‘Influencers’ are individuals with a significant online following. Companies leverage their popularity to promote their brand, product or service on social media like Instagram, TikTok, Twitter and Facebook. Social media has emerged as a powerful platform influencing consumer perception because of its role in building connections and sharing information. In this context, ‘perception’ refers to brand image, product trust and credibility, purchase intention, consumer engagement, perceived value, and skepticism. Influencers tie up with brands to create content that discusses its product features via sponsored posts, product reviews, tutorials, and ads.

Survey and interview methods are used to map consumers’ perception to understand how consumers view a brand and whether  they are more like to purchase its product after seeing its recommendations from social media influencers. We use a pre-existing scale to determine the effect. The selected industries are cosmetics, food, clothing and consumer electronics. The findings will help to give marketers insightful information about how to improve influencer marketing campaigns' authenticity and, consequently, strengthen positive consumer perceptions in the dynamic digital age.

Making chatbots more personalised to improve consumer loyalty

In a world where businesses are driven by abundance and competition, consumer loyalty has become an invaluable tool for survival. Not only does it ensure continuity in sales, but it also reduces cost of acquiring new customers via word of mouth. However consumer personalities are constantly evolving. Numerous personality models have emerged in recent years to navigate the complexity of personality traits, especially to understand their influence on consumer behaviour.

This study takes into account the 16 personality factors model to understand how chatbots influence consumer loyalty in modern businesses. Preliminary investigation shows that chatbots affect consumers’ purchase behaviour and overall experience with a brand. In this relationship, personality factors act as the mediating variable which invariably influences the outcome of their interaction with chatbots. Therefore personalising chatbots is expected to offer companies immense benefits by maximising consumer loyalty. A recommendation model for personalising chatbots is therefore proposed.

Tags: consumer loyalty, personality factors, literature review, systematic review, marketing, sem analysis, mediating variable, mediation analysis, spss amos, research methodology, survey analysis

Systems around the world have become complex and dynamic, meaning that they are made of many components and are changing continuously. The education system is complex as it is made up of multiple interconnected elements like industrial expectations, government regulations, technological innovation, and teaching methods. It is also dynamic because these factors are constantly changing, affecting the whole system and ultimately, learning outcomes.

But learning outcomes are facing a massive challenge in the form of high dropout rates, poor academic performance, and a widening gap between industrial requirements and skills available. This study proposes the concept of systems thinking as a solution to these problems by recommending a non-linear and holistic approach to addressing problems in the education system. It explores case studies of educational institutions that have applied systems thinking in the past and uses the findings to suggest models which can be implemented in order to improve learning outcomes for all higher education institutions.

MANET (Mobile Ad Hoc networks) are the wireless networks which work without any specific infrastructure. MANET routes make use of nodes without a central access point. These nodes witness constant movement and sometimes the information flows from multiple nodes, causing congestion in networks. Therefore, to control the congestion issue and reduce unpredictability from MANET networks, there is a need to build an efficient routing protocol.

Many routing protocols have been built and applied over decades. They were either proactive or reactive. However as the complexity of networks grew, hybrid networks started being developed. Today there is a vast array of hybrid routing protocols in MANETs. Academic research has compared proactive and reactive routing protocols to find out which one is most efficient for communication. However, there has been little attention given to hybrid routing protocols. Such a comparison is warranted to assess different routing protocols' limitations and identify a more advanced method of addressing mobile sensor network traffic issues. The protocols could be simulated using a Network simulator (NS3 or NS2).

The purpose of this module is to compare hybrid, proactive and reactive routing protocols in MANET. The performance metrics for comparison of the routing protocols will be throughput, packet delivery ratio, packet loss, delay, drop packet, and routing overhead. The module focuses on a selection of optimal protocol based on the criteria that there is a rise in throughput performance, routing overhead and packet delivery ratio; and a reduction in packet loss, drop packet and delay. The packet delivery ratio should be at least 85%. Routing protocol fulfilling the performance metrics requirement will tend to have the best performance and major role in the transmission of data effectively.