Guest editorial: Impact of artificial Guest editorial

intelligence on business strategy

in emerging markets: a conceptual
framework and future 917
research directions
1. Introduction
With the rapid advancement of artificial intelligence (AI) technologies, businesses in
emerging markets are embracing AI applications to enhance their productivity and
footprints. Recent developments in machine and deep learning have revolutionized cognitive
computing and natural language processing, laying the foundations for proliferating AI
business applications in nations like China (Bughin et al., 2017). AI is poised to reshape
emerging markets; for example, finance, labor, human resource management, marketing,
advertising, business strategy, supply chain management, services, retail and information
systems. Consequently, savvy entrepreneurs are pondering the use of facial, image and
speech recognition applications to mitigate costs and barriers, enhancing their productivity.
In emerging markets, AI provides a technological solution to the economic and social
challenges faced by governments, firms and people at the bottom of the economic pyramid.
Integrating data from multiple sources (e.g. websites, social media and traditional channels)
can help firms build data management platforms, develop sound business strategies, lower
barriers to doing business, create innovative business models and spur economic development
(Arora and Rahman, 2017). Firms in developing countries may use innovative AI-based
solutions to enhance autonomous goods and service delivery, implement production
automation and develop mobile AI apps for services and credit access (Strusani and
Houngbonon, 2019). AI-based technologies can create opportunities and expand markets by
enhancing productivity, business process automation, financial solutions and government
services. Powered by AI, emerging markets’ public and private sectors can find leapfrogging
solutions and work together to reduce poverty and inequality while boosting economic
mobility and prosperity (Andrews et al., 2019; Xie et al., 2017; Zhou et al., 2019).
New adoption, utilization, integration and implementation challenges have arisen in
emerging markets as businesses embrace AI solutions. Conceptual studies have
addressed the challenges of AI in services (Huang and Rust, 2017, 2018, 2020; Li et al.,
2021a, b; Prentice et al., 2020; Robinson et al., 2020; Van Doorn et al., 2017; Wirtz et al.,
2018), personalization (Hermann, 2021; Kumar et al., 2019), advertising (Kietzmann
et al., 2018; Lee and Cho, 2020), sales management (Paschen et al., 2020; Singh et al.,
2019), industrial marketing (Li et al., 2021a, b; Mart
ınez-L
opez and Casillas, 2013),
automation in business logistics systems (Klumpp, 2018), market research (Wirth,
2018), smart warehousing readiness (Mahroof, 2019), AI platforms (Dawar and Bendle,
2018), tourism management (Cain et al., 2019; Stalidis et al., 2015), medical care (Ahmed
et al., 2020), marketing strategy (Huang and Rust, 2021) and ethics (Davenport et al.,
International Journal of Emerging
2020; Fullerton et al., 2017; Nunan and Di Domenico, 2017). Nonetheless, academic Markets
studies about business-related AI in emerging markets remain scant. Institutional Vol. 17 No. 4, 2022
pp. 917-929
environments in developing countries differ vastly from those in developed countries, © Emerald Publishing Limited
1746-8809
creating novel obstacles and legitimacy issues for AI-related business applications DOI 10.1108/IJOEM-04-2022-995
IJOEM (Yang et al., 2012). Hence, more theoretical and empirical studies to tackle the
17,4 challenges of AI in emerging markets are needed.

