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Social media has become an indispensable part of modern politics. Its rise in the political arena has coincided with the decline in trust toward mainstream media. Today, more than half of the population gets their political news and information through social media platforms like Twitter and Facebook. Social media offers a great marketing opportunity to politicians as they allow them to bypass traditional media and communicate directly with voters, engage citizens during campaign and noncampaign periods, and create a brand image. As social media's influence in politics grows, so has the research devoted to political marketing on social media. It is against this backdrop that this chapter is written, which provides readers with an overview of the academic domain and the current state of literature. The chapter highlights the various research areas that have been explored in the literature and the implications of social media for political marketing strategy, along with the domain's current limitations and possible avenues of further research.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

Over the last four decades – the 1980s, 1990s, 2000s and 2010s – the share of manufacturing in gross domestic product (at current prices) in India has stagnated, whereas the share of services (construction excluded) has increased significantly. The relatively mediocre growth performance of the manufacturing sector as compared to the services sector in India has emerged as a matter of concern. There has been growing recognition in policymaking circles and academia that India needs accelerated growth in the manufacturing industry in the next few decades, so that India's economic growth is led by manufacturing rather than services. In this paper, a comparative analysis of the growth in total factor productivity (TFP) of formal and informal segments of 10 groups of Indian manufacturing industries is undertaken, which is calculated based on KLEMS data obtained from National Accounts Statistics (NAS), published annually by the Central Statistics Office (CSO) of India. The period covered for the analysis of TFP growth is 1980–1981 to 2019–2020, which is broken into four sub-periods. Year wise total factor productivity growth (TFPG) of different industries for the sample period shows a fluctuating growth rate, which includes both positive and negative trends. Decade wise growth and overall TFPG of the different industries also indicate an oscillating pattern, that is, a mix of positive and negative TFPG. The minimum overall TFPG is observed in the coke, refined petroleum products and nuclear fuel industries, and the maximum in electrical and optical equipment for the period 1981–2020.

This study aims to examine the relationship between the personality dimensions of consumers and celebrities; the effect of celebrity personality on attitude towards the celebrity; and the effect of attitude towards the celebrity on purchase intention.

Data were gathered from 400 respondents in the North of England to explore the connections between five consumer personality dimensions (agreeableness, extroversion, openness, conscientiousness and neuroticism) and nine celebrity personality dimensions (neuroticism, extroversion, openness, agreeableness, conscientiousness, sincerity, excitement, stylishness and positivity) and were analysed using structural equation modelling.

The findings suggested that some dimensions of consumer personality, i.e. conscientiousness, extroversion and openness, were significantly related to all the celebrity personality dimensions. Moreover, all the celebrity personality dimensions had a significant effect on consumers’ attitude towards the celebrity; however, only neuroticism, extroversion, openness, sincerity and positivity significantly affected purchase intention. Finally, attitude towards the celebrity had a significant effect on purchase intention.

The study introduces a celebrity personality scale and explores a topic that has not previously been researched.

The purpose of this study is to analyze the impacts of the COVID-19 pandemic on the performance of companies using a hybrid Multi-Criteria Decision-Making (MCDM) approach. Specifically, the study examines Türkiye’s Top 500 Industrial Enterprises to analyze their performance before and during the pandemic, and to capture their performance in determining investment and production strategy.

To achieve the study’s objectives, the Fuzzy Best-Worst Method (F-BWM) was used to obtain importance levels of performance indicators, decreasing the vagueness in experts’ decision-making preferences. The Measurement Alternatives and Ranking According to Compromise Solution (MARCOS) method was used to rank enterprises based on their performance.

The COVID-19 pandemic has clearly had a substantial impact on the performance of Türkiye’s top 500 industrial enterprises. While some companies suffered decreased sales, others reported that their revenues increased or remained constant during the outbreak. The results reveal that the pandemic caused a shift in the initial ranking outcomes for the first two enterprises.

The study’s limitations include the sample size and the time period under consideration, which may have an impact on the generalizability of the findings.

Decision-makers’ investment, employment and operational decisions were influenced by the impact of the COVID-19 pandemic. The results provide insights for decision-makers on how to achieve higher growth and performance under the pressure of the pandemic.

The study’s practical consequences help decision-makers understand how to attain higher growth and performance in the face of the epidemic.

The originality of this study lies in using a hybrid MCDM approach to examine the impact of the COVID-19 pandemic on company performance. A hybrid MCDM approach is proposed to help decision-makers make the best possible investment and implementation decisions.

The COVID-19 crisis has sped up digital transformation and technologies by several years. Customers have dramatically shifted to online channels, and businesses have quickly responded by offering additional canals for online shopping and payment. Customers have also been exhibiting greater preferences for contactless payments, and mobile banking has therefore become a norm in both developed and developing countries. This study aims to understand the antecedents of mobile banking actual usage in an early adoption stage setting (i.e. Morocco) through a comprehensive conceptual model combining the unified theory of acceptance and use of technology, the DeLone and McLean IS success model and additional constructs extracted from extent literature. The moderating effects of age, gender and education are also examined and analyzed using multigroup analysis.

Based on data collected from 616 Moroccan users, the authors empirically tested the proposed conceptual model using structural equation modeling.

