Identification, establishment of connection, and clustering of social risks involved in the agri-food supply chains: a cross-country comparative study

Zhao, Guoqing; Liu, Shaofeng; Lopez, Carmen; Wang, Yi; Lu, Haiyan; Zhang, Jinhua

doi:10.1007/s10479-024-06040-2

Identification, establishment of connection, and clustering of social risks involved in the agri-food supply chains: a cross-country comparative study

Original Research
Published: 09 May 2024

(2024)
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Guoqing Zhao ORCID: orcid.org/0000-0003-4553-2417¹,
Shaofeng Liu²,
Carmen Lopez³,
Yi Wang⁴,
Haiyan Lu⁵ &
…
Jinhua Zhang²

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Abstract

Supply chain risk management (SCRM) literature is heterogeneous. While much attention has been given to the economic and environmental dimensions, the social dimension has so far received less focus. Thus, this study analyzes the social risks involved in the agri-food supply chains (AFSCs) of Argentina and China by employing an integrated approach. Semi-structured interviews were used to collect data, followed by using a combination of three complementary data analysis methods: thematic analysis to identify social risks, total interpretive structural modeling (TISM) to build interrelationships among the identified social risks, and fuzzy MICMAC (cross-impact matrix multiplication applied to classification analysis) to cluster social risks into four categories. Next, we conducted a comparative analysis between the two countries. Theoretical contributions are mainly threefold. First, we identified various social risks involved in the AFSCs of Argentina and China, including those just touched on by scholars, such as cultural issues, government’s weak monitoring system, the power differential between managers and subordinates, inappropriate disposal of agrichemical containers, and the lack of basic literacy skills. Second, we believe that our study is the first to establish connections among the identified AFSC social risks, which represents the originality of this work. Third, we discover that cultural issues is the key risk that has the highest capability to elicit other social risks involved in the AFSCs. Our work extends scholarship’s knowledge to understand AFSC social risks from the cultural perspective. This study also generates contributions to policymakers, migrant associations, and the government tax departments of Argentina and China.

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The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

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Authors and Affiliations

School of Management, Swansea University, Swansea, Wales, U.K.
Guoqing Zhao
Plymouth Business School, University of Plymouth, Plymouth, England, U.K.
Shaofeng Liu & Jinhua Zhang
Southampton Business School, University of Southampton, Southampton, England, U.K.
Carmen Lopez
School of Business, University of Bedfordshire, Luton , England, U.K.
Yi Wang
Business School, Newcastle University, Newcastle , England, U.K.
Haiyan Lu

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Guoqing Zhao
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Shaofeng Liu
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Carmen Lopez
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Yi Wang
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Haiyan Lu
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Jinhua Zhang
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Correspondence to Guoqing Zhao.

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Appendices

Appendix 1 Interview guide

I. Interviewee information.

1)
What is your current designation?
2)
Can you give me a brief introduction to your job within the company’s operations? Probe – What type of crop(s) do you grow/process/deliver?
3)
How many years of your working experience have been in agriculture? Probe – What kind of agricultural activities have you done (e.g., pest management, harvesting and marketing).
4)
How many years of your working experience have you been in the same job role in total? Probe – Have you done other jobs related to agriculture or agri-food supply chains?

II. Company information.

1)
Can you give me an overview of the company’s operations? Probe – How do you understand agricultural business and its role in supply chains?
2)
How many employees are working for the company? Probe – Have you employed any temporary workers?
3)
Can you give a brief overview of your company’s upstream and downstream collaborators in the AFSC?

III. Social risks involved or experienced.

1)
How would you describe the sources of social risks that affect your company? Probe – What affects your company, such as loss of reputation and profit?
2)
How would you describe any social risks related to violating human rights? Probe – How do you understand children working with their parents? How do you understand forced and bonded labor? How do you understand local migrant worker rights violations?
3)
How would you describe any social risks related to labor practices and decent work conditions? Probe – How do you understand limited or no access to personal protective equipment? How do you understand over time work? How do you understand local poor-quality water?
4)
How would you describe any social risks related to society? Probe – How do you understand the unavailability of public facilities? How do you understand exposure to unemployment?