2. A conceptual model of AI-driven business strategy in emerging markets

Following the logic grounding the conceptual frameworks in Yang and Su (2013, 2014), we
created a model that depicts AI-driven business strategy development in emerging markets.
918 As shown in Figure 1, our model highlights the critical role of institutional environments in
shaping AI-driven business strategies and their impacts on the efficiency and legitimacy of
AI-driven business.
We posit that emerging markets’ cultural, technological, social, economic and religious
forces shape AI-related institutions (Yang and Su, 2013; Zhou et al., 2015). Businesses concerned
about the legal, ethical and social issues created by AI applications must consider these forces
to understand AI’s effect on the welfare of workers and consumers at the economic pyramid’s
bottom (Xie et al., 2017). For example, all businesses, especially MNCs, should attend to
economic inequality and the negative externalities of jobs returning from emerging markets to
mature markets due to AI-induced productivity gains. We contend that businesses should
ponder whether opaque AI applications will erode human self-determination (Zhou et al., 2012).
Regarding institutional influence on AI-driven business strategy, the three institutional
pillars posited by Scott (2008) – regulative, normative and cultural-cognitive – provide a
useful framework. Regulative institutions are the laws and regulations set by governments.
Central and local governments are vital to delineating legal foundations, establishing
industrial policies, providing capital and creating a roadmap to harness AI-related business
opportunities in emerging markets. Normative institutions establish industrial associations’
norms and standards for data access, data privacy, security and public trust. Cultural-
cognitive institutions refer to AI-related business behavior; for example, businesspeople and
consumers may distrust AI-related technologies and AI-made decisions.
Scott’s institutions and stakeholders, such as governments, communities, non-
government organizations (NGOs) and investors, are mutually influential. These dynamic
influences can inform managers’ interpretations and evaluations of Scott’s institutions on AI-
based business strategies. Specifically, do institutions impose constraints on or function as

Drivers of AI-related Institutions in Emerging Markets

(e.g., Culture, Society, Economy, Technology, Philosophy, Religion)

Institutional Environments in Emerging Markets

Cultural– Stakeholders in the Market

Regulative Normative cognitive (e.g., government, NGO, community, consumer,
Institutions Institutions Institutions shareholder)

Institutional Logics

Legitimacy Efficiency

Figure 1.
Conceptual model for
AI-Driven business AI Business Strategy in Emerging Markets
strategy in emerging
markets Adoption Utilization Integration Implementation
facilitators for gaining a competitive business advantage? Managers and consumers often Guest editorial
question the efficiency and legitimacy of intelligent agents. AI applications in business
enhance efficiency, convenience and cost-effectiveness while raising concerns such as
algorithmic social and data bias, data privacy and data protection. Institutional logics often
pressure businesses into finding creative solutions. Hence, a novel governance mechanism is
desirable to jointly address legitimacy and efficiency issues (Yang et al., 2012). Managers can
develop creative adoption, utilization, integration and implementation strategies in emerging
markets; for example, the contingent role of chatbot sales–service ambidexterity may resolve 919
the personalization–privacy paradox (Fan et al., 2022).

3. Future research directions

A goal of this thematic issue is to encourage new AI-related theories and conceptual
frameworks, focusing on emerging markets. A scientifically sound mindset should prompt a
reassessment of traditional theoretical assumptions. For example, the fundamental concept
of microeconomic theory – rationality – should be redefined in an AI context to help make and
augment rational decision-making (Parkes and Wellman, 2015). Machina economicus reflects
AI’s profound effects on economic reasoning. Similarly, new conceptual frameworks for AI in
business should suggest fruitful research streams (Hartmann et al., 2018; Kumar et al., 2016;
Yadav and Pavlou, 2014). Our posited model intimates several promising theoretical and
methodological paths to enhance understanding of AI-driven business strategy in emerging
markets.
Due to institutional distances, AI-related business strategies in emerging markets and
developed countries differ. Hence, researchers should question AI-related institutions’
cultural, socioeconomic and technological forces in emerging nations. For example, how will
AI change the traditional export-led path toward economic growth? Similarly, how should
sustainability-focused businesses ensure their AI-driven practices respect fundamental
human values such as dignity, freedom, equality and justice while complying with various
stakeholders’ preferences?
The empirical studies summarized in this issue indicate ways businesses can leverage
AI’s potential in emerging markets. To realize appropriate AI-derived values, business
leaders should build and nurture adaptive organizations with an open and collaborative
culture and teach workers skills that ensure a smooth intellectual transition (International
Finance Corporation, 2020). Future empirical studies can reveal AI’s disruptive potential and
the challenges of adopting, utilizing, integrating and applying AI to businesses in emerging
markets. Because data access is now a competitive advantage, scholars contend that AI will
widen technological and knowledge gaps between emerging and mature nations (Mayer-
Schonberger and Cukier, 2013). To prevent this gap from further retarding economic
development in emerging markets, businesses could use automated analysis of text, images,
audio and video to help the poor and unprivileged (Kopalle et al., in press).
Pagani and Champion (2021) proposed a way to check safety, accountability and societal
well-being per European Commission (2019) requirements for human-centric AI systems.
They posit that programmers’ diverse backgrounds may preclude algorithmic biases in
human–machine interaction design. Furthermore, businesses should adapt AI systems to
local contexts; for example, marketers could customize popular AI platforms (e.g. Apple Siri,
Microsoft Cortana, Amazon Alexa and Google Assistant) or create versions to reshape
marketing strategies such as branding, advertising and promotion. Similarly, businesses in
emerging markets could leverage AI systems to manage customer experience and
engagement by enhancing various service aspects (e.g. service design, co-creation, real-
time customer support, service recovery, satisfaction, loyalty, complaints and
customer churn).
IJOEM Finally, business scholars should address critical methodological issues. They could
17,4 apply AI-powered tools – such as facial recognition, speech recognition system, image
recognition, machine learning and natural language processing – to relevant research on
emerging markets. They should use innovative data analysis approaches to augment often-
insufficient purposive and rule-based data analyses. To harness AI-related opportunities in
emerging markets, scholars should attend to data generated via new forms of interaction (e.g.
virtual reality, augmented reality, metaverse and chatbot) among consumers, businesses and
920 NGOs (Hoffman et al., 2022). Furthermore, they should develop emerging market-compatible
measures of AI-related constructs.

