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It is only logical that a firm aims to make a profit after entering the market. However, some firms enter the market with the goal of market expansion and even burn money to pursue market share, which is counterintuitive in practice. To explore the theoretical foundations behind this rare phenomenon, this paper focuses on discussing the impact of the market expansion entry strategy on the entrant firm and the incumbent firm.

Using a game theory model of a supply chain with an incumbent and an entrant, this paper explores the mathematical conditions for the entrant to adopt either the traditional or the market expansion entry strategy and investigates the incumbent’s benefits and losses under different entry strategies.

The results show that when the market-expansion effect and the selling price ceiling are moderate, the entrant firm always adopts the market expansion entry strategy, and the incumbent firm obtains a free ride from the entrant firm and benefits from it. The entire industry profits and the industry consumer surplus are increased. In particular, we further investigate the cases in which the incumbent firm has a first-mover advantage or there is a troublesome cost, and the results confirm the aforementioned conclusions.

By considering market share as the entrant’s goal, this paper contributes to the dual-purpose literature. Moreover, based on the model’s mathematical results, this paper offers relevant management insights for the entrant and its stakeholders in the e-commerce platform.

Prior research has investigated the benefit of companies that engage in cause-related marketing initiatives. However, this prior research has not adequately examined cause-related marketing situations when brands raise awareness for a cause without contribution of tangible resources to the cause (i.e. awareness marketing); thus, the purpose of this paper is to introduce and test awareness marketing as a new type of cause-related marketing.

Through four experimental studies with different sample sources, the authors introduce and examine a new type of cause-related marketing (awareness marketing) as well as identify mediating explanatory mechanisms.

Awareness marketing produces similarly heightened purchase intentions to other types of cause-related marketing (e.g. financial donation) when compared to situations where cause-related marketing is not used. Awareness marketing can also lead to higher brand authenticity and brand originality perceptions in some situations when compared to cause-related marketing incorporating a financial donation component or when no cause-related marketing is used. Brand perceptions and consumers’ perceived self-brand connection mediate the relationship from cause-related marketing to purchase intentions.

This research is limited by conducting studies in only experimental conditions and in one culture. Theoretical implications are provided to the literature on brand authenticity and self-brand connection. In doing so, the authors explain why awareness marketing is evaluated differently than other types of cause-related marketing or marketing without any cause reference.

Marketers would benefit from using awareness marketing (i.e. raising awareness for a cause without direct contribution to the cause) as a lower investment alternative to traditional cause-related marketing efforts.

To the best of the authors’ knowledge, this is the first research to introduce awareness marketing as a new type of cause-related marketing and compare it to traditional types of cause-related marketing, thereby providing novel contributions as to how cause-related marketing can effectively increase purchase intentions without making a financial, product or other tangible contribution to a cause.

The purpose of this study is to examine Bitcoin's price behavior across market conditions, focusing on the influence of Bitcoin's historical prices, news sentiment and market indicators like oil prices, gold and the S&P index. The authors also assess the stability of Bitcoin-inclusive hedging portfolios under different market conditions, for example, bearish, bullish and moderate market states.

This study uses the Quantile Autoregressive Distributed Lag model to explore the effects of different factors on Bitcoin's prices across various market situations. This method allows for a detailed analysis of historical trends, investor expectations and external market influences on Bitcoin's price movements and systematic stability.

Key findings reveal historical prices and investor expectations significantly influence Bitcoin in all market scenarios, with news sentiment exhibiting substantial volatility. This study indicates that oil prices have minimal impacts on Bitcoin, whereas gold is a stabilizing asset in bear markets, with the S&P index influencing short-term fluctuations. At the same time, Bitcoin's volatility varies with market conditions, proving more efficient as a hedging tool in bear and stable markets than in bull ones.

This study highlights the intrinsic correlation between Bitcoin's prices, news sentiment and financial market indicators, enhancing understanding of Bitcoin's market dynamics. The authors demonstrate Bitcoin's weak direct correlation with commodities like oil, the stabilizing role of gold in crypto portfolios and the stock market's indirect effect on Bitcoin prices. By examining these factors' impacts across various market conditions, the findings offer strategies for investors to improve hedging and portfolio management in cryptocurrency markets.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

We respond to the existing gaps regarding the drivers and outcomes of customer experience quality in the context of bank marketing by examining the interrelationships between distinct dimensions of VTM service quality, customer experience quality and customer loyalty.