First, consumer M-banking actual usage has a significant effect on customer satisfaction and attitudinal loyalty; at the same time, attitudinal loyalty was significantly influenced by customer satisfaction. Second, while M-banking actual usage was significantly influenced by effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, service quality, trust, attitude and perceived security, the results show no significant impact of system quality and information quality. Third, the relationship between M-banking actual usage and its antecedents was significantly moderated by age, gender and education.

The findings help bank practitioners to understand the importance of meeting customers’ needs and expectations as a prerequisite in enhancing actual usage, satisfaction and attitudinal loyalty. More importantly, the authors emphasize the need for demographically oriented strategies to target different demographic segments of customers.

The study bridges a gap in M-banking literature by offering a thorough understanding of consumers’ mobile banking use during the pandemic. The findings provide evidence of the applicability of the conceptual model proposed in this research. Furthermore, the reflection of the moderating effects of gender, age and education emphasizes the mobile banking usage disparities among dissimilar demographic segments.

In 2020, the logistics market in Malaysia was valued at USD 37.60 billion, and it is projected to grow to more than USD 55.0 billion by 2026 at a compound annual growth rate (CAGR) of more than 4%. However, more information is needed about the impact of green logistic practice determinants by the local SMEs on the market share. This study serves as a focal point by examining the factors involved by offering a conceptual framework of determinants and their potential outcomes. This study contributes by demonstrating a conceptual, theoretical framework derived from the synthesis of two theory such as the Resource-Based View theory and the Diffusion of Innovation Theory. At the same time, it offers a holistic approach with an in-depth understanding of the Technological and Organizational factors of SMEs. The relationship between the implementation of green practices and organizational performance is also explored.

Customer value co-creation behavior is promising but undertheorized. To bridge this gap, this study examines the viability of a social cognitive theory positing that customers' value co-creation behavior is shaped by their co-creation experience, self-efficacy, and engagement.

Using healthcare as a case, a stratified random sample comprising 600 patients from 40 hospitals across eight metropolitan cities in an emerging economy was acquired and analyzed using co-variance-based structural equation modeling (CB-SEM).

Customers' co-creation experience has a positive impact on their co-creation self-efficacy, co-creation engagement, and value co-creation behavior. While co-creation self-efficacy and engagement have no direct influence on value co-creation behavior, they do serve as mediators between co-creation experience and value co-creation behavior, suggesting that when customers are provided with a co-creation experience, it enhances their co-creation self-efficacy and engagement, ultimately fostering value co-creation behavior.

A theory of customer value co-creation behavior is established.

The purpose of this study was to determine the influence of environmental pH on production of biofilms and virulence genes expression in Pseudomonas aeruginosa.

Among 303 clinical and environmental samples 109 (61 + 48) isolates were identified as clinical and environmental P. aeruginosa isolates, respectively. Clinical samples were obtained from patients in the Al-Yarmouk hospital in Baghdad city, Iraq. Waste water from Al-Yarmouk hospital was used from site before treatment unit to collect environmental samples. The ability of producing biofilm at various pH levels was examined by microtiter plate and the prevalence of Alg D, Psl A and Pel A was determined by quantitative real time-polymerase chain reaction (qRT-PCR).

This study showed that the ability of clinical and environmental isolates to biofilm development was observed in 86.9% and 85.42% of clinical and environmental isolates, respectively. As well as, the environmental P. aeruginosa isolates showed the highest biofilm production at pH 7. Clinical isolates showed the highest genes expression of Alg D, Psl A and Pel A as compared to environmental isolates with pH change. In general, both clinical and environmental isolates formed biofilm and carried AlgD, PslA and PelA genes. Also, alkaline pH was favored for biofilm production.

There are very few studies done to find out the influence of environmental pH on production of biofilms and virulence genes expression in Pseudomonas aeruginosa. This study is unique as it has highlighted the influence of environmental pH on the ability of clinical and environmental isolates to biofilm development and genes expression.

To improve the loan repayment performance (LRP) of microfinance banks (MFBs) in Pakistan, this study aims to look at the direct impact of multiple borrowing (MB) on LRP and client-business performance (CBP), as well as the direct impact of CBP on LRP. The moderating function of pandemic factors in the relationship between MB and CBP, as well as the mediating effect of CBP in the association between MB and LRP, was also investigated in this study.

A questionnaire was used to obtain data from 531 lower-level workers of microfinance institutions (MFIs) for the study. The respondents were chosen using stratified sampling, which divided the target population into four influential groups: lending officers in agriculture, lending officers in businesses, lending officers in gold loans and lending officers in salary loans. In this study, a two-stage structural equation modeling approach was used, including a measurement model (outer model) and a structural model (inner model). The validity and reliability of the questionnaire were investigated using the measurement model (outer model), whereas PLS-SEM bootstrapping was performed to test the hypothesis and find the relationship among different underpinning constructs by using the structural model (inner model).

The outcomes of this study demonstrate that MB has a direct impact on CBP, and that CBP has a direct impact on LRP. MB, on the contrary, had no direct and significant impact on LRP in this study. The idea that CBP mediates the relationship between MB and LRP, as well as the moderating effect of pandemic factors on the relationship between MB and CBP, is supported by this research.

Until now, the influence of MB on LRP via the mediating role of CBP and the moderating role of a pandemic factor in the setting of Pakistani MFBs has received little attention. During the COVID-19 pandemic, this research also aids MFBs in better understanding MB and its impact on LRP. Furthermore, based on the findings of this study, Pakistani MFIs can enhance their LRP by implementing new lending regulations, particularly with reference to MB and the COVID-19 pandemic.