How to measure company productivity using value-added: A focus on Pohang Steel (POSCO)

Lieberman, Marvin B.; Kang, Jina

doi:10.1007/s10490-007-9081-0

Cited by 43 publications

(20 citation statements)

References 26 publications

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“…We measured productivity aspiration discrepancy as the change in multi‐factor productivity between t‐2 and t‐1 (Lieberman et al, ). Multi‐factor productivity is a weighted average of labour and capital productivity, where the former corresponds to value‐added per worker‐hour and the latter captures value‐added per unit of capital stock (Lieberman and Kang, ). Specifically, we used the following formula to compute changes in multi‐factor productivity:

Δ M F P_{italicit} = [ln V A_{i, t - 1} - ln V A_{i, t - 2}] - (1 - s_{i, t - 1}) [ln K_{i, t - 1} - ln K_{i, t - 2}] - s_{i, t - 1} [ln L_{i, t - 1} - ln L_{i, t - 2}]

…”

Section: Methodsmentioning

confidence: 99%

Causality Rules: Performance Feedback on Hierarchically Related Goals and Capital Investment Variability

Mazzelli

Nason²,

Massis

et al. 2019

J Management Studies

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We extend March and Simon’s (1958) analysis of strategic decisions by distinguishing between two rules for allocating attention – priority versus causality. We develop theory concerning causality rules which have been largely overlooked in prior literature. Specifically, we examine how performance feedback on the intermediate productivity goal and the higher‐order profitability goal independently and jointly influence the variability of firm capital investments. Panel analysis of 2,477 Spanish manufacturing firms reveals that these goals jointly affect the variability of capital investments through both priority and causality attention rules. Our study provides new insights on how firms handle multiple goals, deconstruct performance feedback, and cope with the attentional constraints of bounded rationality.

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Δ M F P_{italicit} = [ln V A_{i, t - 1} - ln V A_{i, t - 2}] - (1 - s_{i, t - 1}) [ln K_{i, t - 1} - ln K_{i, t - 2}] - s_{i, t - 1} [ln L_{i, t - 1} - ln L_{i, t - 2}]

…”

Section: Methodsmentioning

confidence: 99%

Causality Rules: Performance Feedback on Hierarchically Related Goals and Capital Investment Variability

Mazzelli

Nason²,

Massis

et al. 2019

J Management Studies

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“…Labour productivity, which is the most common indicator of productivity measurement, is the amount of output corresponding to the labourintensive acquisition or defined as the value added per hour worked [9]. Work productivity is determined by human capital, technological change and economies of scale [15].…”

Section: Nature Of Productivitymentioning

confidence: 99%

“…Another difference that is particularly significant at the industry or enterprise level is the productivity measures that cover some of the gross output volumes for one or more raw materials and those who use the concept of value added to capture the product movement. [9,12] The OECD has developed several measurement methods for measuring various types of productivity see Fig. 2.…”

Section: Measurement Of Productivitymentioning

confidence: 99%

“…Management is a function of the system that is focused on preserving the main parameters (the loss of which parameters lead to a system collapse when the environment changes), or the management is oriented to the execution of a specific program that provides stable functioning, homeostasis, to achieve certain goals [9,592]. At the macro-economic level, the management entity is the government (or its specialised management substructure).…”

Section: Economics As a Systemmentioning

confidence: 99%

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Productivity, as an Indicator of the Efficiency of a Country’s Economic System: a Latvian Case

Vaskis¹,

Fortiņš²

2018

HSSL

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The aim of the article is to show the range of issues to consider when developing a country's economic policy for increasing productivity. The relative level of national development over a period of time, on the one hand, is the goal of prosperity for any country and its inhabitants. On the other hand, in an open society, in the larger economic space (EU) of the free movement of people (including goods, capital, etc.), this is a condition for sustainable development. The relative level of socio-economic development of the country, without going into detailed relationships, is characterised by GDP per capita. From a production point of view, the labour force in the productive sectors, whose efficiency is reflected in labour productivity, generates the gross mass of GDP. When deciding on the formulation and implementation of national economic policies, governments face a very significant problem: the assessment of productivity and its interaction with other factors. This is especially important for small countries, such as Latvia. This is not possible if the economic category itself -productivity is not defined with sufficient precision. Productivity is a multi-dimensional concept, which has become one of the basic principles upon which modern economic thinking is based. The concept of productivity includes not only economic factors, but also noneconomic factors such as education, science, political stability and value systems. By creating conditions for productivity increase, a dynamic and systematic approach to creating wealth and long-term economic growth is necessary.

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“…Large steel companies have downsized their operations and small steel companies have merged into larger ones [1]. Newly developed technologies has been constantly introduced and implemented, and technical innovation in the global steel industry is progressing rapidly [2]. In an aggressively competitive environment, the global steel market had been dominantly influenced by large steelmakers in the US, Japan and Europe.…”

Section: Introductionmentioning

confidence: 99%

Efficiency Analysis of Global Steel Companies

Lee

2016

AJMAHS

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This study analyzes efficiency of global steel companies for the period 1997-2008. I employ Data Envelopment Analysis (DEA) to estimate the relative efficiency of global steel companies in using their labor, materials and capital resources to generate sales. Based on a panel dataset for global steel companies for the period 1997-2008, I find that the means (medians) of aggregate, technical, and scale efficiency scores were 0.4954 (0.4619), 0.5803 (0.5420), and 0.8656 (0.9385) respectively. Average aggregate efficiency increased from 1998 until 2002 before trending down. Average technical efficiency increased from 1998 until 2003 before trending down. Therefore, it is apparent that the aggregate efficiency and technical efficiency move together. In contrast, average scale efficiency decreased from 1997 until 2008, which indicated a steadily downward trend from the first year in the sample period. Furthermore, average scale efficiency was greater than average technical efficiency for each year throughout the entire sample period, suggesting that the technical factor was a more important source of inefficiency than the scale factor in each year during the entire sample period. That is, there existed a higher level of technical inefficiency compared with scale inefficiency, indicating that there was more room for improvement in technical efficiency.

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How to measure company productivity using value-added: A focus on Pohang Steel (POSCO)

Abstract: Productivity, Performance assessment, Steel industry, POSCO, Value-added,

Cited by 43 publications

References 26 publications

Causality Rules: Performance Feedback on Hierarchically Related Goals and Capital Investment Variability

Causality Rules: Performance Feedback on Hierarchically Related Goals and Capital Investment Variability

Productivity, as an Indicator of the Efficiency of a Country’s Economic System: a Latvian Case

Efficiency Analysis of Global Steel Companies

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