demonstrates that the BTI strategy, under different quality decisions (endogenous and exogenous), can be more profitable for chain members and consumers.

To study these issues, the authors chose a GFSC with one producer and one material supplier as research object, the supplier will offer green material to the producer and the producer will make green food using green production technology. Then, the authors proposed that consumers' perceived value was determined by the trustworthiness levels of the related green and quality-safety information provided by the supplier and the producer. Then, considering the trustworthiness levels of the green and quality information provided by the supplier and the producer, the authors improved the demand function. Afterwards, we constructed four investment models and their income models are built and then a cost-sharing and revenue-sharing contract (hereafter, CSRS) was adopted to coordinate the GFSC.

With the growth of consumers environmental awareness and life level, consumers' requirements for green and high quality food are growing. In recently years, to increase consumers' perceived trustworthiness on the product greenness and quality levels, stakeholders in green food supply chain (hereafter, GFSC) start to adopt the blockchain-based traceability system (hereafter, BLTS). For investors, they need to know the investment conditions and how to coordinate the GFSC.

(1) When the revenue-sharing coefficient is less than three-fourths and higher then a certain vaule, the cost-sharing and revenue-sharing contract can make the GFSC coordinate. (2) The investment cost threshold of the BLTS has a positive relationship with the trustworthiness improvement levels of the green and quality information, the green degree of food products and the quality of food products. (3) In the proposed four investment situations, as the growth of consumers perceived credibility coefficient about the greenness information and the quality information, chain members' revenues will increase. In addition, comparing with co-investing the BLTS, benefits of chain members are lower than them in the sole investment model.

(1) The demand function we proposed can help chain members forecast market demand to support production or ordering decisions. (2) The investment decision policies can offer a theoretical reference for chain members to use the BLTS. (3) The CSRS will offer the theoretical reference for coordinating the supply chain after using the BLTS. Furthermore, our study method can be referenced by other scholars. (4) The study method can offer a method reference for researchers who do a similar discussion in a manufacturing supply chain. Although, our research cannot guide the industrial practices, it can serve as a reference of the similar research in industry.