【免费】理论化数字业务创新：生态系统中的平台和功能-研究论文资源-CSDN文库

需积分: 0 17 浏览量 2021-05-20 07:56:18 上传评论收藏 471KB PDF 举报

资源详情

资源评论

Theorizing Digital Business Innovation

Theorizing Digital Business Innovation: Platforms and Capabilities in Ecosystems

RESEARCH NOTE

Abstract

This paper focuses on how information and technology drive digital business innovation in ecosystems

where multiple co-contributors work together to innovate business models. Specifically, we develop a

framework based on two novel concepts — (1) digital business innovation (DBI) platforms; and (2)

digital business innovation capability—at the ecosystem level of analysis. First, we describe DBI

platform through three properties: scale, scope and speed. Second, we describe DBI ecosystem capability

using three dimensions: operational, dynamic and improvisational capabilities. The DBI platform and

DBI capability together give rise to delivered business value, created at the level of the ecosystem and

shared by the various co-contributors. We generate a set of theoretical propositions by bringing forth the

role of information and technology as twin drivers of value creation and value capture in ecosystems.

While extant research on IT and innovation has focused on the individual, organizational, and rarely at the

interorganizational levels of analysis, we argue for the importance of innovation ecosystems, which are

complex, interdependent, and dynamic. We support our conceptual framework and propositions with a

case example of digital business innovations underway in the automotive sector. We conclude with a set

of critical issues for further development of theory on DBI platforms and DBI ecosystem capabilities that

are disrupting and transforming firms, industries, and markets. Our belief is that IS research could make

major contributions to the broader innovation literature by explicating how information and technology—

both separately and in tandem—give rise to business value co-created and captured in ecosystems.

Keywords: Digital business innovation, ecosystems, capability, platforms, innovation, value-co-creation.

Venkatraman, V., (venkat@bu.edu

), Department of Management Information Systems Boston University; El Sawy,

O. (elsawy@marshall.usc.edu), Marshall School of Business, University of Southern California, Pavlou, P.

(pavlou@temple.edu), Fox School of Business, Temple University, & Bharadwaj, A. (ab@bus.emory.edu),

Goizueta Business School, Emory University.

Theorizing Digital Business Innovation

1. Introduction

Digital innovation as a field of inquiry can trace its roots to the role and impact of information

technology on business processes—termed process innovation (Davenport 1993)—and recently on product

innovations where digitization is integrated with physical products, such as books, thermostats, watches, and

automobiles (Porter and Heppelmann 2014). The impressive increase in the power of computerization and

connectivity requires rethinking product innovations, since enhanced digitization makes physical products

and services programmable, addressable, sensible, communicable, memorable, traceable, and associable

(Yoo 2010). Indeed, the emergence of layered modular architecture (Baldwin and Clark 2000) in digital

products and services impacts the organizing logic of innovation (Yoo et al. 2010) with distributed,

collaborative and combinatorial digital platforms (Yoo et al. 2012). Within this research stream, we examine

how information and technology drive digital business innovation in ecosystems where many co-

contributors simultaneously compete and collaborate to create new business models.

The call for this Special Issue (Nambisan et al. 2014) underscored that innovation processes in a digital

world have become more collaborative, involving global networks of stakeholders and a necessary

recognition of the duality of information and technology as both operand and operant drivers of innovation.

While there is a general understanding of digital innovations through popular writings on digital products

(e.g., smartphones or e-books) and processes (e.g., recommendation systems), there has been a glaring lack

of conceptual frameworks on digital business innovations that give rise to new business models. We focus

on the shift in the locus of innovation from single firms to business ecosystems, where firms interconnect

their business processes and interlink their product modules to architect new digital business models. Thus,

we propose ecosystem as the level of analysis for digital business innovations, extending prior research on

innovation that mostly focused on the organizational—and rarely on interorganizational—level of analysis.

