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In competitive environments, explicitly seeking institutional changes to adopt a new technology, rather than exploiting current resources, can harm more than help organizations’ efforts to achieve their performance goals. However, institutionally embedded organizations often respond to the introduction of industry disruptive technology in counterproductive ways. This paper aims to study the paradox of embedded agency in competitive environments and explore the diffusion of new occupations associated with data analytics.

This study uses the context of the Major League Baseball where the digital platform, PITCHf/x, implemented during 2006 and 2007 seasons facilitated the professional baseball clubs to create occupations for data analytics.

This study found that long-term low performance of organizations resulted in creating occupations for a new technology and deploying professionals to them and the public media’s negative tenor mediated the relationship between the signal of institutional inefficiency and such a boundary work in a competitive environment.

This research enriches our understanding of the early disperse of a new occupation in the times of the emergence of digital platform by exploring the temporal attributes of organizational performance and the role of public media as the antecedents to embedded agency.

With the widespread use of online communications, users are extremely vulnerable to a myriad of deception attempts. This study aims to extend the literature on deception in computer-mediated communication by investigating whether the manner in which popularity information (PI) is presented and media richness affects users’ judgments.

This study developed a randomized, within and 2 × 3 between-subject experimental design. This study analyzed the main effects of PI and media richness on the imitation magnitude of veracity judges and the effect of the interaction between PI and media richness on the imitation magnitude of veracity judges.

The manner in which PI is presented to people affects their tendency to imitate others. Media richness also has a main effect; text-only messages resulted in greater imitation magnitude than those viewed in full audiovisual format. The findings showed an interaction effect between PI and media richness.

The findings of this study contribute to the information systems literature by introducing the notion of herd behavior to judgments of truthfulness and deception. Also, the medium over which PI was presented significantly impacted the magnitude of imitation tendency: PI delivered through text-only medium led to a greater extent of imitation than when delivered in full audiovisual format. This suggests that media richness alters the degree of imitating others’ decisions such that the leaner the medium, the greater the expected extent of imitation.

This empirical study uses herd behavior model to explore the role of anchor investors in ensuring fundraising success and overfunding of crowdfunded ventures.

Qualitative comparative analysis (QCA) is applied to find the configurational patterns describing how anchor investors' information disclosure leads to successful financing and overfunding.

Even when the anchor investor's resume is not detailed or the anchor investor has little experience in entrepreneurial investment, success or overfunding can be achieved, provided the anchor investor is a corporation rather than an individual. For individual anchor investors, a detailed resume matters. Overfunding can be achieved even when an individual anchor investor makes a small relative investment, if this small relative investment is compensated for by a detailed resume. Experience in entrepreneurial investment is crucial when individual anchor investors have few previous investments. Regardless of the anchor investor's identity, investment in absolute terms is crucial for crowdfunding success when experience in entrepreneurial investment is low. Such experience must be extensive if the anchor investor's resume is not detailed.

Both entrepreneurs and crowdfunding platforms can benefit from the findings in relation to the design of campaigns that use anchor investors' informational cues to achieve success and overfunding.

The study examines the importance of anchor investors' information disclosure in digital crowdfunding environments, differentiating between individual and corporate anchor investors.

本實證研究使用羊群行為模型, 去探究錨定投資者在確保眾籌活動可達成功籌資以及過多籌資方面所扮演的角色。

研究人員以定性比較分析法、去找出描述錨定投資者的資訊公佈如何帶來成功融資和過多籌資的配置模式。

研究結果顯示、只要錨定投資者不是個人、而是一間公司, 則即使他們的履歷不詳盡, 又或他們對企業投資的經驗淺薄, 也無礙籌資或過多籌資的成功完成。如錨定投資者為個人, 則詳盡的履歷會影響甚鉅。即使個人錨定投資者相對而言參與少量的投資, 但若這少量的投資給他們詳盡的履歷所彌補的話, 則過多籌資仍可成功達到。若個別錨定投資者原有的投資量不多的話, 則企業投資的經驗至為重要。不管錨定投資者的身份是什麼, 若他們對企業投資所持的經驗淺薄, 則按絕對值計算的投資額對眾籌能否成功至為重要。若錨定投資者的履歷不詳盡, 則這種經驗必須是豐富廣泛的。

