Examining the two-dimensional perceived marketplace influence and the role of financial incentives by SEM and ANN

In recent years, research on sustainable consumption has been particularly relevant, highlighting the importance of the collective over the individual to reduce pollution. This study focuses on the study of the perceived marketplace influence (PMI) concept in its organizational and consumer dimensions, together with the financial incentives that exist in the adoption of electric cars and their effect on green customer engagement. A sample of 382 potential buyers of electric vehicles was obtained. A new hybrid analytical approach was taken structural equation modelling and artificial neural network. The research found the most significant variables affecting purchase intention were financial incentives, followed by PMI Organization and finally PMI Consumer. The results of artificial neural network analysis confirmed all the findings of the structural equation modelling, although the importance of each PMI dimension is different for each technique used. The conclusions point to new business opportunities that can be exploited by companies selling this green technology.Funding for open access charge: Universidad de Granada / CBU

Biometric m-payment systems: A multi-analytical approach to determining use intention

Although mobile payment systems offer countless advantages, they do present certain drawbacks, mainly associated with security and privacy concerns. The inclusion of biometric authentication technologies seeks to minimise such drawbacks. The aim of this article is to examine the effect of key antecedents of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and perceived risk on the intention to use a mobile payment system featuring biometric identification. A new hybrid analytical approach is taken. A sample of more than 2500 smartphone users was obtained through an online panel-based survey. Two techniques were used: first, structural equation modelling (PLS-SEM) was conducted to determine which variables had a significant influence on the adoption of the mobile payment system, and second, an artificial neural network (ANN) model was used, taking a deep learning approach, to rank the relative influence of significant predictors of use intention obtained via PLS-SEM. The study found that the most significant variables affecting use intention were performance expectancy, effort expectancy, facilitating conditions, hedonic motivation and risk. In contrast, subjective norms, price value and habit were found to be weak predictors of use intention. The results of the ANN analysis confirmed almost all SEM findings but yielded a slightly different order of influence among the least significant predictors. A review of the extant scientific literature revealed a paucity of published studies dealing with the adoption and use of mobile payment systems featuring biometric identification. The conclusions and managerial implications point to new business opportunities that can be exploited by firms through the use of this technology

Economic horizons

The concept of supply chain management (SCM) has occupied serious research a! ention in recent years. This concept goes beyond intra-organizational boundaries to achieve a greater value of the entire supply chain network. The development of ICT, together with the Internet environment, has an impact on the management concept of traditional supply chains, allowing the integration of participants and the management of complex interfaces between organizations in the supply chain network. The e-business model connects the separate activities of the supply chain in an integrated, coordinated, fl exible, effi cient and responsive system. This paper analyzes the key aspects of e-SCM and diff erent supply chains architectures in an e-environment as the starting point for defi ning the generic architecture model of e-SCM

The Effectiveness and Students’ Perception of an Adaptive Mobile Learning System based on Personalized Content and Mobile Web

As the whole world is going mobile, application of mobile devices in education, also known as m-learning, is becoming one of the most popular areas of educational research. This paper presents the implementation and evaluation of the effectiveness and students’ attitudes toward an adaptive mobile learning system based on personalized content and mobile web. Personalization of learning materials is based on the Felder-Silverman learning style model and the features of the accessing mobile device were identified using the device library. The results of the study confirm students’ positive attitudes toward mobile learning and the developed adaptive m-learning system. They also prove the effectiveness of the system and m-learning as an additional educational tool in terms of increasing students’ knowledge and scores

MEASURING THE DATA MODEL QUALITY IN THE ESUPPLY CHAINS

The implementation of Internet technology in business has enabled the development of e-business supply chains with large-scale information integration among all partners.The development of information systems (IS) is based on the established business objectives whose achievement, among other things, directly depends on the quality of development and design of IS. In the process of analysis of the key elements of company operations in the supply chain, process model and corresponding data model are designed which should enable selection of appropriate information system architecture. The quality of the implemented information system, which supports e-supply chain, directly depends on the level of data model quality. One of the serious limitations of the data model is its complexity. With a large number of entities, data model is difficult to analyse, monitor and maintain. The problem gets bigger when looking at an integrated data model at the level of participating partners in the supply chain, where the data model usually consists of hundreds or even thousands of entities.The paper will analyse the key elements affecting the quality of data models and show their interactions and factors of significance. In addition, the paper presents various measures for assessing the quality of the data model on which it is possible to easily locate the problems and focus efforts in specific parts of a complex data model where it is not economically feasible to review every detail of the model

SUPPLY CHAIN INFORMATION INTEGRATION THROUGH SERVICE ORIENTED ARCHITECTURE

In recent years information integration became significant problem for both natural and legal persons in everyday operations. Huge amount of information are available, but insufficiently processed in order to have useful value. Choosing the right combination of tools and technologies for integration is prerequisite for requiring information from multiple heterogeneous sources and their qualitative and simple using after.In this paper, we have focused on information integration within companies which are parts of supply chain or network. This environment typically includes a various mix of sources, structured (such as relational or other databases), and unstructured (such as document repositories, spreadsheets, documents, web pages, emails and others). Effective information integration and sharing significantly enhances supply chain practices. Service oriented architecture (SOA) is an architectural style for building software applications that use services available in a network such as the web. The use of SOA to achieve inter-enterprise supply network information integration has many advantages

MULTIOBJECTIVE SUPPLIER SELECTION USING GENETIC ALGORITHM: A COMPARISON BETWEEN WEIGHTED SUM AND SPEA METHODS

Supplier selection is one of the basic and most important activities of purchasing management. This activity often includes solving of multiobjective optimization problems with different and usually conflicting objectives. Modern supplier selection techniques involve novel multiobjective optimization algorithms based on computational application. In this paper supplier selection using genetic algorithm is presented. The authors used two different methods: weighted sum method and SPEA method. Weighted sum method belongs to category of Decision before Search methods. SPEA method is a member of Search before Decision group of methods. As criteria for selection optimization variance of quality and total costs are used. Results show that described methodology can be applicable for the practical purposes. Finally, comparative analysis of two different methods, used in this research, is presented