Operations strategy of cloud-based firms: achieving firm growth in the Big Data era

Abstract

Purpose
Cloud computing is a major enabling technology for Industry 4.0 and the Big Data era. However, cloud-based firms, who establish their businesses on cloud platforms, have received scant attention in the extant operations management (OM) literature. To narrow this gap, the purpose of this paper is to investigate cloud-based firms from an operations strategy perspective.

Design/methodology/approach
A two-phase multi-method approach was adopted. In the first phase, content analysis of 27 reports from cloud-based firms was conducted, aided by text mining keyword extraction. Two data-related operations capabilities were identified and hypotheses were posited regarding the relationships between data resources (DR), operations capabilities and firm growth (FG). In the second phase, a sample of 190 cloud-based firms was collected. Seemingly unrelated regression and bootstrapping method were employed to test the proposed hypotheses using the survey data.

Findings
The content analysis indicates data as a key resource and both data processing capability and data transformational capability as critical operations capabilities of cloud-based firms. FG is regarded as a top priority in the cloud context. The regression results indicate that DR and the two capabilities contribute to the growth of cloud-based firms. Moreover, a follow-up bootstrapping analysis reveals that the mediating effects of the two capabilities vary between different types of FG.

Originality/value
To the authors’ best knowledge, this is one of the first OM studies on cloud-based firms. This study extends the operations strategy literature by identifying and testing the key operations capabilities and priorities of cloud-based firms. It also provides insightful implications for industrial practitioners.

Full text

Operations strategy of
cloud-based firms: achieving
firm growth in the Big Data era
Yongyi Shou and Xinyu Zhao
School of Management, Zhejiang University, Hangzhou, China, and
Lujie Chen
International Business School Suzhou,
Xi’an Jiaotong-Liverpool University, Suzhou, China
Abstract
Purpose –Cloud computing is a major enabling technology for Industry 4.0 and the Big Data era. However,
cloud-based firms, who establish their businesses on cloud platforms, have received scant attention in the
extant operations management (OM) literature. To narrow this gap, the purpose of this paper is to investigate
cloud-based firms from an operations strategy perspective.
Design/methodology/approach –A two-phase multi-method approach was adopted. In the first phase,
content analysis of 27 reports from cloud-based firms was conducted, aided by text mining keyword
extraction. Two data-related operations capabilities were identified and hypotheses were posited regarding
the relationships between data resources (DR), operations capabilities and firm growth (FG). In the second
phase, a sample of 190 cloud-based firms was collected. Seemingly unrelated regression and bootstrapping
method were employed to test the proposed hypotheses using the survey data.
Findings –The content analysis indicates data as a key resource and both data processing capability and
data transformational capability as critical operations capabilities of cloud-based firms. FG is regarded as a
top priority in the cloud context. The regression results indicate that DR and the two capabilities contribute to
the growth of cloud-based firms. Moreover, a follow-up bootstrapping analysis reveals that the mediating
effects of the two capabilities vary between different types of FG.
Originality/value –To the authors’best knowledge, this is one of the first OM studies on cloud-based firms.
This study extends the operations strategy literature by identifying and testing the key operations
capabilities and priorities of cloud-based firms. It also provides insightful implications for industrial
practitioners.
Keywords Cloud computing, Operations strategy, Firm growth, Cloud-based firm,
Data processing capability, Data transformational capability
Paper type Research paper
1. Introduction
Cloud computing, an enabling technology in Industry 4.0, contributes to the realisation of
valuable business offerings by providing high-performance, low-cost data storage and
computing services while connecting various stakeholders and enabling them to access IT
resources on-demand from any platform or device at any time (Mell and Grance, 2011;
Xu et al., 2018). Path-breaking cloud computing technologies can not only help firms make
use of Big Data, but also encourage firms to develop their capabilities to fulfil emerging
data-based business needs (Kumar et al., 2018). Cloud platforms, such as Amazon’s AWS
and Microsoft Azure, have attracted much attention in recent studies (Li and Kumar, 2018;
Retana et al., 2018); however, cloud platforms are insufficient for realising the technological
paradigm shift of Industry 4.0 (Christensen and Rosenbloom, 1995) until there are sufficient
firms who establish their businesses on the cloud. These firms, termed cloud-based firms in
International Journal of Operations
& Production Management
© Emerald Publishing Limited
0144-3577
DOI 10.1108/IJOPM-01-2019-0089
Received 16 February 2019
Revised 14 July 2019
Accepted 19 September 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0144-3577.htm
This work was supported by the National Natural Science Foundation of China under Grant
No. 71821002.
Cloud-based
firms

this study, turn data storage and computing capabilities of cloud platforms into data-based
products or digital services (Brinch, 2018; Hartmann et al., 2016; Kathuria et al., 2018).
Among the emerging business research on cloud computing, there are two main streams:
the benefits of cloud computing (Caldarelli et al., 2017; Ross and Blumenstein, 2015) and the
factors that affect cloud computing adoption (Gangwar and Date, 2016; Hadhri et al., 2017;
Loukis et al., 2017). However, few studies pay attention to the operations management (OM)
or operations strategy of cloud-based firms, except that Kathuria et al. (2018) explore the
strategic value of data technology integration between clouds and cloud-based firms, and
Jain and Hazra (2019) analyse the optimal capacity portfolio of private and public clouds.
Besides, a number of studies have evidenced the importance of DR and data-related
capabilities in non-cloud-based firms, although they mainly focus on the direct performance
effects of such resources and capabilities (Gunasekaran et al., 2017; Mamonov and
Triantoro, 2018). Undoubtedly, DR and data-related capabilities are also essential for
cloud-based firms. Nonetheless, the extant OM literature pays scant attention to cloud-based
firms, and hence an in-depth investigation of data-related capabilities in these firms is
necessary to broaden our understanding of these emerging type of firms.
To narrow the above-mentioned gap, this study endeavours to identify data-related
operations capabilities and then investigate their roles in cloud-based firms from an
operations strategy perspective. Operations strategy refers to the decisions and plans
focussing on a firm’s effective use of inputs and operations capabilities to achieve superior
competitiveness and long-term performance (Boyer et al., 2005; Slack and Lewis, 2017). It
offers a granular understanding by connecting business strategy with firm operations
(Slack and Lewis, 2017). Regarding data as key resources, cloud-based firms can establish
data-related operations capabilities to utilise such resources and compete for survival and
growth, which has not yet been empirically tested in the extant OM literature. Hence, from
the operations strategy perspective, we aim to answer the following research questions:
RQ1. What are the key data-related operations capabilities of cloud-based firms?
RQ2. What are the roles of the data-related operations capabilities in cloud-based firms
from an operations strategy perspective?
