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The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.”

Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016).

While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%).

The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices.

The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence.

To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

This paper aims to explore the interdisciplinary nature of coordination challenges in the logistic response to food safety incidents while distinguishing the food supply chain positions involved.

This adopts an exploratory qualitative research approach over a period of 11 years. Multiple research periods generated 38 semi-structured interviews and 2 focus groups. All data is analysed by a thematic analysis.

The authors identified four key coordination challenges in the logistics response to food safety incidents: first, information quality (sharing information and the applied technology) appears to be seen as the biggest challenge for the response; second, more emphasis on external coordination focus is required; third, more extensive emphasis is needed on the proactive phase in the logistic response; fourth, a distinct difference exists in the position’s views on coordination in the food supply chain. Furthermore, the data supports the interdisciplinary nature as disciplines such as operations management, strategy and organisation but also food safety and risk management, have to work together to align a rapid response, depending on the incident’s specifics.

The paper shows the need for comprehensively reviewing and elaborating on the research gap in coordination decisions for the logistic response to food safety incidents while using the views of the different supply chain positions. The empirical data indicates the interdisciplinary nature of these coordination decisions, supporting the need for more attention to the interdisciplinary food research agenda. The findings also indicate the need for more attention to organisational learning, and an open and active debate on exploratory qualitative research approaches over a long period of time, as this is not widely used in supply chain management studies.

The results of this paper do not present a managerial blueprint but can be helpful for practitioners dealing with aspects of decision-making by the food supply chain positions. The findings help practitioners to systematically go through all phases of the decision-making process for designing an effective logistic response to food safety incidents. Furthermore, the results provide insight into the distinct differences in views of the supply chain positions on the coordination decision-making process, which is helpful for managers to better understand in what phase(s) and why other positions might make different decisions.

The findings add value for the general public, as an effective logistic response contributes to consumer’s trust in food safety by creating more transparency in the decisions made during a food safety incident. As food sources are and will remain essential for human existence, the need to contribute to knowledge related to aspects of food safety is evident because it will be impossible to prevent all food safety incidents.

As the main contribution, this study provides a systematic and interdisciplinary understanding of the coordination decision-making process for the logistic response to food safety incidents while distinguishing the views of the supply chain positions.

The personality of an individual plays a vital role in the way an individual perceives organizational politics and justice in the workplace. However, there is meager research on how an individual's personality affects the perceptions of organizational politics and justice. This study endeavors to fill this gap by analyzing the mediating role of organizational politics perceptions on the relationship between Big Five personality dimensions and organizational justice by controlling various demographic variables. The study also proposes a benchmarking model that the policymakers can use to create positive organizational justice perceptions.

In this cross-sectional research, the data were collected through a multi-stage random sampling technique from 493 faculty members working in four public universities of Punjab, India. Out of 493 employees, 76.9% of the employees were assistant professors, 12.0% were associate professors and 11.2% were assistant professors. 51.5% of the employees were female, and 48.5% of the employees were male. To test the proposed hypothesized relationships, a structural equation modeling technique was used.

Results of the structural equation modeling showed that openness to experience, conscientiousness and extraversion have a negative relationship with perceptions of organizational politics. However, their relationship with perceptions of organizational justice is positive. Neuroticism has a positive relationship with perceptions of organizational politics, whereas it has a negative relationship with perceptions of organizational justice. Results also showed that high perceptions of organizational politics have a negative effect on employee's perceptions regarding organizational justice. The mediation analysis results showed that perceptions of organizational politics mediate the relationship between an individual's personality and perceptions of organizational justice.

There is a scant amount of research available that considers Big Five personality dimensions and organizational politics as the antecedents of organizational justice. Hence, the current study tries to fill this research gap by proposing a research model on antecedents and consequences of perceptions of organizational politics based on the cognitive-affective processing system (CAPS).

The purpose of this study is two-fold: classifying non-Muslim halal fashion buyers by applying quantitative techniques and identifying the persuading determinants of the non-Muslim women’ halal fashion buying behaviour (HFBB).