IV. Measures adopted or will be adopted to tackle social risks.

1)
How would you describe any measures or strategies that have been adopted by your company to tackle social risks related to violating human rights?
2)
How would you describe any measures or strategies that have been adopted by your company to improve the working conditions of employees?
3)
How would you describe any measures or strategies that have been adopted by the local government to tackle social risks from the whole society’s perspective?
4)
How would you describe any measures or strategies that have been adopted by the focal company of the AFSC to tackle social risks?

Appendix 2 Detailed information of each interviewee involved in this study

Country	Case firm	Role in AFSCs	Ownership	Education level	Working experience	Interviewee
Argentina	A	Input supplier (agrichemical provider)	Private	Junior high school	25 years	Co-founder
	B	Farmers	Private	Master in Agricultural Engineering	30 years	Owner
	C		Private	Junior high school	20 years	Owner
	D		Private	Primary school education	30 years	Owner
	E		Private	Master in Pest Management	10 years	Owner
	F	Research institutes	Public	PhD in Rural Extension	30 years	Professor in Rural Extension
	G		Public	Master in Agricultural Science	22 years	Technical Manager
	H		Public	PhD in Agricultural Sensors	30 years	Dean of the faculty of agriculture
	I	Wholesalers	Public	Master	20 years	Marketing director
	J	Government	Public	Master	15 years	Director of Agri-food Ministry of Buenos Aires province
	K	Government	Public	PhD in Chemistry	10 years	Technical Manager of pesticide residue test
	L	Distributor	Private	Primary education	20 years	Owner
China	A	Farmer	Public	Master in Agricultural Management	10 years	Technical Manager of intelligent farm
	B		Private	Master in Gene Modification	20 years	Owner
	C		Public	Bachelor’s degree in Management	25 years	CEO
	D		Private	Master’s degree in Science	15 years	Owner
	E		Private	Master’s degree in science	10 years	Owner
	F	Research institutes	Public	PhD in Engineering	20 years	Professor of Supply Chain Management
	G		Public	PhD in Management	25 years	Professor of Management
	H		Public	PhD in Engineering	15 years	Professor of Agricultural Sensors
	I		Public	PhD in Management	10 years	Professor of Supply Chain Management
	J	Supermarket	Private	Primary School	25 years	Owner
	K	Wholesaler	Private	Primary School	15 years	Owner
	L	Government	Public	Master in Agricultural Science	30 years	Director

Appendix 3(a) Initial and final reachability matrix of AFSC social risks of Argentina

	E1	E2	E3	E4	E5	E6	E7	E8	E9	E10	E11	E12	E13	E14
E1	1	1	1*	1	1	1*	1	1	1*	1	0	0	0	0
E2	0	1	1	0	1*	1*	0	0	1*	0	0	0	0	0
E3	0	0	1	0	1*	1	0	0	1	0	0	0	0	0
E4	0	0	0	1	0	0	0	0	0	0	0	0	0	0
E5	0	0	0	0	1	0	0	0	0	0	0	0	0	0
E6	0	0	0	0	1	1	0	0	1	0	0	0	0	0
E7	0	0	0	0	0	0	1	0	0	0	0	0	0	0
E8	0	0	0	0	0	0	1	1	0	0	0	0	0	0
E9	0	0	0	0	0	0	0	0	1	0	0	0	0	0
E10	0	0	0	0	0	0	0	0	1	1	0	0	0	0
E11	1	1	1*	1	1	1*	1	1	1	0	1	0	1	0
E12	1	1	1	1	1	1	1*	1	1	1	1	1	1	0
E13	1	1	1*	1	1*	1*	1*	1*	1*	1*	0	0	1	0
E14	1	1	1	1	1	1*	1*	1	1	1*	1*	1	1*	1

Note: * means transitivity

Appendix 3(b) Initial and final reachability matrix of AFSC social risks of China