We recognize the early stages of conceptualization and theory building in digital business innovations

and offer some foundational theoretical building blocks to help move towards a more formally articulated

and testable theory. We conceive digital business innovations not as intraorganizational process, but as

multiorganizational platforms driven by interoperable technologies, intelligent data, and inputs from

multiple firms in the ecosystem. Subsequently, we argue that value realization by the various co-contributing

Theorizing Digital Business Innovation

firms within ecosystems is based on a set of capabilities, which are also conceptualized at the ecosystem

level. We discuss the inherent dynamics in digital business model innovations through a learning loop that

iterates to recognize and respond to system-level changes. We develop a set of propositions on how

information and technology as twin drivers of innovation affect the ecosystem’s platforms and capabilities.

We use examples to support our propositions and illustrate the internal consistency of our propositions using

examples of digital business innovations in the automotive sector over the last decade (Online Appendix A).

Finally, we conclude with outlines of a research agenda and a path forward for IS research and practice.

2. Digital Business Innovation Platform

We define digital business innovation platforms as “actions of a network of companies with

complementary competencies to co-innovate new business models that are intrinsically based on

information and technology functionality.”

First, we conceptualize innovation as platform beyond the conventional framing of innovation as

process. Innovation platforms allow for business model innovations driven and supported by information

technology. We follow Amit and Zott (2001) in defining a business model as “a bundle of specific

activities that are conducted to satisfy the perceived needs of the market, including specification of the

parties that conduct these activities and how these activities are linked to each other.” Business model

innovations through information and technology go beyond prior work on the traditional role of IT in the

design of innovative products (e.g., software; Internet of Things) or business process innovations

(Davenport 1993) and service delivery (Brynjolfsson and Saunders 2009).

Second, digital innovations are not created by autonomous firms but by a network of firms in a

business ecosystem pooling their complementary skills. Accordingly, Android or iOS are not digital

innovations from Google and Apple, respectively. They are business ecosystems involving

complementary hardware, applications, and services orchestrated by Google and Apple and involving

several firms that compete and cooperate in dynamic networks (Iansiti and Levien 2004). Empirically,

small, independent software vendors often improve their business performance by participating in

enterprise software ecosystems (Ceccagnoli, Forman, Huang, and Wu 2012).

Theorizing Digital Business Innovation

Third, we bring attention to both information and technology in the design, development, and

deployment of business model innovations. While the literature on innovation may have mostly ‘black-

boxed’ technology features and functionality, we formally focus on the increasing role of data and

information (big data and data-driven insights) as well as the technology (shaping digital product

architecture and firm-customer interactions) underlying innovative business models.

In sum, our definition of digital business innovation platform at the ecosystem level of analysis is

consistent with Powell, Koput, and Smith-Doerr (1996), who argued that “when the knowledge base of an

industry is both complex and expanding and the sources of expertise are widely distributed, the locus of

innovation will be found in networks of learning, rather than in individual firms” (emphasis added; p.116).

2.1 Properties of Digital Business Innovation Platforms

Our conceptualization of digital business platforms is consistent with the general assertion that

platforms succeed due to combination of direct and indirect network effects. A DBI platform has high

direct network effects when it attracts high number of customers for its innovation (customer network

effects). At the same time, DBI platforms become attractive to customers if they have a high degree of

complements that support the innovation (complementor network effects). We now know that several

platforms have succeeded due to the interplay between these two types of effects in settings such as

games (Venkatraman and Lee, 2004) and software platforms (Evans, Hagiu, and Schmalensee 2006).

Our conceptualization of DBI platforms extends insights from studies on platforms at the level of

product innovations such as Microsoft Windows, Adobe PDF, Xbox, PS/2 and others involving two-

sided markets (Parker and Van Alstyne 2005; Rochet and Tirole 2006) to broader innovation platforms at

the level of ecosystems that include a set of actors who contribute key capabilities for the creation and

capture of value from new innovations. We conceptualize digital business innovation platforms through

three important properties—(1) scale, (2) scope and (3) speed—and how they give rise to potential value.