To address these two research questions, this study adopts a two-phase multi-method
approach (Boyer and Swink, 2008; Choi et al., 2016). Firms based on cloud platforms
comprise an emerging research area, with few empirical studies. Thus, observation
represents a good starting point for this study (Boer et al., 2015). Content analysis is
employed because it is suitable for addressing new research questions (Dooley, 2016) and
can be used in concert with other empirical methods (Tangpong, 2011). In the first phase of
this study, an explorative content analysis is conducted to address the first research
question. In the second phase, seemingly unrelated regression (SUR) and bootstrapping
method are used to analyse the data collected by questionnaire survey for the second
research question. Given the new research context of cloud computing, validity and rigour
of this study are strengthened by triangulation of the two phases (Choi et al., 2016).
This study contributes to the literature in several ways. To our best knowledge, this
study is one of the first OM studies on cloud-based firms. It identifies data processing
capability (DPC) and data transformational capability (DTC) as two key operations
capabilities and firm growth (FG) as a top priority of cloud-based firms. It empirically
confirms the contribution of DR to FG, which is mediated by both operations capabilities.
More interestingly, DPC offers a significant mediating effect to end-user growth while DTC
contributes more to data asset growth and sales growth, which indicates that cloud-based
firms should adopt a suitable strategy to prioritise their capability building in different
lifecycle stages. These findings extend the extant operations strategy literature.
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2. Literature review
2.1 Cloud-based firms
The literature has recently witnessed a sharp increase in studies on cloud computing. There
are two main streams of research. One stream focusses on cloud computing adoption and
investigates the impact of various factors, such as cloud provider capability (Habjan and
Pucihar, 2017), cloud security and availability (Gangwar and Date, 2016), employees’ICT
skills (Hadhri et al., 2017) and ICT infrastructure (Loukis et al., 2017). Some studies also
examine the effects of inter-firm relationships and environmental factors on cloud
computing adoption (e.g. Chou et al., 2017; Gangwar et al., 2015; Hadhri et al., 2017). The
other stream focusses on the benefits of cloud computing adoption. Caldarelli et al. (2017)
summarise the key benefits expected, including cost reduction, greater flexibility, better
mobility and information access, ICT employee reduction and decreased ICT focus. Cloud
computing also provides convenient global market access for small firms (Ross and
Blumenstein, 2015).
Some studies investigate the performance effect of cloud computing (e.g. Chang et al.,
2019; Gupta et al., 2018, 2019; Kathuria et al., 2018; Shee et al., 2018). For instance,
cloud-enabled integration helps firms improve their organisational and supply chain
performance (Gupta et al., 2018; Kathuria et al., 2018; Shee et al., 2018); and cloud adoption
helps firms build competitive advantage (Chang et al., 2019). Gupta et al. (2019) state that
data predictive capabilities significantly contribute to firm performance. Although a
number of studies have investigated the performance effects of cloud computing, these
studies are conducted in traditional firms that adopt cloud computing as an extra
technology to strengthen their existing businesses. Few studies are on firms that are based
on clouds. Moreover, these studies emphasise the financial performance (such as ROI and
revenue) or operational performance (such as productivity), except Shee et al. (2018), which
includes sales growth as one of the indicators of financial performance. Indeed, FG has been
recognised as a competitive priority by cloud-based firms (Mamonov and Triantoro, 2018;
Mitra et al., 2018). Owing to the winner-take-all competition in digital service markets,
cloud-based firms are driven to regard FG as the most imperative priority. However, the
growth of cloud-based firms has been neglected in the extant OM literature.
2.2 Operations strategy
Since Skinner (1969) first specified the gap between manufacturing operations and
corporate strategy, operations strategy has been regarded as a powerful “strategic weapon
in competition”(Skinner, 1996, p. 3) by reconciling operational resources and market
requirements (Skinner, 1969; Slack and Lewis, 2017). Operations strategy studies emphasise
“the effective use of inputs and process capabilities to produce outputs that help to achieve
business and corporate goals”(Hitt et al., 2016, p. 79). Specifically, operations strategy refers
to decisions and plans about needed resources and capabilities related to resource
deployment processes, which help firms achieve competitive advantages by identifying
competitive priorities as their top value propositions (Boyer et al., 2005; Park and Paiva,
2018; Slack and Lewis, 2017). Previously, operations strategy studies focus on the traditional
manufacturing and service sectors (Boyer et al., 2005; Spring and Araujo, 2014). A broader
view of operations strategies is needed as the business evolves (Boyer et al., 2005), especially
in the emerging Big Data era when digital products and services could be new foci of
operations strategy research.
Therefore, this study attempts to extend the operations strategy research in the Big Data
era. Advanced technologies, such as cloud computing, can be regarded as strategic enablers
(Boyer et al., 2005). While cloud computing techniques provide firms with a novel paradigm,
offering instant, elastic and cost-effective data storage and processing services (Agrifoglio
et al., 2017; Subramanian and Abdulrahman, 2017), it also fosters new firms whose
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firms

operations strategy is mainly based on cloud platforms. These firms have to allocate
resources and develop capabilities according to competitive priorities in this new context
(Liu and Liang, 2015). In the cloud computing context, DR become the core inputs and the
key processes of input transformation involve data-related capabilities (Hartmann et al.,
2016). Previous operations strategy research, mostly in manufacturing industries, has
identified two categories of capabilities (Boyer et al., 2005; Hayes and Wheelwright, 1984):
the first focusses on structural or “bricks-and-mortar”decisions about facilities, technology
and capacity, while the second infrastructure one captures the policies and systems for
managing the structural components. The content analysis of this study also reveals
two categories of capabilities in cloud-based firms, though both are more data-centred.
Competitive priorities are a core decision variable of operations strategy, guiding the
emphasis on resource aggregation and capability exploiting of organisations (Boyer and
Lewis, 2002). Hayes and Wheelwright (1984) identify four typical competitive priorities for
manufacturers: cost, delivery, flexibility and quality. Recent studies call for an update of the
classic works to reflect new competitive ideas regarding requirements in the business
context, for example, servitisation and sustainability priorities (Boyer et al., 2005; Longoni
and Cagliano, 2015).
In the emerging cloud computing context, growth has exceeded other priorities to be the
top concern of cloud-based firms. Fast growth ensures firms the benefits of larger market
share, higher network effects and hence superiority in the winner-take-all competition
(Hayes, 2002), which is especially vital in early lifecycle stages of these firms. Therefore,
growth is a reasonable competitive priority choice for cloud-based firms. It is argued that
operational resources and capabilities should be carefully matched with the firm’s
competitive priorities (Boyer and Lewis, 2002). Particularly, firms are able to achieve growth
with effective operation functions (Clegg, 2018) and proper capabilities (Bi et al., 2017;
Matthias et al., 2017).