	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11	F12
F1	1	1*	1	1	1	1*	0	1*	1	0	1	0
F2	0	1	1*	1*	1*	1	0	1	0	0	1*	0
F3	0	0	1	0	1	0	0	0	0	0	0	0
F4	0	0	0	1	0	1	0	0	0	0	0	0
F5	0	0	0	0	1	0	0	0	0	0	0	0
F6	0	0	0	0	0	1	0	0	0	0	0	0
F7	0	1	1	1	1	1	1	1*	1	0	1*	0
F8	0	1*	1	1	1	1	0	1	0	0	1	0
F9	0	0	1	0	1	1	0	0	1	0	0	0
F10	1	1	1	1	1	1*	1	1*	1*	1	1	0
F11	0	1	1	1*	1*	1*	0	1*	0	0	1	0
F12	1	1*	1	1	1*	1*	1	1*	1*	1	1	1

Note: * means transitivity

Appendix 4(a) Partitioning the reachability matrix into different levels – Argentina

Variable	Reachability Set (RS)	Antecedent Set (AS)	RS∩AS	Level
Iteration 1
E1	1,2,3,4,5,6,7,8,9,10	1,11,12,13,14	1
E2	2,3,5,6,9	1,2,11,12,13,14	2
E3	3,5,6,9	1,2,3,11,12,13,14	3
E4	4	1,4,11,12,13,14	4	I
E5	5	1,2,3,5,6,11,12,13,14	5	I
E6	5,6,9	1,2,3,6,11,12,13,14	6
E7	7	1,7,8,11,12,13,14	7	I
E8	7,8	1,8,11,12,13,14	8
E9	9	1,2,3,6,9,10,11,12,13,14	9	I
E10	9,10	1,10,12,13,14	10
E11	1,2,3,4,5,6,7,8,9,11,13	11,12,14	11
E12	1,2,3,4,5,6,7,8,9,11,12,13	12,14	12
E13	1,2,3,4,5,6,7,8,9,10,13	11,12,13,14	13
E14	1,2,3,4,5,6,7,8,9,10,11,12,13,14	14	14
Iteration 2
E1	1,2,3,6,8,10	1,11,12,13,14	1
E2	2,3,6	1,2,11,12,13,14	2
E3	3,6	1,2,3,11,12,13,14	3
E6	6	1,2,3,6,11,12,13,14	6	II
E8	8	1,8,11,12,13,14	8	II
E10	10	1,10,12,13,14	10	II
E11	1,2,3,6,8,11,13	11,12,14	11
E12	1,2,3,6,8,11,12,13	12,14	12
E13	1,2,3,6,8,10,13	11,12,13,14	13
E14	1,2,3,6,8,10,11,12,13,14	14	14
Iteration 3
E1	1,2,3	1,11,12,13,14	1
E2	2,3	1,2,11,12,13,14	2
E3	3	1,2,3,11,12,13,14	3	III
E11	1,2,3,11,13	11,12,14	11
E12	1,2,3,11,12,13	12,14	12
E13	1,2,3,13	11,12,13,14	13
E14	1,2,3,11,12,13,14	14	14
Iteration 4
E1	1,2	1,11,12,13,14	1
E2	2	1,2,11,12,13,14	2	IV
E11	1,2,11,13	11,12,14	11
E12	1,2,11,12,13	12,14	12
E13	1,2,13	11,12,13,14	13
E14	1,2,11,12,13,14	14	14
Iteration 5
E1	1	1,11,12,13,14	1	V
E11	1,11,13	11,12,14	11
E12	1,11,12,13	12,14	12
E13	1,13	11,12,13,14	13
E14	1,11,12,13,14	14	14
Iteration 6
E11	11,13	11,12,14	11
E12	11,12,13	12,14	12
E13	13	11,12,13,14	13	VI
E14	11,12,13,14	14	14
Iteration 7
E11	11	11,12,14	11	VII
E12	11,12	12,14	12
E14	11,12,14	14	14
Iteration 8
E12	12	12,14	12	VIII
E14	12,14	14	14
Iteration 9
E14	14	14	14	IX