4. Articles in the special issue

This special issue is devoted to advancing knowledge of AI-driven business strategies in
emerging markets. We are delighted to include 12 articles on adopting, utilizing, integrating
and implementing AI in various industries. We now summarize each article per its main
research questions (see Table 1).
In “The impact of artificial intelligence (AI) finance on financing constraints of non-SOE
firms in emerging markets,” Shao et al. (2022) discuss AI finance’s influence on the financial
constraints of non-SOE firms in China. The authors report that AI financing can alleviate
these constraints, especially for smaller and more innovative businesses in developing areas.
This finding has practical policy implications because non-SOE businesses are more
constrained in obtaining external financing than their SOE peers.
“Artificial intelligence in peer-to-peer lending in India: A cross-case analysis,” by Anil and
Misra (2022), presents research at the cusp of peer-to-peer (P2P) markets and AI in India, one
of the fastest-growing markets for fintech. Six breakout segments comprise Indian fintech,
with P2P as an important subsegment of “credit.” The article reveals (1) AI’s evolving role in
Indian P2P lending markets; (2) how a disruptive technology like AI is revolutionizing P2P
platforms with predictive intelligence for making credit decisions, thereby acting as a
catalyst for them and (3) how Indian P2P lenders using automated processes and manual
underwriting will eventually transition to totally automated processes.
The article by Lai and Luo (2022), entitled “How does intelligent technology investment
affect employment compensation and firm value in Chinese financial institutions?,” reports
on the nexus between intelligent technology investment and employee compensation and its
impact on firm value. They found a persistent inhibitory effect on this nexus in emerging
markets’ financial institutions and show increases in intelligent investment have a positive
two-year lagged effect on firm value. Their findings may help financial firms better
understand their need to address the subsequent growth-related costs of intelligent
technology input.
In “How do AI applications in service marketing differ from human employee to influence
consumer behaviors?,” Jiang et al. (2022) address this question by examining the moderating
role of service provider type (humanoid robot vs human employee) on consumer reactions.
They found consumers served by a humanoid robot are more easily convinced about the
utilitarian value of functional but not culturally mixed products. In contrast, consumers
served by a human employee prefer to be persuaded by cultural connotations and culturally
mixed rather than functional products. Furthermore, this effect is driven by perceived
usefulness (vs perceived enjoyment) when served by a humanoid robot (vs human employee).
The article by Fan et al. (2022), entitled “How can marketers design an AI chatbot creating
profits while catering to various demands from customers?,” addresses this question by
examining the contingent role that chatbot sales–service ambidexterity play in adapting to
customers’ personalization–privacy paradox. In taking an organizational ambidexterity
perspective to explore AI chatbot efficacy, they found the inherently negative (positive)
Authors/
Guest editorial
Study Data/Approach Key findings Contributions to the SI