By adapting items from prior studies, a structured questionnaire was developed and distributed face-to-face to various Muslim fashion stores in Malaysia. After a one-month effort, 221 responses were obtained from non-Muslim consumers by using convenience sampling. Next, a clustering analysis was used to classify them from a contrasting perspective. Finally, regression and Andrew F. Hayes’s process procedures were applied to examine the three independent variables’ effect and the moderating variables.

The results revealed the characteristic behaviour of the non-Muslim women explicitly, which is related to their halal fashion purchasing decision. Based on the ANOVA results, there were different motives for buying halal fashion by non-Muslim women. Additionally, it was found that the most crucial determinants for non-Muslim’s HFBB are “cultural adaptation”, albeit, there is no substantial proof of a significant moderating effect of age and income on the consumers.

These discoveries are advantageous for halal fashion retailers and provide an appealing domain for further investigations in the context of the global halal study.

This study provided an idea for an untapped segment on the halal fashion sellers’ segmentation and positioning strategy. The study’s results suggested specific managerial and practical recommendation that the sellers can use to attract non-Muslim consumers.

This study was amongst the uncommon investigations within the halal fashion context that will enlighten the managers’ selling strategy on the most neglected market segment. The results of this study provided an empirical understanding of how to sell halal fashion to non-Muslim consumers.

With the rapid growth of wireless sensor networks (WSNs), they have become an integral and substantial part of people's life. As such WSN stands as an assuring outlook, but because of sensor's resource limitations and other prerequisites, optimal dual route discovery becomes an issue of concern. WSN along with central sink node is capable of handling wireless transmission, thus optimizing the network's lifetime by selecting the dual path. The major problem confronted in the application of security mechanisms in WSNs is resolving the issues amid reducing consumption of resources and increases security.

According to the proposed system, two metrics, namely, path length and packets delivery ratio are incorporated for identifying dual routes amid the source and destination. Thereafter by making use of the distance metric, the optimal dual route is chosen and data transmission is carried out amid the nodes. With the usage of the recommended routing protocol high packet delivery ratio is achieved with reduced routing overhead and low average end to end delay. It is clearly portrayed in the simulation output that the proposed on demand dual path routing protocol surpasses the prevailing routing protocol. Moreover, security is achieved make use of in accord the data compression reduces the size of the data. With the help of dual path, mathematical model of Finite Automata Theory is derived to transmit data from source to destination. Finite Automata Theory comprises Deterministic Finite Automata (DFA) that is being utilized for Dual Path Selection. In addition, data transition functions are defined for each input stage. In this proposed work, another mathematical model is 10; introduced to efficiently choose an alternate path between a receiver and transmitter for data transfer with qualified node as relay node using RR Algorithm. It also includes Dynamic Mathematical Model for Node Localization to improve the precision in location estimation using Node Localization Algorithm. As a result a simulator is built and various scenarios are elaborated for comparing the performance of the recommended dual path routing protocol with respect to the prevailing ones.

Reliability and fault-tolerance: The actual motive in utilizing the approach of multipath routing in sensor network was to offer path resilience in case of a node or link failures thus ascertaining reliable transmission of data. Usually in a fault tolerant domain, when the sensor node is unable to forward the data packets to the sink, alternative paths can be utilized for recovering its data packets during the failure of any link/node. Load balancing: Load balancing involves equalizing energy consumption of all the existing nodes, thereby degrading them together. Load balancing via clustering improves network scalability. The network's lifetime as well as reliability can be extended if varied energy level's nodes exist in sensor node. Quality of service (QoS): Improvement backing of quality of service with respect to the data delivery ratio, network throughput and end-to-end latency stands very significant in building multipath routing protocols for various network types. Reduced delay: There is a reduced delay in multipath routing since the backup routes are determined at the time of route discovery. Bandwidth aggregation: By dividing the data toward the same destination into multiple streams (by routing all to a separate path) can aggregate the effective bandwidth. The benefit being that, in case a node possesses many links with low bandwidth, it can acquire a bandwidth which is more compared to the individual link.

Few more new algorithms can be used to compare the QoS parameters.