本研究區分了個人錨定投資者與公司錨定投資者兩者對眾籌的影響, 就此而研究在數碼的眾籌環境裡, 錨定投資者信息公佈的重要性。

研究結果可幫助企業家和群眾募資平台去設計可使用錨定投資者的資訊提示來達至成功眾籌和過多籌資的活動。

Financial professionals are increasingly important in the Indian financial system. Our study examines the association between the Big Five personality traits and Indian financial professionals' behavioral biases when making investment decisions.

After testing our questionnaire's reliability and validity, we used it to obtain the sample responses. We used multiple regression analysis and other statistical tools to identify the relationships between the Big Five personality traits and behavioral biases.

Our findings reveal a high level of extraversion and conscientiousness, a moderate level of agreeableness and openness and a low neuroticism level among financial professionals. The results show a significant association between neuroticism, extraversion, openness and all behavioral biases except anchoring bias. The neuroticism trait has a statistically significant relationship with all behavioral biases examined, whereas agreeableness and conscientiousness traits lack a significant association with behavioral biases. The openness trait is associated with many emotional biases and cognitive heuristics, while the extraversion trait has a significantly positive relationship with availability bias.

Future researchers could analyze primary (survey) and secondary investor data from brokerage houses. Using a larger sample could provide more generalizable findings. Researchers could also consider other aspects of investment decision-making using various asset classes. Understanding financial professionals' personality traits and behavioral biases could help them develop strategies to suit client needs.

This study provides the first comprehensive examination of the association between personality traits and behavioral biases of Indian financial professionals.

A separate study of the different behavioral biases does not allow for a full understanding of the complexity and stability of the heterogeneity of beliefs. Therefore, through a more global view of these anomalies, the authors wish to show that they can converge on a single concept, which is the heterogeneity of beliefs.

It is therefore essential to stress that the importance of this study is mainly reflected in the methodological approach used in the construction and analysis of the map and not only in the results achieved. This contribution states that structural analysis, as a means of building the cognitive map, can facilitate the task of investors and other decision-makers, in the identification and analysis of the heterogeneity of beliefs that can therefore guide investors' strategy in decision-making.

The authors have studied the behavior of the investor and its way of interpreting the information and the authors have emphasized the value of studying the concept of heterogeneity of beliefs in its complexity. So that part of the work seems to be relevant and crucial to filling, if you will, that void. In this sense, the authors have shown that behavioral abnormalities are multidimensional concepts: “self-deception”, “cognitive bias”, “emotional bias” and “social bias”.

In particular, this article will aim to achieve the objective of proposing a model for measuring the heterogeneity of beliefs. Thus, the authors want to show that the heterogeneity of beliefs can be measured directly through the different behavioral anomalies.

This paper describes how financial professionals' behavioral biases influence their financial forecast and decision-making process. Most of the earlier studies are focused on well-developed financial markets, and little is researched about financial professionals, such as institutional investors, portfolio managers, investment advisors, financial analysts, etc., in emerging markets.

An expert-validated questionnaire measure four prominent behavioral biases and Indian financial professionals' rational decision-making process. The final sample consists of 274 valid responses using the purposive sampling technique. IBM SPSS and AMOS structural equation modeling (SEM) software are used to build measurement and structural models, multivariate analysis including regression, factor analysis, etc.

The results provide empirical insights into the relationship between behavioral biases and the decision-making process. The results suggest that the structural path model closely fits the sample data. The presence of behavioral biases indicates that financial professionals' forecasting and decision-making is not always rational but bounded rational or irrational due to these factors. Furthermore, these biases (except overconfidence bias) have a markedly significant and positive relationship with irrational decision-making.