This research follows the Stimulus-Organism-Response theory to examine the antecedents and behavioural outcomes of customer experience quality during their journeys with video teller machine (VTM) services in the banking industry (also known as LiveBank or SmartBank). First, we conducted in-depth interviews with 34 bank customers to develop distinct measurement scales for customer experience quality and VTM service quality. A structural equation model linking six dimensions of VTM service quality, including tangibles, interaction quality, empathy, reliability, user’s friendliness and efficiency with the affective-sensory and intellectual values of customer experience quality and customer loyalty to VTM service is tested using data obtained from 405 individual customers.

The findings reveal that tangibles, interaction quality, reliability, user-friendliness and efficiency contribute to customer experience quality, which, in turn, drives customers’ intention to use VTM again. This research provides crucial theoretical background and practical implications to accelerate the penetration of VTM among bank customers and hence, foster financial inclusion among societies.

This paper presents the first research that empirically employs the value-based approach to measure customer experience quality in the banking service industry and examine its linkages to service quality and customer loyalty. Moreover, given the emergence of VTM, this is also among the pioneering studies which validate measurement scales for VTM service quality. This could be either reused or revisited for further research about VTM. Overall, our study contributes to the literature about customer retention in the banking service industry from not only the customers’ backwards-looking evaluations of service performance (i.e. service quality) but also their forward-looking evaluations (i.e. their own experience).

This study aims to analyze the development of digital market characteristics particularly focusing on how the strategic choices of platforms are not fully reflected in pricing. In addition, the implications for the development of theories of harm are investigated to explore the necessity of a relevant market definition in assessing infringement and evaluating the adequacy of Indonesian competition law.

This study is a legal analysis that uses statutory approaches, cases, comparative law and the development of theories of harm in digital mergers. The case approach is conducted by analyzing three cases decided by the Indonesia Business Competition Supervisory Commission. This approach provides insight into the response of Komisi Pengawas Persaingan Usaha concerning the merger and acquisition cases in the digital era as well as the provision of different analyses in conventional markets. However, competition can be potentially damaged in digital markets and a comparative law approach is taken by analyzing digital merger cases decided by authorities in other countries.

Results reveal that the digital market has created a “relevant market” that is challenging and blurred due to multi-sided network effects and consumer data usage characteristics. Platform-based enterprises’ prices fluctuate due to the digital market’s network effect and consumer data statistics. Smartphone prices depend on the number of apps and consumer data. Neoclassical theory focusing on product markets and location applied in Indonesia must be revised to establish a relevant digital economy market. To evaluate digital mergers, new harm theories are needed. The merger should also protect consumer data. Law Number 27 of 2022 on Personal Data Protection and Government Regulation on the Implementation of Electronic Systems and Transactions protects online consumers, a basic step in due diligence for digital mergers. The Indonesian Government should promptly strengthen the notion of “relevant markets” in the digital economy, which could lead to fair business competition violations like big data control. Notify partners or digital merger participants of the accessibility of sensitive data like transaction history and user location.

The development of digital market characteristics has implications for developing theories of harm in digital markets. Indonesian competition law needs to develop such theories of harm to analyze the potential for anticompetitive digital mergers in the digital economy era.

The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.

The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.

The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.

The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.

The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

This study aims to establish marketing practices which predict business performance of architecture firms within the Nigerian Construction Industry (NCI) to address the sustained poor business performance of firms, which affects allied professionals as many projects in the built environment depend on design proposals from architects.

Survey responses from 86 firms were used to model business performance measured as total revenue of the firms from 40 commonly deployed marketing practices in construction.

Two-thirds of the marketing practices most used by architectural firms were ineffective in predicting business performance. The model also explains up to half the variance in business performance (37.4–49.9%), supporting the view that marketing in the CI affects business performance. Researching client needs and competitors emerged as the only significant positive predictor of business performance (β = 0.827, p = 0.043). Using social media (β = −1.247, p = 0.004), regular participation in awards/competitions (β = −1.420, p = 0.013) and inclusion of political offers in bids (β = −1.050, p = 0.016) negatively predicted business performance.

Architecture and allied professional bodies in Nigeria need to rethink existing restrictions regarding marketing based on traditional code of ethics in light of present-day realities of digital and internet business environments. Principals and management of architecture firms require a paradigm shift in deploying the appropriate marketing practices, especially as it relates to research regarding changing client expectations and current competition within the NCI.

The study established marketing practices which model business performance and demonstrate their value in a framework for improving the financial sustainability of architecture firms within the NCI.

This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns.

This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model.

The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model.

Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis.

To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.