scholarly journals Artificial intelligence reveals environmental constraints on colour diversity in insects

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Shipher Wu ◽  
Chun-Min Chang ◽  
Guan-Shuo Mai ◽  
Dustin R. Rubenstein ◽  
Chen-Ming Yang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Structural Equation ◽  
Feature Vector ◽  
High Elevation ◽  
Image Feature ◽  
Equation Modeling ◽  
Ecological Gradient ◽  
Biogeographic Pattern ◽  
Underlying Mechanisms

Abstract Explaining colour variation among animals at broad geographic scales remains challenging. Here we demonstrate how deep learning—a form of artificial intelligence—can reveal subtle but robust patterns of colour feature variation along an ecological gradient, as well as help identify the underlying mechanisms generating this biogeographic pattern. Using over 20,000 images with precise GPS locality information belonging to nearly 2,000 moth species from Taiwan, our deep learning model generates a 2048-dimension feature vector that accurately predicts each species’ mean elevation based on colour and shape features. Using this multidimensional feature vector, we find that within-assemblage image feature variation is smaller in high elevation assemblages. Structural equation modeling suggests that this reduced image feature diversity is likely the result of colder environments selecting for darker colouration, which limits the colour diversity of assemblages at high elevations. Ultimately, with the help of deep learning, we will be able to explore the endless forms of natural morphological variation at unpreceded depths.

Proactive Personality as a Double-Edged Sword: The Mediating Role of Work–Family Conflict on Employee Outcomes

2020 ◽  
pp. 089484531989998
Author(s):  
Thomas G. Altura ◽  
Alaka N. Rao ◽  
Meghna Virick
Keyword(s):  
Structural Equation ◽  
Turnover Intentions ◽  
Organizational Outcomes ◽  
Work And Family ◽  
Proactive Personality ◽  
Equation Modeling ◽  
Work Family Conflict ◽  
Work Related ◽  
Work Family ◽  
Underlying Mechanisms

In this study, we examine the effects of proactive personality on employees’ perceptions of conflict in the work and family domains and work-related outcomes. We test our hypotheses using survey data collected from 1,150 employees. Our results indicate that proactive personality is associated with increased work-interference-in-family (WIF) but decreased family-interference-in-work (FIW). These opposing, domain-specific effects have masked hitherto hidden relationships that we highlight in this study. Using structural equation modeling, we demonstrate that WIF and FIW function as simultaneous mediating variables between proactive personality and turnover intentions as well as between proactive personality and job performance. Through an investigation of underlying mechanisms, this study advances a more nuanced portrait of how individual differences shape individual and organizational outcomes.

scholarly journals Inner-Self vs. Outer-Self and Socially Responsible Product Consumption

2020 ◽  
Vol 12 (22) ◽  
pp. 9362
Author(s):  
Yeujun Yoon ◽  
Kevin Chastagner ◽  
Jaewoo Joo
Keyword(s):  
Purchase Intention ◽  
Structural Equation ◽  
Online Survey ◽  
Brand Image ◽  
Equation Modeling ◽  
Chinese Consumers ◽  
Product Consumption ◽  
Inner Self ◽  
Status Seeking ◽  
Underlying Mechanisms

This paper investigates how two fundamental consumer characteristics, self-esteem (inner-self) and status seeking (outer-self), influence consumers’ purchasing behaviors of CSR (Corporate Social Responsibility) products via two mediating effects: brand image and self-enhancement. In particular, we analyze these effects in two different CSR domains: environmental and social. By doing so, we are able to verify the underlying mechanisms of how different types of consumers respond to various CSR promotions. We propose a distinctive CSR consumption model incorporating both inner-self and outer-self components. We collected data from two countries, the US and China, using two commonly used online survey platforms: Amazon M-Turk and Loop Information Technology. Using structural equation modeling, our analysis in the environmental domain revealed that both inner-self and outer-self components play a significant role in consumers’ desire to purchase CSR products. Additionally, this process is mediated by the brand image of the firm and the tendency to enhance self-value. Interestingly, we found that in the social domain, self-enhancement mediated consumer characteristics and purchasing behavior of CSR product, whereas brand image did not. This indicates that environmental CSR activities increase brand value and its impact on purchase intention, while social CSR activities do not. Additionally, we found similar patterns for both US and Chinese consumers.