Shao et al. Qualitative study with a The development of AI Emerging market countries
(2022) sample of non-SOE-listed finance can alleviate can ease financing
companies in China from financing constraints for non- constraints on non-SOE firms
2011 to 2018 SOE firms. This effect is more by promoting AI finance
pronounced for smaller firms, development 921
more innovative firms and
firms in developing areas
Lai and Qualitative study with a A persistent inhibitory effect Help practitioners in
Luo (2022) sample of 86 listed financial exists on the nexus of emerging countries better
institutions in China from intelligent technology understand that firms need to
2010 to 2019 investment and employee reasonably deal with the
compensation in financial subsequent cost of growth
institutions. The increase in caused by intelligent
intelligent investment has a technology input
positive two-year lagged
effect on firm value
Fan et al. Online survey data collected As the benefits of Enrich the literature on
(2022) from 507 AI chatbot users personalization decreased frontline ambidexterity and
and the risk to privacy extend it to human-machine
increased, the inherently interaction
negative (positive) effects of
imbalanced (combined)
chatbots’ sales–service
ambidexterity had an
increasing (decreasing)
influence on customer
experience
Jiang et al. Experimental data collected When served by a humanoid Offer insights for managers to
(2021) from 203 undergraduate service robot (vs. human develop service marketing for
students (Study 1) and 217 employee), consumers exhibit mixed products
frontline staff members more positive attitudes and
(Study 2) purchase intentions toward
functionally (vs culturally)
mixed products
Hamdan Used a machine learning Perceived benefit and ease of Deliver a decision support
et al. (2021) method to collect data from use are the most influential system for business leaders to
167 SMEs in Palestine’s determinants of blockchain estimate the potential for
largest industrial sectors adoption blockchain adoption
Dong et al. A theoretical framework is Collaboration value is a Provide a framework for
(2021) developed through grounded building block for intelligent enterprises to build an
theory and case analysis product ecosystems. These intelligent product ecosystem
ecosystems are upgraded by
coordinating products,
platforms and networks
Yao et al. Experimental data collected Higher social class Help multinational
(2022) from 93 consumers participants were more enterprises (MNEs) develop
(Experiment 1) and 196 willing than lower social class strategies for scaling up robot
participants (Experiment 2) participants to choose robot services
services in credence-based
service settings. Risk
aversion mediated the
interaction effect Table 1.
Summary of special
(continued ) issue articles
IJOEM Authors/
17,4 Study Data/Approach Key findings Contributions to the SI

Sharma Used a modified total Identifies ten key factors Detailed analysis of the ten
et al. (2021) interpretive structural essential for analyzing AI’s factors can help tourism firm
modeling (m-TISM) approach impact on a firm’s managers enhance
competitiveness competitiveness
922 Anil and Cross-case study based on Showcases AI’s evolving role Illustrate P2Ps still stuck to
Misra semi-structured interviews in Indian peer-to-peer lending manual underwriting and
(2022) with 6 NBFC-P2P founders (P2P) markets. Findings show the merit in AI-driven
and 12 fintech and P2P indicate that AI has reached a processes
industry experts tipping point in India
Sui and Mo Experimental data collected Moral standards declined for In emerging markets,
(2022) from 396 participants in low-SES but not high-SES managers and marketers
China (study 1) and 300 persons when using smart should be aware of this
participants in the UK (study devices (vs non-smart morality erosion and use
2) devices) preventive measures in
advance
Gao et al. Survey data collected from Two dimensions of AI Help enterprises better
(2022) 209 survey participants from technology stimuli positively understand customer
August to December 2020 affect smart customer psychology and AI
experience; the moderating technology to promote a
effects of contrasting positive customer experience
dimensions of technology and improve consumers’
readiness are significantly word-of-mouth intentions,
different; smart customer especially in the emerging
experience has a positive markets
effect on consumers’ word-of-
mouth intentions
Wang et al. Interview data collected from SMEs in central China are Firms should attend to
(2022) 66 SMEs across 20 industries enthusiastic about intelligent executives’ role in promoting
in central China transformation while facing intelligent transformation
internal and external and fully use policy support
pressures. They have been to access additional resources
forced to take a step-by-step
strategy based on actual
needs instead of long-term
overall system design,
constrained by limited
Table 1. resources