It is critical to eradicate these psychological errors, but awareness and attentiveness toward behavioral biases may help financial professionals to make informed decisions. Investors can improve their portfolio decisions and investments by recognizing their judgment errors and focusing on specific investment strategies to mitigate the impact of these biases. It is necessary to incorporate behavioral insights while developing training techniques for financial professionals. Rules of thumb, visual tools, financial coaching and implementing social-cultural elements in training programs enable financial professionals to develop simple, engaging, appealing and customized approaches for their clients.

This novel study is the first of this kind of research that examines the relationship between financial professionals' behavioral biases and rational decision-making process. This study significantly and remarkably provides insights into irrationality in financial professionals' decision-making.

Few studies have conceptualized how companies can build and nurture international dynamic marketing capabilities (IDMCs) by implementing growth hacking strategies. This paper conceptualizes growth hacking, a managerial-born process to embed a data-driven mind-set in marketing decision-making that combines big-data analysis and continuous learning, allowing companies to adapt their dynamic capabilities to the ever-shifting international competitive arenas.

Given the scarcity of studies on growth hacking, this paper conceptualizes this managerial-born concept through the double theoretical lenses of IDMCs and information technology (IT) literature.

The authors put forward research propositions concerning the four phases of growth hacking and the related capabilities and routines developed by companies to deal with international markets. Additional novel propositions are also developed based on the three critical dimensions of growth hacking: big data analytics, digital marketing and coding and automation.

Lack of prior conceptualization as well as the scant literature makes this study liable to some limitations. However, the propositions developed should encourage researchers to develop both empirical and theoretical studies on this managerial-born concept.

This study develops a detailed compendium for managers who want to implement growth hacking within their companies but have failed to identify the necessary capabilities and resources.

The study presents a theoretical approach and develops a set of propositions on a novel phenomenon, observed mainly in managerial practice. Hence, this study could stimulate researchers to deepen the phenomenon and empirically validate the propositions.

This paper aims to conduct a fuzzy discrete time cost quality risk in the ambiguous mode CO_{2 tradeoff problem} (FDTCQRP*TP) in a megaproject based on fuzzy ground.

A combinatorial evolutionary algorithm using Fuzzy Invasive Weed Optimization (FIWO) is used in the discrete form of the problem where the parameters are fully fuzzy multi-objective and provide a space incorporating all dimensions of the problem. Also, the fuzzy data and computations are used with the Chanas method selected for the computational analysis. Moreover, uncertainty is defined in FIWO. The presented FIWO simulation, its utility and superiority are tested on sample problems.

The reproduction, rearrangement and maintaining elite invasive weeds in FIWO can lead to a higher level of accuracy, convergence and strength for solving FDTCQRP*TP fuzzy rules and a risk ground in the ambiguous mode with the emphasis on the necessity of CO₂ pollution reduction. The results reveal the effectiveness of the algorithm and its flexibility in the megaproject managers' decision making, convergence and accuracy regarding CO₂ pollution reduction.

This paper offers a multi-objective fully fuzzy tradeoff in the ambiguous mode with the approach of CO₂ pollution reduction.

To construct effective compliance programmes, the phenomenon of non-compliance and variations in its abidance must be elucidated. The purpose of this paper is to discuss the social reality of ethical decision making, which examines the internalisation of moral norms and realities of social behaviour and, therefore, the general non-compliance with everyday laws (Tyler, 2006).

This paper makes use of several social theories, including social proof theory, traditional social theory and social control theory. Humans are social beings, and decision-making in ethics is strongly influenced by herding behaviour (Roy, 2021). The behaviour of others and normative ethical standards inform the compliance of behaviour to an undiminishable degree.

Although there is a host of factors to consider, the success of compliance can largely be attributed to people’s perception and reception of authority. The perception of authority and legitimacy plays a vital role in appreciating the complexity of rule following. Legitimacy, and its embodiment by persons in public roles, is a cornerstone of the subsequent discussion.

This paper uncovers the underlying motivations of non-compliance as well as the social psychology involved in the ethics of compliance. Cross-disciplinary connections are made between the private and public sector and practical compliance recommendations. The significant impact of integrity culture and value-based compliance emerges from the dissection of the social reality.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.