scholarly journals Implementasi Technology Acceptance Model terhadap Adopsi Teknologi Artificial Intelligence pada Startup Digital

2021 ◽  
Vol 9 (2) ◽  
pp. 173-181
Author(s):  
Margo Purnomo ◽  
Erna Maulina ◽  
Aulia Rizki Wicaksono ◽  
Muhamad Rizal
Keyword(s):  
Artificial Intelligence ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Structural Equation ◽  
Simple Random Sampling ◽  
Sem Analysis ◽  
Perceived Usefulness ◽  
Equation Modeling ◽  
Artificial Intelligence Technology ◽  
Acceptance Model

The purpose of this study is to describe and analyze the implementation of the technology acceptance model on the adoption of artificial intelligence technology in digital startups. By using simple random sampling, a sample of 109 out of 143 populations, which are startups in Greater Jakarta, was obtained. Based on an analysis using Structural Equation Modeling (SEM) analysis with the SmartPLS tool. The results show that the adoption of artificial intelligence in digital startups is influenced by attitudes, perceptions of convenience and perceptions of usefulness. Likewise, perceived convenience and perceived usefulness are significantly affected by external pressures.

Variation matters

2019 ◽  
Vol 29 (6) ◽  
pp. 1469-1484 ◽  
Author(s):  
Guanxiong Huang
Keyword(s):  
Social Media ◽  
Structural Equation ◽  
Design Methodology ◽  
Brand Attitude ◽  
Equation Modeling ◽  
Purchase Intent ◽  
Content Type ◽  
Content Creation ◽  
Content Strategy ◽  
Underlying Mechanisms

Purpose In-feed native ads have become a major social media advertising format. The purpose of this paper is to investigate strategies for leveraging native advertising in terms of content creation and platform selection on social media, proposing that variations in content and platform reduce the intrusiveness of native ads, thereby resulting in enhanced brand attitude and purchase intent. Design/methodology/approach Two experiments were conducted with online samples, employing a 2 (content strategy: repeated ads vs varied ads) × 2 (platform strategy: single platform vs multiple platforms) between-subject factorial design. ANCOVA and structural equation modeling were used to test the hypotheses. Findings When repeated ads were used, the use of multiple platforms reduced ad intrusiveness, resulting in more favorable brand attitude and greater purchase intent as opposed to the use of a single platform. In contrast, when varied ads were used, there were no significant differences in the outcome variables between a single platform and multiple platforms. The results were largely consistent across the two experiments. Originality/value This study contributes to theory advancement by unpacking the underlying mechanisms of processing native advertising and shedding light on which content and platform strategies are the most effective on social media.

scholarly journals Employees’ Trust in Artificial Intelligence in Companies: The Case of Energy and Chemical Industries in Poland

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1942
Author(s):  
Justyna Łapińska ◽  
Iwona Escher ◽  
Joanna Górka ◽  
Agata Sudolska ◽  
Paweł Brzustewicz
Keyword(s):  
Artificial Intelligence ◽  
Perspective Taking ◽  
Latent Variables ◽  
Structural Equation ◽  
Organizational Trust ◽  
Equation Modeling ◽  
Multidisciplinary Research ◽  
General Trust ◽  
Research Gap ◽  
Trust In Technology

The use of artificial intelligence (AI) in companies is advancing rapidly. Consequently, multidisciplinary research on AI in business has developed dramatically during the last decade, moving from the focus on technological objectives towards an interest in human users’ perspective. In this article, we investigate the notion of employees’ trust in AI at the workplace (in the company), following a human-centered approach that considers AI integration in business from the employees’ perspective, taking into account the elements that facilitate human trust in AI. While employees’ trust in AI at the workplace seems critical, so far, few studies have systematically investigated its determinants. Therefore, this study is an attempt to fill the existing research gap. The research objective of the article is to examine links between employees’ trust in AI in the company and three other latent variables (general trust in technology, intra-organizational trust, and individual competence trust). A quantitative study conducted on a sample of 428 employees from companies of the energy and chemical industries in Poland allowed the hypotheses to be verified. The hypotheses were tested using structural equation modeling (SEM). The results indicate the existence of a positive relationship between general trust in technology and employees’ trust in AI in the company as well as between intra-organizational trust and employees’ trust in AI in the company in the surveyed firms.