Appendix 4(b) Partitioning the reachability matrix into different levels – China

Variable	Reachability Set (RS)	Antecedent Set (AS)	RS∩AS	Level
Iteration 1
F1	1,2,3,4,5,6,8,9,11	1,10,12	1
F2	2,3,4,5,6,8,11	1,2,7,8,10,11,12	2,8,11
F3	3,5	1,2,3,7,8,9,10,11,12	3
F4	4,6	1,2,4,7,8,10,11,12	4
F5	5	1,2,3,5,7,8,9,10,11,12	5	I
F6	6	1,2,4,6,7,8,9,10,11,12	6	I
F7	2,3,4,5,6,7,8,9,11	7,10,12	7
F8	2,3,4,5,6,8,11	1,2,7,8,10,11,12	2,8,11
F9	3,5,6,9	1,7,9,10,12	9
F10	1,2,3,4,5,6,7,8,9,10,11	10,12	10
F11	2,3,4,5,6,8,11	1,2,7,8,10,11,12	2,8,11
F12	1,2,3,4,5,6,7,8,9,10,11,12	12	12
Iteration 2
F1	1,2,3,4,8,9,11	1,10,12	1
F2	2,3,4,8,11	1,2,7,8,10,11,12	2,8,11
F3	3	1,2,3,7,8,9,10,11,12	3	II
F4	4	1,2,4,7,8,10,11,12	4	II
F7	2,3,4,7,8,9,11	7,10,12	7
F8	2,3,4,8,11	1,2,7,8,10,11,12	2,8,11
F9	3,9	1,7,9,10,12	9
F10	1,2,3,4,7,8,9,10,11	10,12	10
F11	2,3,4,8,11	1,2,7,8,10,11,12	2,8,11
F12	1,2,3,4,7,8,9,10,11,12	12	12
Iteration 3
F1	1,2,8,9,11	1,10,12	1
F2	2,8,11	1,2,7,8,10,11,12	2,8,11	III
F7	2,7,8,9,11	7,10,12	7
F8	2,8,11	1,2,7,8,10,11,12	2,8,11	III
F9	9	1,7,9,10,12	9	III
F10	1,2,7,8,9,10,11	10,12	10
F11	2,8,11	1,2,7,8,10,11,12	2,8,11	III
F12	1,2,7,8,9,10,11,12	12	12
Iteration 4
F1	1	1,10,12	1	IV
F7	7	7,10,12	7	IV
F10	1,7,10	10,12	10
F12	1,7,10,12	12	12
Iteration 5
F1	1	1,10,12	1	IV
F7	7	7,10,12	7	IV
F10	1,7,10	10,12	10
F12	1,7,10,12	12	12
Iteration 6
F10	10	10,12	10	V
F12	10,12	12	12
Iteration 7
F12	12	12	12	VI

Appendix 5(a) Binary direct reachability matrix of Argentina

	E1	E2	E3	E4	E5	E6	E7	E8	E9	E10	E11	E12	E13
E1	0	1	0	1	1	0	1	1	0	1	0	0	0
E2	0	0	1	0	0	0	0	0	0	0	0	0	0
E3	0	0	0	0	0	1	0	0	1	0	0	0	0
E4	0	0	0	0	0	0	0	0	0	0	0	0	0
E5	0	0	0	0	0	0	0	0	0	0	0	0	0
E6	0	0	0	0	1	0	0	0	1	0	0	0	0
E7	0	0	0	0	0	0	0	0	0	0	0	0	0
E8	0	0	0	0	0	0	1	0	0	0	0	0	0
E9	0	0	0	0	0	0	0	0	0	0	0	0	0
E10	0	0	0	0	0	0	0	0	1	0	0	0	0
E11	1	1	0	1	1	0	1	1	1	0	0	0	1
E12	1	1	1	1	1	1	0	1	1	1	1	0	1
E13	1	1	0	1	0	0	0	0	0	0	0	0	0
E14	1	1	1	1	1	0	0	1	1	0	0	1	0

Appendix 5(b) Binary direct reachability matrix of China

	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11
F1	0	0	1	1	1	0	0	0	1	0	1
F2	0	0	0	0	0	1	0	1	0	0	0
F3	0	0	0	0	1	0	0	0	0	0	0
F4	0	0	0	0	0	1	0	0	0	0	0
F5	0	0	0	0	0	0	0	0	0	0	0
F6	0	0	0	0	0	0	0	0	0	0	0
F7	0	1	1	1	1	1	0	0	1	0	0
F8	0	0	1	1	1	1	0	0	0	0	1
F9	0	0	1	0	1	1	0	0	0	0	0
F10	1	1	1	1	1	0	1	0	0	0	1
F11	0	1	1	0	0	0	0	0	0	0	0
F12	1	0	1	1	0	0	1	0	0	1	1