2.1.1 Scale of Digital Business Innovation Platform

Our first assertion is that DBI platform has high degree of scale when it has both high potential

adopters (customer network effects) and a large number of potential co-creators (complementary network

effects). This assertion is based on theoretical proofs, case based insights and empirical studies on

Theorizing Digital Business Innovation

platforms involving settings as diverse as Amazon Kindle digital publishing ecosystem, mobile operating

systems (Android and iOS), videogame consoles and games, Uber in transportation, media and

entertainment with YouTube, Netflix and HBO, Apple Pay in mobile payments and others.

Innovation scale moves by firms involve choices pertaining to technology architecture and

information-based insights. Modular architecture is a key driver of innovation scale. Amazon Kindle

explored both hardware and software as options for its customer base to access digital content, which

increased the customer adoption scale and enhanced its attractiveness to digital content providers. Rapid

acceptance of Netflix has been due to their focus on making every screen (mobile, tablet, PC, monitors)

capable of receiving its content through multiple different set-top boxes and their variants. In videogames,

Microsoft introduced XNA middleware in 2004 to enable Windows PC game developers to port their

games to Microsoft’s Xbox, thereby increasing the number of available titles for its platform. Apple’s

newly announced Apple Pay, as an innovation, has potential value through the installed base of over 800

million iTunes users, but the platform needs complementor scale in terms of acceptance and adoption of

this functionality by banks, merchants and global credit card networks. Thus, we propose:

P1a: Digital business innovation platforms that leverage customer scale and complementor

scale together create greater potential value from innovations than comparable platforms

that leverage one without the other.

Information exchanged within the platform plays an important role in providing insights for the various

participants within the ecosystem to deliver their part of the value. Proprietary recommendation engines

based on detailed data from search queries (e.g., Amazon or YouTube) or social interactions and influences

(e.g., Netflix linked to Facebook) have contributed significantly to sales and engagement. These innovation

platforms formally apply analytics to detailed data attributes on various key trends to make recommendations

to create stickiness within specific innovation platforms. Such stickiness creates value for both customers

and complementors—thereby further reinforcing the virtuousness of customer and complementor scale.

Beyond recommendation engines, platforms use timeliness of data to enhance value to customers and

complementors. Uber has leveraged detailed information on demand patterns to introduce dynamic

pricing, with additional premium fees shared with the drivers that are integral within the ecosystem.

剩余27页未读，继续阅读

评论收藏

内容反馈

理论化数字业务创新：生态系统中的平台和功能-研究论文

评论0

最新资源

理论化数字业务创新：生态系统中的平台和功能-研究论文

评论0

最新资源

相关推荐

TX-1B型单片机开发板原理图.pdf

天祥电子TX-1C型单片机开发板原理图

郭天祥 TX-1C型经典版单片机开发板原理图(高清完整版)

GQDJL-1型51单片机电路原理图

VB课件（适合大中专高职类教学用）

TX系统单片机开发板介绍

RoboMaster 开发板B型 原理图1

TX-1C型单片机开发板原理图

TX-1C型单片机实验板原理图

郭天祥10天学会51单片机_TX-1C型单片机开发板原理图.zip

51单片机开发板原理图TX-1C经典版、增强版、综合版

TX-1C天祥电子开发板原理图

tx-1c51单片机开发板原理图

天祥电子51单片机开发板（TX-1C）proteus原理图

单片机开发板原理图TX-1C.pdf

hot51增强型单片机开发板原理图

单片机开发板---51单片机开发板原理图和PCB.pdf

NXP 飞思卡尔 FRDM-KE02Z40M开发板原理图.pdf

HC6800-ES-V2.0开发板原理图.pdf

TX-1C型单片机开发板介绍

单片机的TX-1C扩展版原理图

TX-3C开发板的原理图

TX-5A PIC开发板原理图

RoboMaster 开发板B型原理图1