effects of imbalanced (combined) chatbots’ sales–service ambidexterity had an increasing

(decreasing) influence on customer experience. Furthermore, customer experience fully
mediated the association of chatbots’ sales–service ambidexterity with customer patronage.
In “Analysing the impact of artificial intelligence on the competitiveness of tourism firms:
A modified total interpretive structural modeling (m-TISM) approach,” Sharma et al. (2022)
focus on tourism firms’ use of AI to boost their competitiveness. Researchers and
practitioners are optimistic that AI can help tourism firms profitably deliver services and
products at competitive prices while enhancing customers’ satisfaction. AI-based systems –
such as chatbots and virtual assistants – are changing the tourism and hospitality industry.
Using a modified total interpretive structural modeling approach, Sharma et al. (2022)
explored the factors influencing tourism firms’ overall performance. Hierarchical
interrelationships among these factors are crucial to realizing AI’s potential. The model
also answers the “why” and “how” of these relationships. Adopting AI could help tourism and
hospitality firms mitigate risks and challenges while sustaining competitiveness.
 In “The impact of artificial intelligence technology stimuli on smart customer experience Guest editorial
and the moderating effect of technology readiness,” Gao et al. (2022) construct an impact
mechanism model for AI technology stimuli on word-of-mouth intentions. They found that (1)
two dimensions of AI technology stimuli (i.e. passion and usability) positively affect smart
customer experience, (2) different dimensions of technology readiness (i.e. optimism and
discomfort) have different moderating effects on the relationship between AI technology
stimuli and smart customer experience and (3) smart customer experience positively affects
word-of-mouth intentions. These results can help businesses better understand customers’ 923
psyches and use AI technology to create positive customer experiences that enhance word-of-
mouth in emerging markets.
Rising labor costs and a robotic technology boom have grown the role of robots in China’s
service sector. Considering this trend, it is imperative that marketers better understand the
factors that affect consumers’ attitudes toward AI robots, especially in emerging markets. In
“The impact of social class and service type on preference for AI service robots,” Yao et al.
(2022) examine how social class and service type jointly affect consumers’ willingness to
choose AI service robots. In essence, their research suggests that companies should adopt
different strategies when deploying AI service robots in different markets. In particular,
current AI robots are most appropriate for credence-based service markets with high upward
social mobility.
In “What is the impact of artificial intelligence on people’s morality in emerging markets?,”
Sui and Zhou (2022) examine the moderating effect of socioeconomic status (SES) on smart
device usage and moral standards in the UK and China. They found that using smart devices
(vs non-smart devices) lowers moral standards for low-SES persons but not high-SES
persons. Although smart device usage is the norm in developed markets, such usage is rising
among employees in emerging markets. To avoid moral degradation and deviant behaviors
in the workplace, managers can train their employees before adopting emerging technologies.
Smart product ecology can improve the collaborative value creation of physical products
by triggering major organizational strategic and structural changes. In “Product
intellectualization ecosystem: A framework through grounded theory and case analysis,”
Dong et al. (2022) develop a conceptual framework for a product intellectualization ecosystem
by studying two companies: Haier Group Company (Haier) and Xiaomi Corporation (Xiaomi).
After identifying a three-stage process that entails smart product unit, smart product system
and smart product ecology, they establish a coordination mechanism and three coordination
modes: product coordination, platform coordination and network coordination. Their posited
model delineates the collaborative values achievable via different coordination modes across
the stages.
In “What are determining factors of blockchain technology adoption in an emerging
market?,” Hamdan et al. (2022) respond to this question by using a machine learning method
to predict blockchain adoption by Palestinian firms. They relied on a Bayesian network
examination to develop an extrapolative decision support system, highlighting the
determinants – perceived benefit and ease of use – -that most influence managers’
predictions for their company’s technology adoptions. The findings provide insight into the
literature by showing reduced technological complexity is unrelated to perceived benefit,
perhaps because Palestinian SMEs are eager to adopt a new technology despite a perceived
difficulty and complexity of use.
Finally, “How to survive in the age of artificial intelligence? Exploring the intelligent
transformation of SMEs in Central China,” by Wang et al. (2022), explores how AI
transformed these SMEs. After interviews across 20 industries, they found that SMEs in
central China are enthusiastic about intelligent transformation despite internal and external
pressures. Constrained by limited resources, these SMEs were forced into a step-by-step
strategy based on immediate needs rather than overall system design. Although they should
IJOEM attend to executives’ roles in promoting intelligent transformation, overemphasizing social
17,4 responsibility will hinder SMEs’ intelligent transformation. These findings can help
businesses in labor-intensive and resource-deficient emerging markets.