Using hybrid SEM – artificial intelligence

2019 ◽  
Vol 49 (1) ◽  
pp. 67-86 ◽  
Author(s):  
A. Mohammed Abubakar
Keyword(s):  
Artificial Intelligence ◽  
Job Satisfaction ◽  
Generational Differences ◽  
Structural Equation ◽  
Service Industry ◽  
Negative Impact ◽  
Psychological State ◽  
Equation Modeling ◽  
Conservation Of Resources ◽  
Content Type

Purpose Boreout is a psychological state of intense boredom and apathy. Characterized by the absence of mental stimuli (i.e. menial tasks) required to keep employees conscious about their environment, and this incessant decline in mental stimuli may turn employees into “professional zombies.” The diversity in work needs and preferences across generations has become a key organizational factor, generational differences have been studied in Western countries, not much information is available about generational cohorts and satisfaction (i.e. career, life and job satisfaction) in developing countries. The purpose of this paper is to provide more insights on these phenomena. Design/methodology/approach Drawing upon conservation of resources theory, this paper examines the potential effects of boreout on important job outcomes (i.e. career, life and job satisfaction) conditioned by generation (Gen-Xers and Gen-Yers) in the service industry. Data analyses with Artificial Intelligence technique (i.e. artificial neural network) and structural equation modeling were conducted with data collated from Nigerian service employees. Findings Results revealed that boreout has a negative impact on career, life and job satisfaction. The hypothesized relationships were significantly moderated by generation cohorts as Gen-Xers and Gen-Yers were found to be distinct cohorts. Originality/value This paper advocates that non-western organizations should avoid utmost service standardization and rigid stylization of work processes and procedures.

Dual mechanisms of reinforcement reward and habit in driving smartphone addiction

2019 ◽  
Vol 29 (6) ◽  
pp. 1551-1570 ◽  
Author(s):  
Chongyang Chen ◽  
Kem Z.K. Zhang ◽  
Xiang Gong ◽  
Matthew Lee
Keyword(s):  
Structural Equation ◽  
Design Methodology ◽  
Online Survey ◽  
Value Added ◽  
Equation Modeling ◽  
Smartphone Addiction ◽  
Formation Mechanisms ◽  
Content Type ◽  
Underlying Mechanisms

Purpose In the limited existing research on smartphone addiction, reinforcement reward and automatic habit have been identified as two distinct underlying mechanisms. The purpose of this paper is to better understand the interaction between these two mechanisms, as well as role of smartphone features in activating different formation mechanisms. Design/methodology/approach An online survey with 379 samples was conducted to empirically validate the model. The data were analyzed by structural equation modeling approach. Findings The automatic habit strengthens the driving effects of reinforcement rewards (i.e. perceived enjoyment and mood regulation) on smartphone addiction. Smartphone features activate distinct underlying mechanisms. Value-added function facilitates the perception of reinforcement rewards, while convenience promotes the habit formation. Originality/value This study is one of the first ones that assess the interplay between the two formation mechanisms in the extant literature on smartphone addiction. This study also reveals the dangers of smartphone features which have always been regarded as advantages. The findings contribute to the current understandings of smartphone addiction.

scholarly journals Does Artificial Intelligence (AI) Enabled Recruitment Improve Employer Branding?