2.3 Resource-based view
The resource-based view (RBV ) has long been viewed as a solid theoretical lens to
understand how operations strategy can create sustainable competitive advantage
(Boyer et al., 2005). RBV has been widely associated with FG, since valuable, rare, inimitable
and non-substitutable resources help firms perform better in opportunity seizing, product
and service offerings, and value creation (Kor and Mahoney, 2004; Nason and Wiklund,
2018; Peteraf and Barney, 2003). Hayes and Upton (1998) and Hitt et al. (2016) note that
acquiring and leveraging resources and operations capabilities help firms create sustainable
competitive advantages, which becomes embedded within organisations and inherently
difficult to imitate.
From the RBV perspective, data have been regarded as a valuable, rare, unique and
appropriable resource (Grover et al., 2018; Hitt et al., 2016; Mamonov and Triantoro, 2018).
DR is shareable between organisations and departments; it is non-fungible and each data
item is hardly substitutable; it is versatile because the same data can be used to generate
various outcomes, and although it may depreciate over time, data are renewable to offer
more value (Levitin and Redman, 1998; Mamonov and Triantoro, 2018). Increasing the
volume and sources of data helps unravel its potentials of higher value in the emerging
business contexts owing to these unique characteristics (Mamonov and Triantoro, 2018). As
Hartmann et al. (2016) propose, firms with cloud-based business models “rely on data as a
key resource”(p. 1382) to guarantee their growth and profit.
While DR plays a critical role for cloud-based firms, recent research argues that
the successful application and exploitation of data contribute to a firm’s competitive
advantage (Matthias et al., 2017). Following classic operations strategy works, we assume that
there are two types of data-related operations capabilities, corresponding to structural and
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infrastructural decisions (Boyer et al., 2005; Hayes and Wheelwright, 1984). For the structural
dimension, cloud-based firms build up data processing capabilities by utilising dynamic,
scalable IT infrastructure and software applications of cloud computing (Chang et al., 2019;
Hartmann et al., 2016). For the infrastructural dimension, Liu et al. (2015) and Ylijoki and
Porras (2019) highlight a firm’s ability to transform its organisational processes and search for
business opportunities for the strategic use of its data. Such organisational capability also
engages in formal collaboration and control processes to achieve firms’business needs and
priorities (Mamonov and Triantoro, 2018; Wamba et al., 2017). However, no research has
investigated the operations capabilities of cloud-based firms from an operations strategy
perspective and distinguished the structural and infrastructural dimensions.
3. Research design
This study constructs a multi-method triangulation research design to elucidate operations
capabilities and their contribution to the growth of cloud-based firms (Boyer and Swink,
2008; Choi et al., 2016). The triangulation approach, which combines multiple research
methods, can develop and validate the findings or conclusions of each method (Venkatesh
et al., 2013). Thus, research quality can be improved by mitigating the intrinsic limitations of
any single method (Boyer and Swink, 2008).
Specifically, content analysis followed by a survey-based regression was adopted in this
study. These two methods complement each other (Singhal et al., 2008). Given the very
limited research on the operations strategy of cloud-based firms, content analysis is
regarded as a useful tool to conceptualise the core resources and capabilities and frame the
research model for the cloud-based context (Choi et al., 2016; Sodhi and Tang, 2014).
Secondary data were collected from a competition that required participant cloud-based
firms to provide a report on their operations strategy, which “tap the construct of interest
exactly”(Boyer and Swink, 2008, p. 342). Compared with other qualitative methods such as
case study, content analysis is more objective, systematic and of better generalisability with
secondary data (Chan et al., 2016; Lombard et al., 2002). Researchers may concern about the
validity of content analysis because publicly collected materials may be biased owing to
elaborated statements (Tangpong, 2011). To address this concern, a survey-based
regression was conducted in the second phase. While the content analysis phase identified
the two operations capabilities and growth priority of cloud-based firms, the survey-based
phase tested the proposed relationships empirically. Moreover, secondary data in the
content analysis phase help alleviate the concerns in the survey phase, e.g. common method
bias (Chan et al., 2016). The triangulation of selected methods and corresponding data
categories increased the rigour of this study (Boyer and Swink, 2008; Chan et al., 2016).
4. Phase 1: content analysis and hypotheses development
4.1 Content analysis
Content analysis was conducted on secondary data from clients of Alibaba Cloud. Alibaba is
one of the world’s most valued internet companies, and Alibaba Cloud is a key player in its
smart business ecosystem (Zeng, 2018). Alibaba Cloud, established in 2009, was the first in
Asia Pacific cloud computing market and the third in the global market in 2018
(Williams et al., 2019). Alibaba Cloud served near a half of the customers in Chinese cloud
market (Nie, 2018). In 2016, Alibaba Cloud initiated a business competition to promote its
cloud computing services, in which over 600 client firms participated. A panel of experts
from the industry and academia screened the participant firms. The firms that were
innovative in taking advantage of cloud computing services and had superior performance
were selected by the panel. Then 100 firms were invited to submit semi-structured text
reports, in which they described their industrial background, business models and how they
Cloud-based
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utilised cloud computing to develop their businesses. The panel selected 30 firms as
short-listed firms for the final competition in March 2017, among whom the top 20 were
awarded by Alibaba Cloud. The authors carefully read the short-listed firms’reports and
dropped three firms because they were not cloud-based although they were Alibaba Cloud’s
clients. Finally, 27 reports were used for the content analysis. The reports were written by
partners or managers of these short-listed firms. Each report had an average length of five
pages. The sample size satisfies the requirement of content analysis and is close to prior
studies in similar nature (Chan et al., 2016). The profile of 27 firms is presented in Table I.
Content analysis has been adopted by many OM studies (e.g. Chan et al., 2016; Chen et al.,
2015). Following the instructions by Tangpong (2011) and Vincent et al. (2007), content
analysis was conducted in the following procedures: determining recording units; defining
proper content categories based on the context and literature review; developing and
refining the coding rules by checking the meaning of recording units and which category it
belongs to; and coding and assessing reliability.
The present study adopted a text mining method to extract keywords from the reports.
A text mining method could dramatically reduce subjective bias (Guerreiro et al., 2016). The
TextRank algorithm was utilised, which uses a relation-oriented, graph-based ranking
model to process nature language texts and identify keywords accurately (Mihalcea and
Tarau, 2004). After removing stopwords (i.e. common terms of limited importance) and
case-specific terms, a keyword cloud for all reports was generated (see Figure 1). The
high-frequency keywords include “data”,“platform”,“service”,“system”,“user”,
“information”and “growth”among over 200 keywords.