Appendix 6(a) Fuzzy direct reachability matrix of Argentina

	E1	E2	E3	E4	E5	E6	E7	E8	E9	E10	E11	E12	E13
E1	0	0.1	0	0.5	0.3	0	0.5	0.3	0	0.5	0	0	0
E2	0	0	0.5	0	0	0	0	0	0	0	0	0	0
E3	0	0	0	0	0	0.3	0	0	0.5	0	0	0	0
E4	0	0	0	0	0	0	0	0	0	0	0	0	0
E5	0	0	0	0	0	0	0	0	0	0	0	0	0
E6	0	0	0	0	0.5	0	0	0	0.5	0	0	0	0
E7	0	0	0	0	0	0	0	0	0	0	0	0	0
E8	0	0	0	0	0	0	0.3	0	0	0	0	0	0
E9	0	0	0	0	0	0	0	0	0	0	0	0	0
E10	0	0	0	0	0	0	0	0	0.9	0	0	0	0
E11	0.1	0.3	0	0.7	0.5	0	0.3	0.5	0.7	0	0	0	0.5
E12	0.5	0.1	0.1	0.7	0.5	0.5	0	0.5	0.5	0.5	0.3	0	0.5
E13	0.3	0.3	0	0.5	0	0	0	0	0	0	0	0	0
E14	0.1	0.7	0.3	0.5	0.3	0	0	0.1	0.1	0	0	0.5	0

Appendix 6(b) Fuzzy direct reachability matrix of China

	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11
F1	0	0	0.3	0.3	0.3	0	0	0	0.3	0	0.1
F2	0	0	0	0	0	0.5	0	0.3	0	0	0
F3	0	0	0	0	0.3	0	0	0	0	0	0
F4	0	0	0	0	0	0.9	0	0	0	0	0
F5	0	0	0	0	0	0	0	0	0	0	0
F6	0	0	0	0	0	0	0	0	0	0	0
F7	0	0.3	0.3	0.3	0.7	0.3	0	0	0.3	0	0
F8	0	0	0.5	0.3	0.3	0.1	0	0	0	0	0.1
F9	0	0	0.3	0	0.5	0.3	0	0	0	0	0
F10	0.7	0.5	0.5	0.3	0.5	0	0.5	0	0	0	0.5
F11	0	0.3	0.3	0	0	0	0	0	0	0	0
F12	0.1	0	0.3	0.3	0	0	0.5	0	0	0.5	0.3

Appendix 7(a) The fuzzy MICMAC stabilized matrix of Argentina

	E1	E2	E3	E4	E5	E6	E7	E8	E9	E10	E11	E12	E13	Driving power
E1	0	0.1	0.1	0.5	0.3	0.1	0.5	0.3	0.5	0.5	0	0	0	2.9
E2	0	0	0.5	0	0.3	0.3	0	0	0.5	0	0	0	0	1.6
E3	0	0	0	0	0.3	0.3	0	0	0.5	0	0	0	0	1.1
E4	0	0	0	0	0	0	0	0	0	0	0	0	0	0
E5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
E6	0	0	0	0	0.5	0	0	0	0.5	0	0	0	0	1
E7	0	0	0	0	0	0	0	0	0	0	0	0	0	0
E8	0	0	0	0	0	0	0.3	0	0	0	0	0	0	0.3
E9	0	0	0	0	0	0	0	0	0	0	0	0	0	0
E10	0	0	0	0	0	0	0	0	0.9	0	0	0	0	0.9
E11	0.3	0.3	0.3	0.7	0.5	0.3	0.3	0.5	0.7	0.3	0	0	0.5	4.7
E12	0.5	0.3	0.3	0.7	0.5	0.5	0.5	0.5	0.5	0.5	0.3	0	0.5	5.6
E13	0.3	0.3	0.3	0.5	0.3	0.3	0.5	0.3	0.3	0.3	0	0	0	3.4
E14	0.5	0.7	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.3	0.5	0.5	6.5
Dependence power	1.6	1.7	2	2.9	3.2	2.3	2.6	2.1	4.9	2.1	0.6	0.5	1.5