2022 ◽  
Vol 17 (2) ◽  
pp. 45
Author(s):  
Giulia Baratelli ◽  
Elanor Colleoni
Keyword(s):  
Artificial Intelligence ◽  
Structural Equation Modeling ◽  
Random Sample ◽  
Structural Equation ◽  
Positive Relationship ◽  
Equation Modeling ◽  
Employer Branding ◽  
Recruitment Process ◽  
Talent Acquisition ◽  
Employer Attractiveness

Extant research over the last decades has stressed how artificial intelligence (AI) can be used to boost the recruitment process and to attract the best talents. Although AI is increasingly used for talent acquisition, with 36% of hiring processes expected to have a pre-screen through AI (Oracle, 2019) in the next two years, we have limited knowledge of how AI shapes talents’ perceptions about the organisation to which they are applying to. The goal of this research is to investigate if and how the usage of AI in the recruitment process improves employer attractiveness and employer branding in the eyes of the applicants. To investigate this issue a survey has been conducted on a random sample of individuals composed of 50% females and 50% males. To examine the survey’s result a structural equation modeling (SEM) has been applied. Results showed a positive relationship between EB and AI and more in particular that AI-enabled tools are perceived in a positive way by potential candidates. Thus, according to this study, AI is significantly related to Employer branding and therefore it contributes to improving talent attraction.

scholarly journals The impact of artificial intelligence analytics in enhancing digital marketing: the role of open big data and AI analytics competencies

2021 ◽  
Author(s):  
Tahereh Saheb ◽  
Bahareh Amini
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Structural Equation ◽  
Strategic Decision ◽  
Digital Marketing ◽  
Equation Modeling ◽  
Strategic Decision Making ◽  
Knowledge Based ◽  
The Impact

Abstract Purpose: The goal of digital marketing is to enhance strategic decision making to discover competitive and consumer behavior, and to provide early warnings of risks and opportunities. In the age of digitalization, digital marketing may be influenced with the use of cognitive technologies and also open sources of big data. The aim of this paper is to understand the impact of artificial intelligence analytics on digital marketing and the role of open big data and AI analytics competencies in this relationship.Design: A structural equation modeling of the 227 questionnaire was carried out. Findings: Our analysis shows that open big data has a significant impact on digital marketing, and AI analytical competencies moderate the impact of open big data on digital marketing. Value: Theoretically, this study extends the literature on knowledge-based view and AI analytics in digital marketing. Practically, this study broadens understandings regarding the perquisites of digital marketing through the use of cognitive technologies and open big data

The differentiated role of organizational ambidexterity and organizational innovation in the subsidiary reverse knowledge transfer process

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Felipe Mendes Borini ◽  
Leandro Lima Santos ◽  
Muhammad Mustafa Raziq ◽  
Rafael Morais Pereira ◽  
Aldo José Brunhara
Keyword(s):  
Knowledge Creation ◽  
Structural Equation ◽  
Resource Dependence ◽  
Organizational Innovation ◽  
Equation Modeling ◽  
Organizational Ambidexterity ◽  
Content Type ◽  
Knowledge Transfers ◽  
Underlying Mechanisms ◽  
Resource Dependence Perspective

Purpose This paper underscores how organizational ambidexterity and organizational innovation play differentiated roles in the subsidiary reverse knowledge transfers (RKT). The authors argue that both organizational ambidexterity and organizational innovation play a positive but differentiated role in the RKT process in that the former positively influences subsidiary knowledge creation, whereas the latter positively influences subsidiary knowledge transfers. Design/methodology/approach Data were collected from 289 foreign subsidiaries operating in Brazil. Hypotheses were developed and tested by applying partial least squares structural equation modeling. Findings The results supported the hypotheses and showed that organizational ambidexterity promotes knowledge creation, and that organizational innovation facilitates knowledge transfers. Research limitations/implications The paper offers implications with regard to drivers of subsidiary investments and actions of subsidiary managers vis-à-vis the subsidiary objectives of knowledge creation and/or transfers. Originality/value Showing the different roles of organizational ambidexterity and organizational innovation, this paper reveals some underlying mechanisms of the RKT process and contributes by explaining the competitive heterogeneity of subsidiaries, with impacts on subsidiary management’s evolutionary and resource dependence perspective.

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