The authors strictly followed the above-mentioned procedures to determine recording
units and define content categories. Two coders individually checked the meaning of each
keyword across the records to ensure that the meaning of keywords is consistent in textual
expressions. Thus, the semantic validity was guaranteed. From the RBV perspective, the
majority of keywords were classified into four categories: DR, the structural and
infrastructural dimensions of operations capability, and outcomes. The refined coding rules
are presented in “Coding rules for content analysis”. The coding process was conducted by
two coders who were familiar with the research questions and text reports. Table II reports
the inter-coder reliability of each category, which is higher than the threshold value of
0.7, ensuring the reliability of the content analysis (Gölzer and Fritzsche, 2017).
Number %
Firm age (years)
⩽5 16 59.2
5–10 8 29.6
⩾10 3 11.1
Industry
ICT 12 44.4
Manufacturing 2 7.4
Finance 2 7.4
Education 2 7.4
Healthcare 3 11.1
Others 6 22.2
Venture capital received (million RMB)
W10 11 40.7
⩽10 6 22.2
na 10 37.0
Table I.
Profile of the
short-listed firms
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Coding rules for content analysis:
Instructions are as follows: based on the content of each text unit, coders assign each text
unit to one of the following categories:
(1) A text unit is assigned to the “data resource”category if its content indicates that the
firm has data stored (on the cloud platforms); information and data source(s);
website content or text items; media data; and client behaviour data.
(2) A text unit is assigned to the “data processing capability”category if its content
indicates that the firm has data analysis model(s), project(s) or artificial intelligence
programme(s); technology(ies) of cloud computing, IOT devices and so on;
digital system(s) for specific business purposes; mobile application(s), software
development and updating; monitoring the condition of devices or programmes or
malfunction detection; data auto-acquisition; data standardisation or preparation;
and well-designed human–computer interaction.
(3) A text unit is assigned to the “data transformational capability”category if its
content indicates that the firm has management practices or business processes that
are designed, implemented, improved based on data analysis; cooperation between
employees, departments or companies that is aided by data technology; demand of
services or products that is precisely predicted based on data; solution(s) proposed
after data analysis; innovative opportunities identified after data acquisition;
data-enabled personalisation of products or services; and improved efficiency based
on data analysis results.
Note: The font size of a keyword is proportional to its frequency in the
reports
Figure 1.
Keyword cloud
for the reports
DR DPC DTC FG Total
Codings of coder 1 51 73 51 51 226
Codings of coder 2 44 65 53 36 198
Matching codings 34 56 40 33 163
Inter-coder agreement 0.72 0.81 0.77 0.76 0.77
Table II.
Number of codings
and inter-coder
reliability
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(4) A text unit is assigned to the “firm growth”category if its content indicates that the
firm has an increasing number of users, clients, members or enlarging online
community; fast development of main business; increasing service or information
requirement; growth of firm scale or sales; venture investments; and better and
various experience offered.
Note: italic words were identified by text mining.
The content analysis results facilitate better understandings of this study by
recognising the key constructs and potential relationships. DR from various sources were
extensively mentioned in the reports. It is assumed that the operations capabilities of
cloud-based firms have specific structural and infrastructural dimensions, which was
evidenced by the content analysis. FG was confirmed as a highly-recognised competitive
priority of cloud-based firms.
4.2 Hypotheses development
To further understand the relationships between DR, operations capabilities and FG
of cloud-based firms, hypotheses were proposed based on the RBV and content analysis of
the reports.
4.2.1 Data resource. The importance of DR has been recognised by previous studies
(Grover et al., 2018; Mamonov and Triantoro, 2018) and demonstrated by the content
analysis. Among the 27 short-listed firms, 24 reported that they had valuable DR. Firms
emphasised the specific DR they possessed. For example, a firm claimed that their data
centre has stored “more than 100 million webpages, 2 billion pictures, and 10 billion product
items, transaction records and articles”. One startup admitted that their data were from
three distinct sources: “market research, data sharing with leading companies like Alibaba,
Tencent and Huawei, and automatic acquisition from social media”. Another firm reported
that their data system is connected with “over 200 enterprises’security databases and more
than 1,000 public surveillance channels”. Thus, DR is regarded as a key resource and the
foundation of cloud-based firms’main operations and value proposition.
Unique DR help attract and lock in more users and customers (Amit and Zott, 2001). The
content analysis results show that cloud-based firms tend to obtain data that can seldom be
accessed by customers before. Providing customers the proper data they want creates great
value for cloud-based firms and leads to fast growth. In turn, the more the data generated
from use, the higher the opportunity for future purchase and use –a typical positive
feedback loop. It is advocated that DR is an important operations resource to facilitate
production, service offering and eventually FG (Manyika et al., 2011). In short, DR
contributes to the growth of cloud-based firms, as proposed in the following hypothesis:
H1. DR is positively related to the growth of cloud-based firms.
4.2.2 Data capabilities. Among the 27 firms, 25 emphasised the importance of DPC. DPC
refers to the structural dimension of operations capability that directly enables firms to
provide digital services or products based on cloud services. Traditional manufacturing
firms use “brick-and-mortar”facilities to process raw materials (Boyer et al., 2005), while
cloud-based firms use cloud platforms to process data. Content analysis of the reports
identified three typical processes of data processing –data auto-generation, data
preparation and aggregating, and data analysis –as discussed in prior studies
(e.g. Hartmann et al., 2016; Kiron et al., 2014). For instance, one short-listed firm stated
that they “adopted technologies such as IoT devices, mobile internet and cloud computing to
automatically capture, mine and analyse the data of elders, including their daily activities,
safety and health conditions”. A large part of data is unstructured, and these data have to be
cleaned, extracted, translated and systematically stored (Gupta and George, 2016). Thus, the
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value from data can be created and captured through such processes (Ylijoki and Porras,
2019). For example, another firm reported the processes of data analytics in their core
services: “Our platform is able to build models by machine learning on existing data. It then
applies the model to new scenarios to automatically select, evaluate and analyse new data”.
DPC yields competitive advantages for firms. Raguseo (2018) states two benefits of DPC:
it can dramatically enhance productivity through automatic and efficient data acquisition,
aggregating and analysis procedures; and it helps provide better cloud products and
services. For instance, one short-listed firm provides warning and maintenance services for
air-conditioning devices. With IoT sensors and an efficient data analytics cloud platform,
the firm is able to detect real-time signals and maintain the devices in good condition.
Several short-listed firms claimed that they relied solely on their extraordinary DPCs to win
orders. Hartmann et al. (2016) suggest that business insights generated through data
preparation, aggregation and analytics also contribute to FG. Therefore, the second
hypothesis is proposed:
H2. DPC is positively related to the growth of cloud-based firms.
