J. N. Cleveland

Organizational Behavior and Human Resource Management · 2001–2015

A researcher in the Science corpus: 6 papers; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom J. N. Cleveland trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Tammy D. Allen
  2. Michael K. Lindell
  3. R. Eisenberger
  4. Eileen White Jahn
  5. J. Colquitt
  6. S. Seibert
  7. C. Thompson
  8. S. Clark
  9. David P. MacKinnon

Who trusts J. N. Cleveland overall

  1. Ž. Stankevičiūtė
  2. Asta Savaneviciene

Papers from J. N. Cleveland's perspective

Ordered by how strongly the researchers J. N. Cleveland trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Perceived organizational support: a review of the literature. (Journal of Applied Psychology)
  2. A comparison of methods to test mediation and other intervening variable effects. (Psychological methods)
  3. On the dimensionality of organizational justice: a construct validation of a measure. (Journal of Applied Psychology)
  4. Perceived supervisor support: contributions to perceived organizational support and employee retention. (Journal of Applied Psychology)
  5. Family-Supportive Work Environments: The Role of Organizational Perceptions
  6. Accounting for common method variance in cross-sectional research designs. (Journal of Applied Psychology)
  7. When supervisors feel supported: relationships with subordinates' perceived supervisor support, perceived organizational support, and performance. (Journal of Applied Psychology)
  8. Work-supportive family, family-supportive supervision, use of organizational benefits, and problem-focused coping: implications for work-family conflict and employee well-being. (Journal of Occupational Health Psychology)
  9. Work-life benefits and organizational attachment: Self-interest utility and signaling theory models
  10. Do “high commitment” human resource practices affect employee commitment? A cross-level analysis using hierarchical linear modeling

Their papers