战略性技术应用中的风险规避

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-03-12 DOI:10.1108/jm2-10-2023-0221
J. Pedro Mendes, Miguel Marques, Carlos Guedes Soares
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引用次数: 0

摘要

目的组织技术可根据其发挥的作用分为商品技术和战略技术。商品技术为普通业务提供支持,而战略技术则是为了应对通常由战略远见确定的对竞争力的威胁。这些技术在战略执行中发挥有效作用之前,必须经历一个采用过程。采用过程包括从采购(从内部开发到交钥匙收购)到运营整合等已知活动。本文旨在揭示战略执行过程中出现的战略技术采用风险。设计/方法/途径一个逐步形成的因果循环图模型,在一般文献的支持下,引入了三类一般的技术采用风险:需求不匹配、供应商依赖性和未管理的生命周期。研究局限/意义尽管该方法所涉及的文献很少,但在企业资源规划(ERP)有据可查的历史中,仍有揭示采用风险的实例。实践意义尽管ERP是作为一种通用战略技术提出的,但海运集装箱码头的独特业务特征对其采用构成了严峻挑战,这为讨论提供了额外支持,并强化了结论。首先,它强调的是政策决策,而不是外部事件。其次,它将风险视为系统性风险,而不是独立发生的风险。
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Risk avoidance in strategic technology adoption

Purpose

Organizational technologies can be classified according to the roles they play as either commodity or strategic. Commodity technologies support common operations, while strategic technologies address perceived threats to competitiveness, often identified by strategic foresight. These must go through an adoption process before playing an effective role in strategy execution. The adoption process includes known activities, ranging from sourcing (itself from in-house development to turn-key acquisition) to operational integration. This paper aims to reveal strategic technology adoption risks that arise during strategy execution.

Design/methodology/approach

A gradually developed causal loop diagram model, supported by general literature, introduces three general classes of technology adoption risks: mismatched requirements, supplier dependence and unmanaged life cycles.

Findings

Rather than managed, these risks are incurred or avoided depending on decisions made during the adoption process.

Research limitations/implications

Despite the scarce literature coverage for the approach, examples revealing the presence of adoption risks are nevertheless available in the well-documented history of enterprise resource planning (ERP).

Practical implications

Although ERP is presented as a general-purpose strategic technology, the unique business features of maritime container terminals pose serious challenges to its adoption, which provides additional support to the discussion and reinforces the conclusions.

Originality/value

The approach to identifying risks in strategic technology adoption departs from the current risk paradigm in two significant ways. First, it emphasizes policy decision-making rather than external events. Second, it views risks as systemic rather than occurring independently.

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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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