While manufacturers need routines and systems to manage its structural components
(Boyer et al., 2005), a cloud-based firm needs a similar capability to utilise its DPC. Data
transformation capability (DTC) refers to the infrastructural dimension of operations
capability that facilitates cloud-based firms to sense business opportunities and make
strategic business decisions about the way of data value creation (Barton and Court, 2012;
Liu et al., 2015). Out of the 27 short-listed firms, 21 emphasised their efforts in improving
DTCs. Innovative opportunities for the strategic use of data were frequently mentioned. For
example, one firm stated that they “well recognised the new requirements of stakeholders in
constructing a smart city and designed the data management procedures focussing on the
establishment and operations of smart communities”. Another reported that they
“reorganised their data service procedures to flexibly fulfil the fast-growing, diverse
demands after multiple rounds of interviews with customers and partners, and several times
of in-house brainstorming”.
Recent studies have stressed the importance of DTC, which is a critical, cross-functional,
infrastructural capability for firms to grow rapidly in the digital age (Branchet and Sanseau,
2017). By identifying emerging opportunities, DTC helps firms to update their business
practices and realise value creation potential (Raguseo, 2018; Wang et al., 2018). Wamba
et al. (2017) highlight that from a process view, DTC involves routines to manage a firm’sDR
according to its business needs and priorities. With a formalised structure, cloud-based
firms could make strategic business decisions on data value creation and capture (Ferraris
et al., 2019; McAfee and Brynjolfsson, 2012). Firms that efficiently respond to market
changes and create innovative services may enjoy rapid growth (Grover et al., 2018).
Therefore, the following hypothesis is posited:
H3. DTC is positively related to the growth of cloud-based firms.
4.2.3 The mediating role of data capabilities. Hitt et al. (2016) emphasise that operational
capabilities may have great influence on leveraging operations resources to competitive
positioning. Recent studies suggest potential relationships between DR, data-related
capabilities and growth priority (Hartmann et al., 2016; Mamonov and Triantoro, 2018;
Ylijoki and Porras, 2019). Ylijoki and Porras (2019) conceptually propose that DR contribute
to economic performance via data capabilities. In this regard, data capabilities are based on
DR. For example, one short-listed firm claimed that they “conducted deep learning using
their data, hence improved the data processing capability and achieved fast growth”.Itis
likely to develop advanced data capabilities with adequate DR. Meanwhile, DR may
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contribute to FG indirectly. Hartmann et al. (2016) identify two means by which DR create
value: incremental improvement and optimisation, and product and business model
innovation. The former could be achieved through DPC while the latter may be more
dependent on DTC (Raguseo, 2018). Data may also yield strategic values for organisations
through effective DTC (Grover et al., 2018; Ren et al., 2017). In other words, DPC enables the
scalable exploitation of DR by encapsulating data within products and services, while DTC
helps firms build up formal processes and mechanisms to better sense and seize business
opportunities (Mamonov and Triantoro, 2018). Therefore, the fourth hypothesis is proposed:
H4. The relationship between DR and the growth of cloud-based firms is mediated by
DPC and DTC.
5. Phase 2: hypotheses testing
To ensure the generalisability of the content analysis results and provide more insights
(Choi et al., 2016), data were collected through a questionnaire survey to test the hypotheses
proposed in the first phase of this study.
5.1 Sample and data collection
To test the proposed hypotheses, data were collected from cloud-based firms in China. China
is one of the fastest growing cloud markets worldwide, with a market size of three billion
dollars and a growth rate of 83 per cent in the first half of 2018 (Nie, 2018). A questionnaire
was developed to collect information from cloud-based firms. The guidelines in the literature
were followed in designing the survey research (Dillman et al., 2014; Flynn et al., 2018; Petter
et al., 2007). Rigorous design helps minimise the potential bias and better address the
research questions (Flynn et al., 2018). The initial questionnaire was designed in English and
a rigorous translation/back-translation process was followed. Then the Chinese version of
the questionnaire was used for data collection. Relevant measures in the literature were
reviewed and adapted for the research context. A pilot study was conducted using a
convenience sample of eight senior managers from cloud-based firms, who answered all
items and provided feedback about the design and wording of the questionnaire. The
questionnaire was then refined according to the results of the pilot study.
To obtain a representative sample, the data collection was conducted by a professional
survey company in 2018. Key informants were strictly selected for reliability. Since the unit
of analysis in this study is cloud-based firms, senior or middle managers of cloud-based
firms were invited, for they may have a thorough understanding of the firm’s operations
(Flynn et al., 2018). The respondents were stressed that their responses were highly
confidential and only for academic use. A pool of 500 cloud-based firms was randomly
selected from the database of the survey company. Given the credibility of the survey
company and its large database, the representativeness of the sample could be guaranteed
(Porter et al., 2019). Among the contacted firms, 266 cloud-based firms replied to the survey,
with a response rate of 53.2 per cent. After dropping responses with missing data,
190 questionnaires constituted the final sample, with a usable response rate of 38.0 per cent.
Table III presents the demographic information of the final sample.
A key issue in sampling is whether the sample represents the population of interest
(Malhotra and Grover, 1998). To evaluate the sampling frame error, we compared the
distribution of cloud platforms adopted by the sampled firms with the market share data.
As shown in Table IV, Alibaba Cloud was the leading service provider in China, followed by
Tencent Cloud, and both had a percentage close to their market share reported by a recent
IDC report (Nie, 2018), which indicates that the sample is not biased. In an attempt to
examine non-response bias, we compared the response patterns by testing early 25 per cent
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and late 25 per cent responses (Chen et al., 2018), and found no significant difference.
In short, the sample appears to be fairly representative.
Post-hoc power analysis was conducted to evaluate the sufficiency of the final sample
(Hsu and Sabherwal, 2012). Using statistic software G*Power 3, the statistical power for the
multiple regression was above 0.98 with the sample size 190, an αof 0.05, and a medium
effect size 0.15. The SUR results show that the actual effect size is much larger. Therefore,
the sample size is adequate (Baroudi and Orlikowski, 1989).
Given the nature of this study (it is the first to address the research questions in this
emerging context and most cloud-based firms are small firms), it is acceptable to use
single respondents in collecting data (Flynn et al., 2018). Yet, it is important to assess and
mitigate the possibility of common method variance since the data were collected from
single respondents (Podsakoff et al., 2003). The questionnaire was developed intentionally to
avoid implying any relationship between constructs. Anonymity of respondents was
guaranteed explicitly (Podsakoff et al., 2003; Sousa and da Silveira, 2019). The data were
Number %
Firm age (years)
⩽5 21 11.1
5–10 47 24.7
10–15 52 27.4
15–20 30 15.8
⩾20 40 21.1
Number of employees
⩽50 18 9.5
50–100 28 14.7
100–500 107 56.3
500–1,000 20 10.5
⩾1,000 17 8.9
Industry
ICT 136 71.6
Manufacturing 29 15.3
Finance 11 5.8
Others 20 10.5
Job title of respondents
Founder/co-founder 15 7.9
Vice president/CEO or above 6 3.2
(Deputy) manager 69 36.3
(Deputy) head of a department 99 52.1
Table III.