Appendix 7(b) The fuzzy MICMAC stabilized matrix of China

	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11	Driving power
F1	0	0.1	0.3	0.3	0.3	0.3	0	0.1	0.3	0	0.1	1.8
F2	0	0	0.3	0.3	0.3	0.5	0	0.3	0	0	0.1	1.8
F3	0	0	0	0	0.3	0	0	0	0	0	0	0.3
F4	0	0	0	0	0	0.9	0	0	0	0	0	0.9
F5	0	0	0	0	0	0	0	0	0	0	0	0
F6	0	0	0	0	0	0	0	0	0	0	0	0
F7	0	0.3	0.3	0.3	0.7	0.3	0	0.3	0.3	0	0.1	2.6
F8	0	0.1	0.5	0.3	0.3	0.3	0	0	0	0	0.1	1.6
F9	0	0	0.3	0	0.5	0.3	0	0	0	0	0	1.1
F10	0.7	0.5	0.5	0.3	0.5	0.5	0.5	0.3	0.3	0	0.5	4.3
F11	0	0.3	0.3	0.3	0.3	0.3	0	0.3	0	0	0	1.8
F12	0.5	0.5	0.5	0.3	0.5	0.5	0.5	0.3	0.3	0.5	0.5	4.9
Dependence power	1.2	1.8	3	2.1	3.7	3.9	1	1.6	1.2	0.5	1.4

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Zhao, G., Liu, S., Lopez, C. et al. Identification, establishment of connection, and clustering of social risks involved in the agri-food supply chains: a cross-country comparative study. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-06040-2

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Received: 02 September 2022
Accepted: 26 April 2024
Published: 09 May 2024
DOI: https://doi.org/10.1007/s10479-024-06040-2

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Identification, establishment of connection, and clustering of social risks involved in the agri-food supply chains: a cross-country comparative study

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Appendices

Appendix 1 Interview guide

Appendix 2 Detailed information of each interviewee involved in this study

Appendix 3(a) Initial and final reachability matrix of AFSC social risks of Argentina

Appendix 3(b) Initial and final reachability matrix of AFSC social risks of China

Appendix 4(a) Partitioning the reachability matrix into different levels – Argentina

Appendix 4(b) Partitioning the reachability matrix into different levels – China

Appendix 5(a) Binary direct reachability matrix of Argentina

Appendix 5(b) Binary direct reachability matrix of China

Appendix 6(a) Fuzzy direct reachability matrix of Argentina

Appendix 6(b) Fuzzy direct reachability matrix of China

Appendix 7(a) The fuzzy MICMAC stabilized matrix of Argentina

Appendix 7(b) The fuzzy MICMAC stabilized matrix of China

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Identification, establishment of connection, and clustering of social risks involved in the agri-food supply chains: a cross-country comparative study

Abstract

Access this article

Similar content being viewed by others

Agroecological principles and elements and their implications for transitioning to sustainable food systems. A review

The Governance of Global Value Chains: Unresolved Human Rights, Environmental and Ethical Dilemmas in the Apple Supply Chain

The sustainability of “local” food: a review for policy-makers

Data availability

References

Author information

Authors and Affiliations

Corresponding author

Ethics declarations

Conflict of interest

Additional information

Publisher’s Note

Appendices

Appendix 1 Interview guide

Appendix 2 Detailed information of each interviewee involved in this study

Appendix 3(a) Initial and final reachability matrix of AFSC social risks of Argentina

Appendix 3(b) Initial and final reachability matrix of AFSC social risks of China

Appendix 4(a) Partitioning the reachability matrix into different levels – Argentina

Appendix 4(b) Partitioning the reachability matrix into different levels – China

Appendix 5(a) Binary direct reachability matrix of Argentina

Appendix 5(b) Binary direct reachability matrix of China

Appendix 6(a) Fuzzy direct reachability matrix of Argentina

Appendix 6(b) Fuzzy direct reachability matrix of China

Appendix 7(a) The fuzzy MICMAC stabilized matrix of Argentina

Appendix 7(b) The fuzzy MICMAC stabilized matrix of China

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