5. Conclusion
AI’s rapid development provides an opportunity for innovative research in emerging
924 markets. To refine and extend existing theories and build new ones, we propose a conceptual
model of AI-driven business strategy to depict how businesses should adopt, utilize, integrate
and implement AI to gain a competitive advantage. By identifying the main forces,
businesses can better understand the regulatory, normative and cultural-cognitive
institutions that promote or constrain AI-driven businesses. Stressing the confluence of
legitimacy and efficiency of AI-driven business, our model suggests several promising
research streams.
The essential issue is how businesses can better leverage AI. Advances in institutional
theory can help businesses interpret, manipulate, revise and elaborate on business marketing
institutions. We hope this special issue will encourage business scholars to work and think
innovatively about institutional theory in the context of ever-expanding business markets.
Xinyue Zhou
School of Management, Zhejiang University, Hangzhou, China
Zhilin Yang
School of Management, North China University of Water Resources and Electric Power,
Zhengzhou, China and
College of Business, City University of Hong Kong, Kowloon Tong, Hong Kong
Michael R. Hyman
Marketing, College of Business, New Mexico State University, Las Cruces, New Mexico, USA
Gang Li
School of Management, North China University of Water Resources and Electric Power,
Zhengzhou, China, and
Ziaul Haque Munim
Faculty of Technology, Natural and Maritime Sciences, University of South -Eastern Norway,
Notodden, Norway