Sample overview
Sample percentage (%) Market share (%)
Alibaba Cloud 43.8 43.0
Tencent Cloud 18.0 11.2
Huawei Cloud 12.4 2.3
NetEase Cloud 8.2 –
E Cloud (China Telecom) 6.4 7.4
Microsoft Azure 4.5 3.7
Others 6.7 32.4
Note: The market share was estimated by IDC (Nie, 2018)
Table IV.
Cloud platforms
reported by sampled
firms
Cloud-based
firms

tested for normality and outliers using the Kaiser–Meyer–Olkin (KMO) measure of sampling
adequacy and Bartlett’s test of sphericity. The tests presented a KMO value of 0.87 with the
significance of Bartlett’s test at the 0.001 level, indicating that the data were suitable for
factor analysis. The common method variance was tested using Harmon’s one-factor test
(Podsakoff et al., 2003). The first factor explained only 32 per cent of the total variance, and
more than one factors were extracted. The results suggest that common method variance is
not a serious concern in this study (Podsakoff et al., 2003).
5.2 Measures
Measures used in this study were adapted from the extant literature, as presented
in Table V. The respondents were asked to compare with their main competitors and rate
each item through a seven-point Likert scale (1 ¼“strongly disagree”or “much slower”and
7¼“strongly agree”or “much faster”).
DR are fundamental to cloud-based firms, and can be generated via multiple sources.
Data may be acquired from internal or external sources or both (Gupta and George, 2016;
Kiron et al., 2014). Internal data refer to a firm’s operations data while external data are those
collected from multiple sources, such as the internet, e-commerce communities, mobile
devices and IoT devices (Gupta and George, 2016). Cloud-based firms can also integrate data
from different sources to form unique databases.
DPC refers to a firm’soperationscapabilityinregardto data generation, preparation
and analytics. In the extant literature, an information systems view is widely adopted to
analyse data technologies (Ylijoki and Porras, 2019). For example, Hartmann et al. (2016)
examine data-driven startups and classify data technologies into several clusters,
Constructs and items Mean SD Sources
Data resource (DR)
DR1 More unique external data source 5.395 1.146 Gupta and George (2016)
DR2 More adequate internal data 5.200 1.096
DR3 Better access to large volume data for
analysis
5.142 1.225
Data processing capability (DPC)
DPC1 Better mastery of automatic data
generation technologies
5.179 1.090 Fuchs et al. (2018), Hartmann
et al. (2016), Rai et al. (2006)
DPC2 Better mastery of data processing and
aggregating technologies
5.311 1.185
DPC3 Better application of data analytics
techniques (e.g. data mining, deep
learning)
5.274 1.226
Data transformational capability (DTC)
DTC1 Continuous examination of innovative
opportunities for the strategic use of
data
5.563 0.920 Ferraris et al. (2019),
Liu et al. (2015),
Wamba et al. (2017)
DTC2 Performing data planning processes in
systematic and formalized ways
5.521 1.208
DTC3 Clear responsibility for data analytics
development
5.363 1.209
Firm growth (FG)
FG1 Growth of sales 5.068 1.057 Cho and Pucik (2005), Nason and
Wiklund (2018), Song et al. (2018)
FG2 Growth of end users 4.895 0.882
FG3 Growth of data assets 5.032 0.906
Table V.
Constructs and
survey items
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including data generation, transforming, cleaning, aggregation (organising and selecting)
and data analytics. Similar taxonomies are proposed by other studies (e.g. Kiron et al.,
2014). Such a process model is also supported by the content analysis results in the
present study. Hence, DPC was measured through three items –data generation, data
preparation and data analytics.
DTC is a firm-level operations capability that can help identify business opportunities
and explore the value of data to the extent as far as possible. Therefore, the present study
defines DTC as a reflective construct, which focusses on data-related opportunity sensing,
planning, coordination and value creation (Shamim et al., 2019). Survey items were adapted
from prior studies (Ferraris et al., 2019; Liu et al., 2015; Wamba et al., 2017).
FG is the dependent variable in this study. The growth of cloud-based firms is measured
in terms of the growth of sales, end users and data volume. Sales growth is widely adopted
in the extant literature (Nason and Wiklund, 2018). The growth of end users is a proper
measure for firms that mainly provide digital information or services (Song et al., 2018). For
cloud-based firms, data are recognised as the most valuable asset while asset growth is used
by prior studies as a growth performance indicator (Cho and Pucik, 2005; Grover et al., 2018;
Nason and Wiklund, 2018).
The present study includes firm size and firm age as control variables (Nason and
Wiklund, 2018). Firms with a larger size or more years of experience may have more
resources and capabilities to achieve superior performance. Firm size is measured by the
natural logarithm of the number of employees (Croom et al., 2018). Firm age is measured by
the natural logarithm of years (Anderson and Eshima, 2013). Besides, dummy variables of
industry were included (Sousa and da Silveira, 2019).
5.3 Reliability and validity
In this study, three constructs (i.e. DR, DPC and FG) were established as formative
constructs. Constructs should be modelled as formative under the following conditions:
the direction of causality is from indicators to construct; the indicators need not be
interchangeable; there is no need for indicators to covary with each other; and
the nomological net of indicators can differ ( Jarvis et al., 2003; Petter et al., 2007). The three
constructs meet the above criteria. For example, data volume and data sources may not
increase together but they both indicate the unique DR which firms hold. A cloud-based
firms could develop data analysis technologies using external data so data acquisition is not
a prerequisite. Cloud-based firms may not achieve fast growth in all three aspects (i.e. sales,
end users and data volume) simultaneously. Previous studies have proposed similar
formative constructs, such as data (Gupta and George, 2016), data capability (Fuchs et al.,
2018) and FG (Cho and Pucik, 2005). Thus, the formative constructs are proper for the
concepts in this research context. It is advocated that the weighting of formative indicators
should be specified a priori as part of construct definition (Lee and Cadogan, 2013). Given
the importance of each indicator and the explorative nature of this study, all indicators were
equally weighted.
Table VI shows the significance and variance inflation factor (VIF) value of each
indicator and the adequacy coefficient (R2
a) for each formative construct. All indicators are
significant and have VIF values lower than 3.3, which indicates the reliability of the
indicators (Petter et al., 2007). Further, the validity of the formative constructs was evaluated
using R2
a, which is calculated by dividing the sum of the squared correlations between the
construct and its indicators by the number of indicators (Edwards, 2001). The adequate
coefficients R2
aare all above 0.50, indicating the validity of the constructs (Edwards, 2001;
MacKenzie et al., 2011).
