Sales Control Management in Banking and the Usefulness of Emerging

This paper aims to study the usefulness of technological tools in the strategic control of sales management in the banking sector. It aims to reveal what components to include that reflects the characteristics of the bank and its environment while designing a sales management control to ensure effective feedback and control. Design/methodology/approach: 15 (Fifteen) out of 25 commercial banks in Romania were chosen which have 80% of the market share. A detailed survey was carried out with 36 sales managers and other senior managers. SPSS 26 was used to analyse the data. Pearson correlation and Spearman One were used to identify the correlation along with Mood's median test and the Mann-Whitney test. Statistical significance was checked by U-test. Findings: The research found that, customer-centric banks should highly control behaviour of their salespeople and use sales tools that encourage self-control. This research confirmed that any bank that apply complex CRM systems should use behaviour control tool. It was also identified that rather than using a generic tool they should use a tool customised on basis of their organisational characteristics. The research also identified that for the banks those are operating in an unstable environment where competition, risks and costs are higher should ideally use tools with behaviour and professional control. Originality/value: This is an original research and part of an assessed PhD study. No such study was done before in the context of the Romanian banking sector

Understanding Heterogeneous EO Datasets: A Framework for Semantic Representations

Earth observation (EO) has become a valuable source of comprehensive, reliable, and persistent information for a wide number of applications. However, dealing with the complexity of land cover is sometimes difficult, as the variety of EO sensors reflects in the multitude of details recorded in several types of image data. Their properties dictate the category and nature of the perceptible land structures. The data heterogeneity hampers proper understanding, preventing the definition of universal procedures for content exploitation. The main shortcomings are due to the different human and sensor perception on objects, as well as to the lack of coincidence between visual elements and similarities obtained by computation. In order to bridge these sensory and semantic gaps, the paper presents a compound framework for EO image information extraction. The proposed approach acts like a common ground between the user's understanding, who is visually shortsighted to the visible domain, and the machines numerical interpretation of a much wider information. A hierarchical data representation is considered. At first, basic elements are automatically computed. Then, users can enforce their judgement on the data processing results until semantic structures are revealed. This procedure completes a user-machine knowledge transfer. The interaction is formalized as a dialogue, where communication is determined by a set of parameters guiding the computational process at each level of representation. The purpose is to maintain the data-driven observable connected to the level of semantics and to human awareness. The proposed concept offers flexibility and interoperability to users, allowing them to generate those results that best fit their application scenario. The experiments performed on different satellite images demonstrate the ability to increase the performances in case of semantic annotation by adjusting a set of parameters to the particularities of the analyzed data

Marketing Consultancy for Launching a New Service

At the global level, developed Western economies experienced a migration from agriculture- and production--based economies to a service-based economy. Starting with 1990, world economy entered a new stage, the informational age, which brought an intense growth of the tertiary domain. This article presents the idea of developing a consultancy and training sector in Romania – a country in transition towards a developed economy – and analyses the preferences and the needs of the SME sector in this respect.consultancy, SMEs, strategic marketing, training.

Student induction experiences: Through the lens of gamification

Student induction serves as the first step of the learning journey, helping students understand the resources, facilities, and supporting infrastructures in the learning environment. A positive induction experience helps improve better learning efficacy and boost performance later on. However, students nowadays complain induction as boring, time-wasting and useless. Given the importance of induction, scholars have called for new research, finding a new way to deliver better-quality and more engaging induction. To respond to this call, the current research aims to investigate whether gamification offers better induction experiences to the students. Gamification is the use of game design techniques, game thinking, and game mechanics in a non-game context. Drawing on the student-centred learning theory, we propose that, through the game-play process, students shall feel less stressed but more confident in learning, leading to a more positive learning experience and outcome. Following the same logic, we hypothesise that gamification is positively correlated with the experiences of induction. That is, gamification-empowered induction brings better experiences to the new students. To examine the research hypothesis, we plan to recruit 200 students (research participants) through flyers and noticeboards during the university induction period in September 2023 (Ethics Approval Ref: ETH2223-0198). The recruitment is operated on a voluntary basis and participants can drop out at any time. Participant Information Letter, Consent Form, and other participant protection measures are arranged in line with the guidance of institutional ethics committee. The participants will be randomly assigned into two conditions. In Condition A, participants will receive a conventional induction through a regular teaching classroom. All documents and instructions are communicated through paper-based handouts. Participants will receive a campus map, explaining the location of buildings and respective services. The induction will be completed inside the classroom. In Condition B, participants will receive gamification-empowered induction. All documents and instructions are communicated through a gamification APP (to be installed in participants’ mobiles). To complete the induction, participants must visit the designated locations in the campus, exploring the services in person. To further understand participants' views and experiences of the induction, we plan to collect data through anonymous questionnaires surveys at the end of induction. Condition A will receive questions through web-based surveys, where Condition B will receive questions through APP-based surveys. Both conditions will receive the same survey questions, and Condition B will receive additional questions of APP-user experiences (A copy of the survey questions is enclosed in appendix). The data collected will be analysed and compared through SPSS and Excel software. Research findings will first and foremost examine whether gamification-empowered induction offers better induction experiences to the students. The answers will bring new insights to the gamification-induction literatures. Research findings will be important to the teaching practitioners and policy makers, particularly for those who wish to create better induction programmes through innovative strategies. Implications on induction design and delivery will be clarified. Research limitation and suggestions for future research will also be discussed