Acknowledgments
This special issue would never have been completed without the reviewers’ generous
contributions. The authors are honored to list the reviewers in alphabetical order by first
name: Afshin Omidi from University of Neuchatel, Switzerland; Aidin Namin from Loyola
Marymount University, USA; Alexis Papathanassis from Eastern Mediterranean University,
Turkey; Amal Dabbous from Universite Saint-Joseph Faculte de gestion et de management,
Lebanon; Anandakuttan Unnithan from Indian Institute of Management Kozhikode, India;
Andy Hao from University of Hartford, USA; Boris Urban from university of wits, South
Africa; Bublu Thakur-Weigold from ETH Zurich, Switzerland; Chandana Gunathilaka from
University of Sri Jayewardenepura, Sri Lanka; Chang Liu from Chinese University of Hong
Kong; Charles Zhang from UC Riverside, USA; Chen Yang from South China University of
Technology, China; Chenfeng Yan from Huazhong University of Science and Technology,
China; Chia-Yi Liu from Tunghai University, Taiwan; Connie Chang from Musashino
Daigaku, Japan; David McMillan from University of Stirling, UK; Fangjian Fu from Singapore Guest editorial
Management University, Singapore; Fenfang Lin from University of Southampton, UK; Feng
Yu from Wuhan University, China; Fu-Sheng Tsai from Cheng Shiu University, Taiwan;
Gang He from Stony Brook University, USA; Gongxing Guo from Shantou University, China;
G€unter Hofbauer from Technical University Ingolstadt, Germany; Heinz Herrmann from
Torrens University Australia, Australia; Hongyan Jiang from China University of Mining
and Technology, China; Itzhak Venezia from Academic College of Tel Aviv-Jaffa, Israel; Jay
Wang from University of Oregon, USA; Jianqing Chen from University of Texas at Dallas, 925
USA; Jing Chen from Texas A&M University Kingsville, USA; Juan Zhang from Shanghai
Institute of Foreign Trade, China; Jungkeun Kim from Kennesaw State University, US; K.V
Bhanumurthy from Delhi Technological University, India; Karine Aoun Barakat from
Universite Saint-Joseph Faculte de gestion et de management, Lebanon; Kimmy Chan from
Hong Kong Baptist University; Kritika Nagdev from Vivekananda Institute of Professional
Studies, India; Lai Ying Leong from University Tunku Abdul Rahman, Malaysia; Leanda
Care from Monash University, Australia; Lili Gai from The University of Texas Permian
Basin, USA; Luke Deer from The University of Sydney Australia; M. Omar Parvez from
Eastern Mediterranean University, Turkey; Matteo Landoni from Universita Cattolica del
Sacro Cuore, Italy; Matthew Lastner from UNC Wilmington, USA; Mikhail Komarov from
National Research University Higher School of Economics, Russia; Patrick Van Esch from
Kennesaw State University, USA; Quan Chen from City University of Hong Kong; Raine
(Ruiying) Cai from Colorado Mesa University, USA; Ran Liu from Central Connecticut State
University, USA; Rifat Sharmelly from Quinnipiac University, USA; Robert Luo from The
University of New Mexico, USA; Ryan Randy Suryono from Universitas Teknokrat
Indonesia, India; Şahin Əkbər from Azerbaijan State Economic niversity, Azerbaijan;
SANTANU ROY from Institute of Management Technology Ghaziabad, Indian; Seung Hwan
(Shawn) Lee from Ajou University South, Korea; Sevenpri Candra from Bina Nusantara
University, India; Sha Zhang from University of the Chinese Academy of Sciences, China;
Shibin Sheng from University of Alabama, USA; Stanislav Ivanov from Varna University of
Management, Bulgaria; Sumeet Gupta from Indian Institute of Management Raipur, India;
Sununta Siengthai from Asian Institute of Technology, Thailand; Susan Wakenshaw from
University of Warwick, UK; Swati oberoi dham from New Delhi Institute of Management,
India; Taufik Faturohman from Institut Teknologi Bandung, India; Ting Yu from University
of New South Wales, Australia; Tiziana Russo Spena from University of Naples Federico,
Italy; Wanyi Chen from Shanghai University, China; Weiguo (Patrick) Fan from University of
Iowa Tippie College of Business, USA; Wenchi Ying from Beijing Jiaotong University, China;
Xueyun Luo from Cornell University, USA; Yanghong Hu from University of Aberdeen, UK;
Yangjun Li from City University of Hong Kong; Yulong Yang from Zhejiang Gongshang
University, China and Yu-Ting Lin from University of New South Wales, Australia.
The authors also gratefully acknowledge the strong editorial and technical support from
Nibing Zhu at Beijing University of Foreign Studies during the lengthy review process.
The authors gratefully acknowledge the grants from the National Natural Science
Foundation of China (No: 71925005 and 72072152); National Social Science Foundation of
China (No: 19BGL224); City University of Hong Kong (No: CityU SRG 7005478 and CityU SRG
7005791) from the Research Grant Council of Hong Kong SAR (No: CityU 11502218) for
financial support.

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Further reading
Meyer, K.E. and Peng, W. (2016), “Theoretical foundations of emerging economy business research”,
Journal of International Business Studies, Vol. 47 No. 1, pp. 3-22.
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intelligence: closing the gap between ambition and action”, MIT Sloan Management Review,
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practices”, Journal of Marketing, Vol. 75 No. 4, pp. 166-182.
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9187-7de2ef5075bc/Reinventing-business-through-Disruptive-Tech-v2.pdf?MOD5AJPERES&CVID 929
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