DTC is a reflective construct as suggested by the literature (Ferraris et al., 2019; Liu et al.,
2015; Wamba et al., 2017). Its Cronbach’sαvalue is 0.611, which is acceptable and
Cloud-based
firms

demonstrates the reliability of the construct (Karatzas et al., 2016; Kline, 2000).
The composite reliability is 0.793 and the average variance extracted (AVE) is 0.562.
Both are above the cut-off values (0.70 and 0.50, respectively), and thus ensure convergent
validity. In addition, the square root of the AVE value is greater than the correlations with
other constructs, which provides evidence for discriminant validity.
5.4 Results
The study adopted SUR to test the hypotheses. SUR is an approach that can simultaneously
accomplish regression of several system equations. SUR allows adding observed variables
and latent variables to one estimation together, without worrying about misspecification
(Autry et al., 2010; Greene, 2012). SUR can effectively deal with multiple mediation
variables in a research model at one time, even if the variables are not normally distributed
(Preacher et al., 2007). Given these advantages, SUR was selected as the proper regression
technique for testing this study’s hypotheses.
Table VII presents the model estimation results. The SUR results were generated by
Stata 13.1. Equations (1) and (2) estimate the direct relationships between DR and the
mediators, DPC and DTC, respectively. Equation (3) tests the antecedents of the growth of
Construct Item Significance VIF R2
a
Data resource (DR) DR1 po0.01 1.155 0.511
DR2 po0.01 1.098
DR3 po0.001 1.142
Data processing capability (DPC) DPC1 po0.001 1.184 0.544
DPC2 po0.001 1.149
DPC3 po0.001 1.227
Firm growth (FG) FG1 po0.001 1.226 0.540
FG2 po0.05 1.284
FG3 po0.001 1.106
Table VI.
Formative
construct validation
Equation (1) Equation (2) Equation (3)
DV ¼DPC DV ¼DTC DV ¼FG
Direct effect
DR 0.570*** (0.063) 0.380*** (0.068) 0.194** (0.060)
Mediators
DPC 0.208** (0.064)
DTC 0.179** (0.059)
Controls
Firm age 0.002 (0.047) −0.006 (0.052) 0.0363 (0.038)
Firm size 0.101 (0.078) 0.087 (0.085) 0.0139 (0.062)
Industry
ICT 0.285 (0.244) −0.413 (0.267) 0.0615 (0.198)
Manufacturing −0.008 (0.255) −0.514 (0.278) −0.087 (0.205)
Others 0.217 (0.230) −0.340 (0.251) 0.0357 (0.186)
Constant 1.760*** (0.461) 3.747*** (0.503) 1.637*** (0.418)
Observations 190 190 190
R
2
0.323 0.146 0.344
Notes: Standard errors in parentheses. **po0.01; ***po0.001
Table VII.
Seemingly unrelated
regression results
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cloud-based firms and confirms that DR, DPC and DTC have significant positive direct
effects on FG simultaneously.
To test for the existence of mediating effects, bootstrapping method was employed.
Bootstrapping is preferred in detecting indirect effects (Malhotra et al., 2014) and widely
adopted in OM research (e.g. Obayi et al., 2017; Shou et al., 2018; Wang et al., 2016). Given the
sufficiently large sample size (nW100), bias-corrected bootstrapping was utilised
(Rungtusanatham et al., 2014). Table VIII shows the 5,000 resampling bootstrapping
results. The total indirect effect is 0.187 with the 95% confidence interval ranging from
0.101 to 0.293. The exclusion of zero in the 95% confidence interval indicates the existence of
significant indirect effect of DR on FG.
As shown in Table VIII, the indirect effects through DPC and DTC are 0.119 and 0.068,
respectively. The 95% confidence intervals for both DPC and DTC do not include zero,
which indicates the statistically significant indirect effects through both capabilities. Thus,
significant partial indirect effects of two mediators and their combined indirect effect were
confirmed (Preacher and Hayes, 2008). The partial mediation effect of DPC accounts for
about 31 per cent of the total effect and that of DTC for 18 per cent. As a dual mediation
model, the combined mediation effect is also confirmed to be significant, with a proportion of
49 per cent for the total effect between DR and FG.
To alleviate the concerns that international cloud platforms may induce heterogeneity to
the sample, we re-conducted the above analysis using a subsample excluding firms who
were clients of Microsoft Azure and Amazon’s AWS. The subsample has a size of 170 and
all findings remain the same.
5.5 Supplementary analysis
Based on previous studies (Nason and Wiklund, 2018), three types of FG are considered in
the present study: growth of sales, end users and data assets. Cloud-based firms,
particularly startups, may have different operations strategies and prioritise different types
of growth in each lifecycle stage (Carnes et al., 2017; Muzellec et al., 2015). The growth of end
users and data assets may be imperative in early stages, while sales are more important for
sustainable growth in later stages. Therefore, SUR was run for each type of growth
separately. The SUR results reveal that the three independent variables (i.e. DR, DTC and
DMC) are significant for all three types of growth.
To further investigate the distinct roles of the two capabilities in different types of FG, a
supplementary analysis was conducted. The bootstrapping method was adopted to test
mediating effects in three models. The bootstrapping results are presented in Table IX.
It is observed that the total mediating effects are significant for all three types of growth,
as shown in Table IX. The exclusion of zero in the corresponding 95% confidence intervals
indicates the significant total mediating effects. A close observation of individual paths
reveals further insights. The 95% confidence intervals for paths 2, 3 and 6 do not include
zero, which indicates the significant indirect effects through the three paths. In other words,
DPC mediates the relationship between DR and end-user growth, and DTC mediates the
95% confidence interval
Paths Coefficient SE Lower Upper
DR →DPC →FG 0.119* 0.055 0.020 0.235
DR →DTC →FG 0.068* 0.030 0.019 0.142
Total mediating effect 0.187*** 0.049 0.101 0.293
Notes: The ratio of indirect to direct effect is 0.964. The proportion of total effect that is mediated is 0.491.
*po0.05; ***po0.001
Table VIII.
Compound
mediating effects
Cloud-based
firms

relationships between DR and data asset growth/sales growth. However, the 95%
confidence intervals for paths 1, 4 and 5 do not exclude zero, that is, DPC does not mediate
the relationships between DR and data asset growth/sales growth and DTC does not
mediate the relationship between DR and end-user growth.