Azithromycin resistance in Escherichia coli and Salmonella from food-producing animals and meat in Europe.

OBJECTIVES To characterize the genetic basis of azithromycin resistance in Escherichia coli and Salmonella collected within the EU harmonized antimicrobial resistance (AMR) surveillance programme in 2014-18 and the Danish AMR surveillance programme in 2016-19. METHODS WGS data of 1007 E. coli [165 azithromycin resistant (MIC > 16 mg/L)] and 269 Salmonella [29 azithromycin resistant (MIC > 16 mg/L)] were screened for acquired macrolide resistance genes and mutations in rplDV, 23S rRNA and acrB genes using ResFinder v4.0, AMRFinder Plus and custom scripts. Genotype-phenotype concordance was determined for all isolates. Transferability of mef(C)-mph(G)-carrying plasmids was assessed by conjugation experiments. RESULTS mph(A), mph(B), mef(B), erm(B) and mef(C)-mph(G) were detected in E. coli and Salmonella, whereas erm(C), erm(42), ere(A) and mph(E)-msr(E) were detected in E. coli only. The presence of macrolide resistance genes, alone or in combination, was concordant with the azithromycin-resistant phenotype in 69% of isolates. Distinct mph(A) operon structures were observed in azithromycin-susceptible (n = 50) and -resistant (n = 136) isolates. mef(C)-mph(G) were detected in porcine and bovine E. coli and in porcine Salmonella enterica serovar Derby and Salmonella enterica 1,4, [5],12:i:-, flanked downstream by ISCR2 or TnAs1 and associated with IncIγ and IncFII plasmids. CONCLUSIONS Diverse azithromycin resistance genes were detected in E. coli and Salmonella from food-producing animals and meat in Europe. Azithromycin resistance genes mef(C)-mph(G) and erm(42) appear to be emerging primarily in porcine E. coli isolates. The identification of distinct mph(A) operon structures in susceptible and resistant isolates increases the predictive power of WGS-based methods for in silico detection of azithromycin resistance in Enterobacterales

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Demand for computer chips fuelled by AI could reshape global politics and security

As AI production emerges as a pivotal force in global dynamics, German Research Minister Berttina Stark-Watzinger has urged heightened research efforts at the Munich Security Conference 2024. It is the first time the security conference has a chapter related to technology as a global security problem spot. As Albert Einstein brilliantly said, “The faith of mankind hinges entirely upon man’s moral development”. The role shaped for international tech policy is increasingly important for security risks and redistribution of wealth at a global scale. Here we would like to explain why AI deserves our attention on elections, chips global race, as well as its implications on geopolitical vulnerabilities

AI may develop a huge carbon footprint, but it could also be a critical ally in the fight against climate change

There is uncertainty about whether AI will help address the climate issue or exacerbate it. Indeed, humans are responsible for climate change and have also created AI. On the one hand, Leading nations are engaged in a race for AI development; the environmental impact of AI development is a concern. -Data centers and transmission networks contribute over 1% of global energy use and 0.6% of global carbon emissions. -AI-related technology also increases water consumption. -A single ChatGPT query can generate 100 times more carbon than a regular Google search. On the other hand, Techno-optimists like Sims Witherspoon argue that advanced neural networks could improve our ability to forecast ecosystem shifts and accelerate research for a carbon-neutral energy infrastructure. This article will look at whether AI will help address the climate issue or simply exacerbate it examining both the potential positive and negative effects of AI in relation to Climate change