Proposed mechanism with feedback ascertains improvised delivery ratio compared to the single path protocol since in case of link failure, the protocol has alternative route. In case there are 50 nodes in the network, the detection mechanism yields packet delivery of 95% and in case there are 100 nodes, the packet delivery is lowered to 89%. It is observed that the packet rate in the network is more for small node range. When the node count is 200, the packet ratio is low, which is lowered to 85%. With a node count of 400, the curve depicts the value of 87%. Hence, even with a decrease in value, it is superior than the existing protocols. The average end-to-end delay represents the transmission delay of the data packets that have been successfully delivered as depicted in Figure 6 and Table 3. The recommended system presents the queue as well as the propagation delay from the source to destination. The figure depicts that when compared to the single path protocol, the end-to-end delay can be reduced via route switching. End-to-end delay signifies the time acquired for the delay in the receival of the the retransmitted packet by each node. The comparison reveals that the delay was lower compared to the existing ones in the WSN. Proposed protocol aids in reducing consumption of energy in transmitter, receiver and various sensors. Comparative analysis of energy consumptions of the sensor in regard to the recommended system must exhibit reduced energy than the prevailing systems.

On demand dual path routing protocol. Hence it is verified that the on demand routing protocol comprises DFA algorithms determines dual path. Here mathematical model for routing between two nodes with relay node is derived using RR algorithm to determine alternate path and thus reduce energy consumption. Another dynamic mathematical model for node localization is derived using localization algorithm. For transmitting data with a secure and promising QoS in the WSNs, the routing optimization technique has been introduced. The simulation software environment follows the DFA. The simulation yields in improvised performance with respect to packet delivery ratio, throughput, average end-to-end delay and routing overhead. So, it is proved that the DFA possesses the capability of optimizing the routing algorithms which facilitates the multimedia applications over WSNs.

This paper presents a systematic review of 94 research articles in the domain of gender and firm innovation to map the area and infer future research avenues. The literature captured in this review includes seven theoretical, 16 qualitative and 71 quantitative studies from over 50 journals, examining the role of gender in firm innovation in over 100 countries.

This research utilises a reproducible systematic literature review process to identify prominent theoretical and empirical studies.

The findings suggest that the area is growing and presents interesting opportunities. However, it is observed that empirical investigations are primarily influenced by literature from the Anglo-Saxon areas and give little attention to contextual intricacies of emerging market countries. Among quantitative studies, three main themes of gender are addressed unevenly. They are Top Management Team (TMT) diversity level, R&D team diversity level and individual entrepreneur level studies. Six interesting research avenues are proposed as a major outcome of the review.

This review is one of the first of its kind to extensively review the literature of gender and firm-level innovation. The review consolidates and widens the understanding of the relationship between gender variables and firm innovation-related variables to advance the discussion in the domain by presenting research gaps and questions gleaned from the articles. To this end, the review presents six promising research avenues in the area of gender and firm innovation.

This paper analyses the COVID-19 situation in Nigeria, its effect on the economy and the structural causes that worsened the coronavirus (COVID-19) crisis.

This paper uses simple descriptive analysis to examine the COVID-19 situation in Nigeria.

The findings reveal that the economic downturn in Nigeria was triggered by a combination of declining oil price and spillovers from the COVID-19 outbreak, which not only led to a fall in the demand for oil products but also stopped economic activities from taking place when social distancing policies were enforced. The government responded to the crisis by providing financial assistance to businesses and a small number of households that were affected by the coronavirus (COVID-19) outbreak. The monetary authority adopted accommodative monetary policies and offered a targeted 3.5 trillion loan support to some sectors. These efforts should have prevented the economic crisis from occurring but it did not. Economic agents could not freely engage in economic activities for fear of contracting the COVID-19 disease that was spreading very fast at the time.

The implication of the study is that policymakers should pay attention to three areas of the economy for economic and structural reform. One, policymakers should introduce economic reforms to diversify the economy and reduce Nigeria's dependence on revenue from crude oil export. Two, policymakers in Nigeria should invest in healthcare infrastructure to improve the ability of the national health system to withstand the outbreak of contagious diseases. Three, there is also a need to build appropriate digital infrastructure to facilitate the transition from “face-to-face” business activities to a “digital or online” business activities, which can help to grow the digital economy. Also, policymakers should use legislation to create a robust social welfare safety net for all citizens particularly for unemployed citizens and poor households.

This is the first paper that looks at the economic implication of COVID-19 in a West African country.