6. Discussion
6.1 Theoretical implications
This study addresses the gap between the extant operations strategy research and
proliferating cloud-based firms, thereby contributing to the literature in several ways. First,
this study extends the OM literature by identifying two operations capabilities and the
growth priority of cloud-based firms. The present study applied the classic operations
capability dichotomy to the cloud computing context. Two operations capabilities were
identified as DPC and DTC, corresponding to the structural and infrastructure dimensions
in manufacturing operations strategy, respectively. The two operations capabilities were
evidenced by content analysis and triangulated by questionnaire survey. Further, this study
recognised FG as a top priority for cloud-based firms. Although FG is a long-standing topic
in RBV studies (Nason and Wiklund, 2018), it has received scant attention in OM research.
The two-phase multi-method approach confirmed the importance of FG in this particular
context. This study demonstrated that the operations in such cloud-based firms are
considerably different to manufacturing firms. Hence, the authors call for more research to
further investigate the OM and operations strategy of these firms.
Second, this study established a dual mediation model and confirmed the relationships of
DR, data processing and transformational capabilities, and FG in the cloud computing
context. Given the proliferation of cloud-based firms, it is timely to investigate how their
operations strategy influences FG. The results suggest that DR play a fundamental role in
cloud-based firms, having positive impact on both DPC and transformational capability, and
direct impact on FG as well. Moreover, the mediation tests verify the significance of data
operations capabilities in FG as about half of the effect of DR is mediated by the two
capabilities. In other words, cloud-based firms have to gain more DR for stronger operations
capabilities, and then develop operations capabilities to better utilise their data in order to
achieve FG.
Third, the supplementary mediation analysis provides insights into effective operations
strategies for cloud-based firms in each lifecycle stage. It is observed that DPC mediates the
95% confidence interval
Paths Coefficient SE Lower Upper
Sales growth
(1) DR →DPC →sales growth 0.112 0.083 −0.038 0.292
(2) DR →DTC →sales growth 0.087* 0.046 0.007 0.195
Total mediating effect for sales growth 0.198** 0.072 0.057 0.340
End-user growth
(3) DR →DPC →end-user growth 0.152* 0.070 0.022 0.296
(4) DR →DTC →end-user growth 0.050 0.041 −0.028 0.140
Total mediating effect for end-user growth 0.202** 0.067 0.079 0.243
Data asset growth
(5) DR →DPC →data asset growth 0.092 0.056 −0.011 0.216
(6) DR →DTC →data asset growth 0.067* 0.040 0.003 0.164
Total mediating effect for data asset growth 0.159** 0.055 0.058 0.279
Notes: *po0.05; **po0.01
Table IX.
Mediating effects for
three types of growth
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relationship between DR and end-user growth while DTC mediates the relationships
between DR and data asset growth/sales growth. A cloud-based firm may pursue a different
growth strategy in a certain lifecycle stage; hence, the firm should emphasise either DPC or
DTC accordingly. Firms in the embryonic or early stage should prioritise the development of
DPC to provide more value for customers and end users (Muzellec et al., 2015). When a firm
moves to the emerging or growth stage, customer retention and revenue generation become
critical (Muzellec et al., 2015). According to Carnes et al. (2017), firms in a relatively mature
stage tend to formalise their organisational routines and seek stable revenues. Thus, they
may focus on the exploitation of their data assets. DTC helps identify and seize
market opportunities. Therefore, in later lifecycle stages, DTC is essential for monetising
DR and achieving sales growth. The supplementary analysis in this study deepens
our understanding of the distinct roles of the two operations capabilities for different
growth priorities.
Last, a multi-method research design (i.e. content analysis followed by a survey study) was
applied to explore the OM of emerging cloud-based firms, responding to the call to advance
the rigour and relevance of OM research via combined methods (Choi et al., 2016; Singhal et al.,
2008). Given the potential to disclose rich insights (Senot et al., 2016), a multi-method design is
favoured in studying the under-investigated OM fields. Adopting a two-phase multi-method
approach in this study, a data-centred operations strategy framework in the cloud computing
context was established and evidenced. As an initiative study in this emerging area, it could
stimulate and contribute to future research in this context.
6.2 Managerial implications
This study offers managerial insights for industrial practitioners. For cloud-based firms,
this study provides a feasible framework by which they can evaluate and manage their DR
and capabilities. Data should be managed as an asset. Managers are recommended to
examine whether they have reliable internal and external data sources and have formed
their own unique databases. Managers can also evaluate their operations capabilities using
the indicators from this study. It is recommended to have a comprehensive measure before
adopting cloud computing (Kauffman et al., 2018) and hold periodical reviews of the firm’s
DR and operations capabilities.
Firms are also recommended to adopt a suitable operations strategy according to their
growth priority in each lifecycle stage. They should sharpen their DPC if they want to
attract more end users while they should pay more attention to DMC to pursue the growth of
data assets and sales. It should be noted that DMC is not just about technology
(Tabrizi et al., 2019). In the long term, DMC is essential to create and capture value from the
data asset. Moreover, firms may not necessarily invest in both operations capabilities at the
same time if they have resource constraints, which, however, is often a common situation.
In other words, firms may prioritise their investment in either DPC or DMC depending on
their growth priority.
Cloud computing service providers are recommended to facilitate the capability
building of their clients. They should have a clear understanding of the technology
decoupling point between cloud platforms and clients to maximise computing
performance while reducing the clients’cost and learning efforts (Benlian et al., 2018;
Caldarelli et al., 2017). Moreover, since cloud-based firms may prioritise their operations
capabilities in different lifecycle stages, cloud platforms could provide specific services to
help build their clients’capabilities accordingly.
7. Conclusion
A two-phase multi-method approach was adopted in the present study in an attempt to
understand the current status of cloud-based firms from an operations strategy perspective.
Cloud-based
firms

Content analysis of secondary data was conducted and then proposed hypotheses were
tested using survey data. The SUR and bootstrapping results indicate that DR and
capabilities contribute to the growth of cloud-based firms. Moreover, DPC and DTC have
different mediating effects for different types of FG.
Notwithstanding the contributions to the literature, this study has limitations. First,
cross-sectional data were used in the second phase of this study to test the proposed
hypotheses. A firm with faster growth tends to have more opportunities to accumulate DR,
which, in turn, contribute to FG. A longitudinal study could be conducted in the future to
reveal the evolutional pattern of these relationships. Second, the effects of different types of
products or services provided by the cloud-based firms were not investigated in this study.
It is noted that some firms offered both online and offline products. Cloud computing is
extremely efficient in dealing with online products or services given its high scalability.
However, it is not easy to increase the scalability of offline products, and, hence, the
effectiveness of cloud computing may be weakened. Another interesting point is the
configuration of DR and capabilities in cloud-based firms with offline products or the digital
transformation of traditional manufacturers (Shamim et al., 2019), which is also a key sector
of Industry 4.0.
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Corresponding author
Lujie Chen can be contacted at: lujie.chen@xjtlu.edu.cn
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