Browsing by Author "Irani, Zahir"

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    A consumer behavioural approach to food waste
    (Emerald, 2018-06-30) Aktas, Emel; Sahin, Hafize; Topaloğlu, Zeynep; Oledinma, Akunna; Samsul Huda, Abul Kalam; Irani, Zahir; Sharif, Amir M.; van't Wout, Tamara; Kamrava, Mehran
    Purpose Food waste occurs in every stage of the supply chain, but the value-added lost to waste is the highest when consumers waste food. The purpose of this paper is to understand the food waste behaviour of consumers to support policies for minimising food waste. Design/methodology/approach Using the theory of planned behaviour (TPB) as a theoretical lens, the authors design a questionnaire that incorporates contextual factors to explain food waste behaviour. The authors test two models: base (four constructs of TPB) and extended (four constructs of TPB plus six contextual factors). The authors build partial least squares structural equation models to test the hypotheses. Findings The data confirm significant relationships between food waste and contextual factors such as motives, financial attitudes, planning routines, food surplus, social relationships and Ramadan. Research limitations/implications The data comes from an agriculturally resource-constrained country: Qatar. Practical implications Food waste originating from various causes means more food should flow through the supply chains to reach consumers’ homes. Contextual factors identified in this work increase the explanatory power of the base model by 75 per cent. Social implications Changing eating habits during certain periods of the year and food surplus have a strong impact on food waste behaviour. Originality/value A country is considered to be food secure if it can provide its citizens with stable access to sufficient, safe and nutritious food. The findings and conclusions inform and impact upon the development of food waste and food security policies.
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    A decision support model for identification and prioritization of key performance indicators in the logistics industry
    (Elsevier, 2016-09-03) Kucukaltan, Berk; Irani, Zahir; Aktas, Emel
    Performance measurement of logistics companies is based upon various performance indicators. Yet, in the logistics industry, there are several vaguenesses, such as deciding on key indicators and determining interrelationships between performance indicators. In order to resolve these vaguenesses, this paper first presents the stakeholder-informed Balanced Scorecard (BSC) model, by incorporating financial (e.g. cost) and non-financial (e.g. social media) performance indicators, with a comprehensive approach as a response to the major shortcomings of the generic BSC regarding the negligence of different stakeholders. Subsequently, since the indicators are not independent of each other, a robust multi-criteria decision making technique, the Analytic Network Process (ANP) method is implemented to analyze the interrelationships. The integration of these two techniques provides a novel way to evaluate logistics performance indicators from logisticians' perspective. This is a matter that has not been addressed in the logistics industry to date, and as such remains a gap that needs to be investigated. Therefore, the proposed model identifies key performance indicators as well as various stakeholders in the logistics industry, and analyzes the interrelationships among the indicators by using the ANP. Consequently, the results show that educated employee (15.61%) is the most important indicator for the competitiveness of logistics companies.
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    Managing food security through food waste and loss: small data to big data
    (Elsevier, 2017-11-03) Irani, Zahir; Sharif, Amir M.; Lee, Habin; Aktas, Emel; Topaloğlu, Zeynep; van't Wout, Tamara; Huda, Samsul
    This paper provides a management perspective of organisational factors that contributes to the reduction of food waste through the application of design science principles to explore causal relationships between food distribution (organisational) and consumption (societal) factors. Qualitative data were collected with an organisational perspective from commercial food consumers along with large-scale food importers, distributors, and retailers. Cause-effect models are built and “what-if” simulations are conducted through the development and application of a Fuzzy Cognitive Map (FCM) approaches to elucidate dynamic interrelationships. The simulation models developed provide a practical insight into existing and emergent food losses scenarios, suggesting the need for big data sets to allow for generalizable findings to be extrapolated from a more detailed quantitative exercise. This research offers itself as evidence to support policy makers in the development of policies that facilitate interventions to reduce food losses. It also contributes to the literature through sustaining, impacting and potentially improving levels of food security, underpinned by empirically constructed policy models that identify potential behavioural changes. It is the extension of these simulation models set against a backdrop of a proposed big data framework for food security, where this study sets avenues for future research for others to design and construct big data research in food supply chains. This research has therefore sought to provide policymakers with a means to evaluate new and existing policies, whilst also offering a practical basis through which food chains can be made more resilient through the consideration of management practices and policy decisions.